Towards the Internet of Smart Trains: A Review on Industrial IoT-Connected Railways

Abstract

Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.

Full text

sensors
Article
Towards the Internet of Smart Trains:
A Review on Industrial IoT-Connected Railways
Paula Fraga-Lamas *, Tiago M. Fernández-Caramés and Luis Castedo
Department of Computer Engineering, Faculty of Computer Science, Universidade da Coruña,
15071 A Coruña, Spain; tiago.fernandez@udc.es (T.M.F.-C.); luis.castedo@udc.es (L.C.)
*Correspondence: paula.fraga@udc.es; Tel.: +34-981-167-000 (ext. 6051)
Received: 2 May 2017; Accepted: 19 June 2017; Published: 21 June 2017
Abstract:
Nowadays, the railway industry is in a position where it is able to exploit the opportunities
created by the IIoT (Industrial Internet of Things) and enabling communication technologies under
the paradigm of Internet of Trains. This review details the evolution of communication technologies
since the deployment of GSM-R, describing the main alternatives and how railway requirements,
specifications and recommendations have evolved over time. The advantages of the latest generation
of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless
Sensor Networks (WSNs) for the railway environment are also explained together with the
strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on
providing a holistic approach, identifying scenarios and architectures where railways could leverage
better commercial IIoT capabilities. After reviewing the main industrial developments, short and
medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest
research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video
surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train
control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall,
it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art
of different technologies and services that will revolutionize the railway industry and will allow for
confronting today challenges.
Keywords:
IoT; IIoT; internet of trains; railway safety; rail planning and scheduling; predictive
maintenance; WSN; railway enhanced services; freight transportation; cyber security
1. Introduction
The future of the railway industry is expected to rely upon smart transportation systems that
leverage technologies over a large rail network infrastructure to reduce its life-cycle cost. New services,
such as integrated security, asset management, and predictive maintenance, are expected to improve
timely decision-making for issues like safety, scheduling, and system capacity. Smart railways
represent a combination of interconnected technological solutions and components, as well as modern
transportation infrastructure like automatic ticketing systems, digital displays, and smart meters.
Likewise, these systems require seamless high data rate wireless connectivity and integrated software
solutions to optimize the usage of assets, from tracks to trains, to meet the ever-growing demand for
energy-efficient and safer services. The driving factors of the smart railways are expected to enforce the
growth of the industry. These factors include the increasing importance of sustainability, government
regulations, demographics (i.e., growing traffic of passengers and freight, aging population, and rapid
urbanization), macroeconomics (i.e., limited public funding and government deficit, government
initiatives and partnership models), microeconomics (i.e., price sensitivity, demands for an improved
passenger experience, stakeholders interests), the growing importance of smart cities, the incredible
pace of telecommunications and technological change, and the need for mobility.
Sensors 2017,17, 1457; doi:10.3390/s17061457 www.mdpi.com/journal/sensors

Sensors 2017,17, 1457 2 of 44
The global smart railway market is estimated to grow from USD 10.50 bn last year to USD20.58bn
by 2021, at a Compound Annual Growth Rate (CAGR) of 14.4% [
1
]. Moreover, according to the
International Transport Forum of the Organisation for Economic Co-operation and Development
(
OECD
), by 2050, passenger mobility will increase by 200–300% and freight activity by as much as
150–250% with respect to 2010 [
2
]. It is expected that these figures impact on each and every component
of the value chain of the industry, from passenger service to the back-end organization.
In addition, the complexity of high-speed railway networks has been previously studied by different
research initiatives, which were primarily aimed at fostering transportation quality. Among their diverse
strategic goals is the introduction of advanced communication technologies, which allows for providing
improved services and for coping with the rapidly changing needs of the market [3].
Current European railway communications technology was built in the beginning of the 90 s
taking into consideration well-established standards with potential to deliver the railway services at
that time [4].
The inception of smart railways began with the evolution of Global System for Mobile
Communications-Railways (
GSM-R
), which is considered to be the keystone of rail industry
transformation. Rail operators mainly use GSM-R for operational voice and data communications.
Over a period of time, innovation in wireless communications technologies offered reliable
transmission of video and data services for long distances. In the 2000s, the introduction of novel
technological solutions and various digital devices projected new application areas, such as the
provision of information about the rails to passengers, the Communication-Based Trail Control
(CBTC), rail traffic management systems, and Positive Train Control (PTC) solutions. However,
the rail industry underwent a major revolution after 2005 with the appearance of Internet of
Things (
IoT
) and the adoption of smart city projects, which led to the development of solutions like
smart ticketing, passenger infotainment, rail analytics, and dynamic route scheduling and planning.
Industrial IoT-based solutions have eventually reinforced competitive advantages and have also
uncovered new business models that are already impacting the global rail industry.
However, factors such as operational inefficiency, the lack of infrastructure and interoperability,
high initial cost of deployment, and the integration complexities over legacy systems and the network,
may hinder the rail industry growth. Moreover, legacy infrastructure, aging communications systems,
and the slow adoption of automation and protective technology in this scenario pose enormous safety
risks. Related to the issues of safety and connectivity is security. As rail systems rely more and more
on wireless connectivity, they become more vulnerable to outside interference, intrusion and cyber
attacks. The consequences of even a small disruption become particularly severe as trains become more
powerful, carry more passengers, and travel faster. Systems that are mission-critical for safe operation
can be compromised by a simple electronic device or a small piece of malicious code. When passenger
safety and lives are at stake, strong security becomes a fundamental requirement. Nowadays, the main
challenges when enhancing rail transport can be summarized as [5]:
•
Increase efficiency and competitiveness: railways face ferocious competition from other modes
(for example, the road sector provides attractive, cost-effective, reliable, flexible, and convenient
door-to-door transport of freight and passengers across borders). In Europe, the challenge is
further increased by a fragmented rail market, with numerous national systems for rail signaling
and speed control. Thus, interoperability represents a key challenge for the free flow of rail traffic.
•Reduce rail noise and vibration, particularly in urban areas.
•
Reduce greenhouse gas emissions. Although rail transport compares favorably to other transport
means in terms of environmental impact, it can be further improved.
•
Safety and security [
6
]: rail safety in the European Union (EU) is among the highest in the world.
Rail incidents (accidents, terrorism...) are not frequent and cause a relatively low toll of deaths,
but often involve a substantial number of people. In order to maintain and enhance security,
interoperable and harmonized safety standards are required.
•Reduce operation and maintenance costs, augment the capacity of the rail network.

Sensors 2017,17, 1457 3 of 44
As it will be explained in the next section, considering the diversity of scenarios, the network
architecture should include different types of access networks and technologies at different frequency
bands in order to fulfill different operational requirements.
This review introduces a comprehensive analysis of the evolution of the communications in the
European railways since the deployment of GSM-R. It examines the different alternatives proposed
over time and how railway requirements, specifications and recommendations have evolved in recent
years. Unlike recent literature, the main contribution of this work focuses on presenting a holistic
approach to IIoT applied to railways with a thorough study of the most relevant technologies (like the
communications network). Its aim is to envision the potential contribution of enabling technologies
for revolutionizing the industry and confront today challenges.
The rest of this article is organized as follows. Section 2provides a brief introduction of the
main railway scenarios, examining the communications technologies and architectures used nowadays.
Section 3reviews the basic railway-specific requirements and services offered by GSM-R. Section 4
analyzes the factors that influence the deployment of LTE, and what is necessary to comply with the
specific requirements of railway services. The advantages of the newest generation of communications
systems for the railway environment are also explained together with the roadmap to ensure a satisfactory
migration from GSM-R to LTE-R. Section 5describes the rise of industrial IoT and the paradigm of Internet
of Trains. Furthermore, the main industrial developments are described. Section 6reviews the main
short and medium-term IIoT-enabled services for smart railways. Finally, Section 7is devoted to the
conclusions. For the sake of clarity, Figure 1shows an overview of the contents covered by the survey.
Internet of
Smart Trains
Passengers
Freight
Services &
Requirements
Communication
Systems
Industrial
IoT
Information
Services
Predictive
Maintenance
Smart
Infrastructure
Inter-Car
Train-to-
Infrastructure
Data
Infrastructure-to-
Infrastructure
Wireless
Sensor
Networks
Railway-Specific
Services
Asset
Monitoring
Video
Surveillance
Operations
Autonomous
Systems
Safety
Train
Control Cyber-security
Signaling
Voice
Intra-Car
Inside
Railway
Station
Broadband
Communications
Energy
E!ciency
Figure 1.
Overview of the topics related to the Internet of Smart Trains that are covered in this article.
2. Communication Systems in Railway Scenarios
Railway lines can be categorized mainly into one of four classes: urban, urban/inter-city, inter-city
and/or high-speed. It is necessary to analyze lines or networks separately, given that their differences
may have impact on their requirements (Table 1). Furthermore, railway communication systems
can be divided into three main application groups: safety and control, operator, and customer
oriented networks. In this Section, the communications in the most representative railway scenarios
(Figure 2) are described: train-to-infrastructure communications, inter-car communications, intra-car
communications, communications inside the station, infrastructure-to-infrastructure communications,

Sensors 2017,17, 1457 4 of 44
and wireless sensor networks. In the following subsections, future directions of wireless systems in
railways are addressed.
Table 1. Main characteristics of the different line types.
Characteristics Urban Urban/Inter-City Inter-City High-Speed
Maximum speed (kph) s ≤70 70 < s ≤160 160 < s < 250 ≥250
Line length (km) l ≤20 20 < l < 100 100 ≤l < 250 l ≥250
Parallel tracks (units) 1 2 3 4
Rolling stock Single Similar Mixed Very Mixed
Stock types 1 2–4 5–8 9+
Train stations 1–5 6–20 21–50 51+
Operators 1 2 3–5 6+
Passengers (per km of line) n< 100,000 100,000 ≤n< 200,000 200,000 ≤n< 500,000 n≥500,000
Range of services Single Small diversity Multiple variances Extremely varied
Figure 2.
Railway communications scenarios (Renfe AVE train and train station pictures are under
Creative Commons License). Color meaning: pink (train-to-infrastructure communications), blue (inter-car
communications), light-green (intra-car communications), yellow (communications inside the station),
purple (infrastructure-to-infrastructure communications), and dark green (wireless sensor networks).
2.1. Train-to-Infrastructure Connection
This scenario requires two types of links among the Access Point (
AP
) transceivers located in the
train and the fixed network infrastructure. These links must be bidirectional, with high data rates and
latencies lower than 100 ms while traveling at speeds up to 350 km/h or even higher [
3
]. Jointly with

Sensors 2017,17, 1457 5 of 44
an availability of 98–99% mandatory to comply with Reliability, Availability, Maintainability and
Safety (RAMS) requirements.
Several works exist in the literature related to the characterization of train-to-ground wireless
links [
7
]. Generally, train-to-infrastructure systems communicate with wayside units using GSM-R or
IEEE 802.11. For instance, the effect that structures like viaducts, bridges and terrain cuttings can cause
in GSM-R has been analyzed exhaustively in the literature [
8
]. Furthermore, in the case of high-speed
scenarios, Wang et al. [9] present a survey on channel measurements and models.
2.2. Inter-Car Connection
Wireless communications and optical fiber can both be employed for inter-car communications.
Nevertheless, the latter is less advised since it may be costly to wire a train for network access, and
rewiring may be necessary each time the train is reconfigured [3].
This scenario demands high data rates and low latencies. The APs are rearranged in each wagon
such that every one acts as a client station for the AP in the previous car, and as an AP for all the
stations within its car. The propagation channel of these communications has been investigated in
literature. An example of wireless channel measurement in order to characterize the propagation
environment for inter-car communications is described in [
10
]. Moreover, a measurement and analysis
of a channel considering the use of Trans European Trunked RAdio (TETRA) is presented in [11].
The communications between vehicles cover several use cases. For example, the information
on-the-fly between two vehicles. It is frequently a disabled one, out of range of a communication network
that transmits information to another vehicle passing nearby [
12
–
14
]. A different example to accelerate
the coupling process is the virtual coupling of two vehicles (including car trains or wagons, subways
and trams). Moreover, specific mechanical connectors that deteriorate rapidly under the rough vibration
conditions in railway operations could be avoided. Nevertheless, for virtual coupling, train-to-train
communications are essential to interconnect high-speed networks embedded in both vehicles.
Currently, the main technologies for inter-car communications are Wireless Fidelity (
Wi-Fi
),
Dedicated Short-Range Communications (DSRC), and Worldwide Interoperability for Microwave
Access (
WiMAX
). Another candidate technology for the wireless connection is Ultra-wideband
(
UWB
), the IEEE 802.15.4a standard. The UWB links are more robust to frequency selective fading.
IEEE 802.11p [
15
] may be an option if high data rates are not required. New technologies at 60 GHz
carrier frequencies like mmWave, IEEE 802.11ad and Machine-to-Machine (
M2M
) communication
systems are also being considered.
2.3. Intra-Car Communication Networks
Since the early 1980s, on-board communication networks were installed on trains to reduce the
wiring used to transfer information between distinct devices like Human-Machine Interface (
HMI
) or
Heating, Ventilation and Air Conditioning (
HVAC
). Multiplexing digital information techniques over a
serial cable have tried to replace most of the classical point-to-point copper lines or so-called train lines.
In 1999, wired communication networks were standardized for on-board railway applications
(the standard was superseded in 2010 [
16
]) by defining Wire Train Bus (WTB) and Multifunction
Vehicle Bus (MVB) networks for Train Control and Management System (TCMS) application.
Standards like CANOpen, LonWorks, Profibus, WorldFIP, Leaky Coaxial Cable (LCX) or Train
Communication Network (TCN) are deployed either for metro or trains. Since the 2000s, manufacturers
considered the Real-Time Ethernet (RTE) technologies by adding new standards to IEC 61375 standard
series [
17
], such as Ethernet Train Backbone (ETB) or Ethernet Consist Network (ECN). Besides
the control-command functions provided by classical field bus technologies, RTE provides Internet
Protocol (IP) traffic. For example, a wired Ethernet network could be taken in consideration, but it
implies high installation costs. In recent years, Power Line Communication (PLC) technology has
experienced significant developments. A review on railway embedded network solutions is presented
in [18].

Sensors 2017,17, 1457 6 of 44
In this scenario the links created by the APs provide wireless access to the passengers and to the
sensors inside the car. Such a scenario is prone to backscattering, which results in attenuations [
13
].
Three wireless access modes can be enabled to provide good coverage inside the cars:
•
Direct transmission from the Base Station (
BS
). The problem in this mode is that the signal from the
BS has to penetrate into the car, what derives in a loss of up to 24dB that needs to be compensated
by incrementing the transmission power and the receiver sensitivity.
•
Use of in-car repeaters. The signals from the BS are received by an on-vehicle transceiver, which
forwards them to a micro-base or to a Wi-Fi signal repeater. Note that this scheme increases
the signal power through repeaters, but these additional devices increase the communications
delay significantly. For this reason, a topic under research is the design and implementation of
transmission schemes that offer good coverage for repeaters at high speeds.
•
Two-hop access mode. In this mode the transmission requires first to travel from the BS to the
antennas located on top of the train, and then to the receiver placed inside the train. This approach
usually avoids the penetration losses related to a direct transmission from the BS. Nevertheless,
it is worth noting that, since high frequency bands have large attenuations and path losses, its use
may derive in a limited coverage.
When the second and third access modes are used, it is necessary that the signal penetrates the
vehicle, what usually causes interferences. Furthermore, the selection of a proper communication
technology depends on the bandwidth requirements, which are mainly conditioned by the services
provided and by the number of simultaneous users. Assuming around 130–180 passengers per
car, a bandwidth of up to 3.6GHz would be needed if half of the passengers demand real-time
HD video. If the streaming service has to provide bidirectional HD video (i.e., video conference),
the bandwidth requirement may double. These bandwidth requirements cannot be fulfilled by LTE,
which only makes use of 20 MHz instead of the 7.2 GHz that would be demanded. The solution
might be provided by mmWave/sub-mmWave bands at 28 and 300 GHz, and the 5G communication
systems, which offer larger bandwidths and higher data rates. For instance, massive Multiple-Input
Multiple-Output (
MIMO
) and 3D beamforming may be used with many users to enhance the system
capacity [3]. For wide area coverage, signals at frequency bands below 6 GHz are needed.
Main Technologies for Intra-Car Communication Networks
Several technologies are embedded in the Train Access Terminal (TAT) to provide a continuous
connection. Furthermore, they are able to link the train to the Internet backbone and to provide Internet
on-board. Apart from the bandwidth requirement, among the criteria to select a specific technology
are generally the connection quality (i.e., the signal strength), delay, throughput, security, and cost.
Two major families of technologies may be considered [19,20]:
•
Satellite solutions. Distinct types are available (i.e., Geostationary Orbit (GEO), Medium Earth
Orbit (MEO), Low Earth Orbit (LEO)) with different frequency bands and that may provide
unidirectional or bidirectional communications. Satellites are used for both locating trains (aided
by Global Navigation Satellite Systems (
GNSS
) systems [
21
], like GPS, the European GALILEO,
the Russian GLONASS or the Chinese BEIDOU) and communicating with the wayside equipment.
•
Terrestrial solutions. They can be grouped into two main categories: (a) technologies that rely on
existing networks (i.e., public cellular networks), and (b) technologies that require ground infrastructure
to be deployed: leaky coaxial cable, Wi-Fi, WiMAX, radio-over-Fiber, and optical solutions.
Apart from legacy systems (usually analog), the trend of applying wireless systems in railways is
still in its first decade of life. There are three types of systems. First, those based on open standards:
TETRA, General Packet Radio Service (
GPRS
), and IEEE 802.11 family of standards; second, open
standards with slight modifications on some layers (e.g., GSM-R); finally, proprietary wireless
communication solutions have also its niche in the market. Traincom by Telefunken [
22
] or
FLASH-OFDM [23] are good examples with a great acceptance in the railway sector.

Sensors 2017,17, 1457 7 of 44
Nowadays, GSM-R is the most widely used communications system between trains and the different
elements involved in operation and control within the railway infrastructure. It operates in 38 countries
across the world, including all member states of the European Union (EU) and countries in Asia, America,
and northern Africa [
24
]. Two frequency bands were reserved by the European Telecommunications
Standards Institute (
ETSI
) for railway communications in Europe in 1995, which are 921–925MHz for
Downlink (
DL
) and 876–880MHz for Uplink (
UL
). For each band, it is possible to allocate 19 subcarriers
of 200kHz, including a guard band. Each subcarrier supports 8 data or voice channels.
A Wireless Local Area Network (
WLAN
) technology such as Wi-Fi represents the most common
deployment on-board, and it is accepted that the replication of Wi-Fi APs within the train is the best
approach to connect trains with a client interface. The delivery of broadband Internet access to trains
has been previously analyzed in the literature and some authors have presented surveys that compare
different technologies for such a purpose (e.g., IEEE 802.11, TETRA, satellite) [25].
Due to the rapid changes in technology, it is clear that railways will have to evolve to keep up
with their pace. With such an aim in mind, in recent years operators have included in their systems
different emerging technologies. For example, WiMAX was tested for train-to-ground deployments
in order to provide Internet services to the passengers [
26
]. Other
WLAN
-based networks have been
evaluated to deliver train operation traffic but, until the development of the IEEE 802.11ac standard,
there was a lack of essential Quality of Service (
QoS
) features related to traffic policy enforcement,
end-to-end resource management or traffic admission.
Likewise, new technologies like Wireless Gigabit (WiGig) or Light-Fidelity (Li-Fi) will have to
be considered in the medium-term [
20
]. On the one hand, WiGig (IEEE 802.11ad), promoted by
the Wi-Fi Alliance, operates at the unlicensed 60GHz band (in Europe 9GHz of bandwidth from
57 to 66 GHz). It offers high-speed, low latency, a throughput of up to 7Gbps with a transmission
distance of up to ten meters, and protected connectivity between nearby devices. Its Medium Access
Control (
MAC
) layer is extended and it provides backward compatibility with the IEEE 802.11 standard.
When operating in the mmWave domain, beamforming techniques are needed to overcome the path
loss from transmitter to receiver, what was not an issue for IEEE 802.11 a/b/g/n due to the use of
omnidirectional antennas. On the other hand, Li-Fi (IEEE 802.15) is a 5G Visible Light Communication
(VLC) system that makes use of light form diodes to deliver mobile and high-speed communications.
For instance, Li-Fi uses amplitude modulation of light sources in accordance to an standardized protocol.
Its main drawbacks are that communications require to switch on a light during transmissions and
that mobility is not possible. For example, the France’s national state-owned company Société Nationale
des Chemins de Fer (SNCF) has been interested in Li-Fi during the last years. For instance, recent
applications involving mass-market devices only have
DL
communications implemented. A project
between Lucioum Company and Leti have developed a bidirectional Li-Fi modem that allows for
providing wireless Internet access of up to 20 Mbps [
27
]. Furthermore, Oledcomm provides Internet
access via Li-Fi [
28
]. On-board Internet transmitting via individual lights of the passengers is a topic
under research.
IIoT can harness the surplus capacity offered by mobile operators in order to provide novel
services. In this way, 4G and 5G broadband can help smart railways to attract users from other
competing transport means thanks to their coverage and the possibility of offering services like
live-video streaming or mobile ticketing. Moreover, safety in railways can be improved through driver
advisory systems (i.e., on-board Closed-Circuit Television (
CCTV
) recordings transferred to a Train
Control Center (
TCC
)), train diagnostics, and driver vigilance detection (for instance, the driver’s
health can be monitored by using a wireless wearable EEG [29]).
Nevertheless, quite a few companies have established a quota limit on throughputs. For example,
Amtrak (Washington, DC, USA) blocks the access to streaming media and limits file downloads to
10 MB [
30
]. Such a quota limit is also employed in the NS Dutch railways, which provide a speed
of 150 kbps per user. Most developments were first rolled out in the 2000s, and they have been
upgraded with the first deployments of 4G technologies and the usage of the Ka band for satellite

Sensors 2017,17, 1457 8 of 44
communications. Nevertheless, although 5G systems are currently discussed in 3GPP, commercial
devices will not be available until 2020. A recent document that sets out requirements and guidance
for Internet provision is presented in [31].
2.4. Inside the Railway Station
The railway station is a scenario characterized by a semi-closed scene with a crowd of people.
The links created by the APs provide wireless connectivity to the users, who are usually interested
in broadband communication services. For such a purpose, a fixed/wireless communication
infrastructure has to be deployed in the stations, which might support operational (e.g., fire protection,
automatic doors, surveillance) and commercial services (e.g., cash desks).
For instance, massive
MIMO
technology is an appropriate choice for providing communications
in railway stations and inside cars, since it is able to achieve high spectral efficiency, high data rates,
and high energy efficiency. Moreover, the transmission modes can be adapted dynamically to the
presence of multiple simultaneous users by grouping hundreds of specifically designed antennas.
2.5. Infrastructure-to-Infrastructure
Infrastructures are connected in real-time and require bidirectional links with high data rates and
low latencies. The information is transferred between the cameras or the
IoT
infrastructure and the
APs deployed on the trains, stations, platforms, and the wayside along rail tracks.
Table 2reviews the main characteristics of the technologies that are commonly used in the
scenarios previously described.
2.6. Wireless Sensor Networks
As a result of the combination of the latest advances in electronics, networking, and
robotics, it is feasible to develop advanced sensor systems for different sectors and applications:
energy efficiency [
32
], Industry 4.0 [
33
,
34
], home automation [
35
], public safety and defense [
36
,
37
],
precision agriculture [
38
] or transportation [
39
]. Furthermore, Wireless Sensor Networks (WSNs) have
evolved into an integral part of the protection of mission-critical infrastructures [
36
]. Today, WSNs
are used in the scenarios previously described, where sensors can be on top of the train, inside,
beside, interacting between railway vehicles and tracks, or even as part of the station infrastructure.
An example in the railway station could be the ticket validation equipment based on Low
Frequency (LF), 125–135 kHz, and High Frequency (HF), 13.56 MHz RFID bands, or vehicle tagging
based on Ultra High Frequency (UHF) RFID solutions [40] in the 865–869 MHz band.
The sensors make use of the protocols listed in Table 3to communicate and organize themselves.
The information collected is transmitted to APs that utilize more powerful communication technologies,
such as the ones cited in the previous sections (e.g., GPRS, Wi-Fi, WiMAX, LTE), to transmit the acquired
data to TCCs. In Section 6.2, the applications of WSNs will be further explained.

Sensors 2017,17, 1457 9 of 44
Table 2. Main characteristics of the most popular communication technologies for railways.
Parameter GSM-R P25 TETRA 802.11 WiMAX UMTS LTE-R RoF LCX Satellite FLASH-OFDM
Frequency DL: 921–925 MHz,
UL: 876–880 MHz 700 MHz 400 MHz 2.4/5.8 GHz 2.4/2.5/3.5 GHz 800/910 MHz,
2.1 GHz
450 MHz, 800 MHz, 1.4 GHz
and 1.8 GHz Variable Variable Limited 450 MHz
Channel
bandwidth 200 kHz 12.5 kHz 25 kHz 20–40MHz 1.3–20MHz 5 MHz 1.4–100 MHz 10–100 MHz 30–1000 MHz >20 MHz 1.5–5 MHz
Peak data rate 172 Kbps 40–100 Kbps 5–10 Kbps >10 Mbps >30 Mbps >2 Mbps (stationary)
>384 kbps (mobile) DL: 50 Mbps, UL: 10Mbps 1–10 Gbps 1–10 Mbps >2 Mbps DL: 5.3 Mbps,
UL: 1.8 Mbps
All-IP in
native mode Not standalone No No Yes Yes Yes Yes Yes Yes Yes Yes
Handover
mechanism Standard Standard Standard Proprietary Standard Standard Standard,soft (no data loss) Standard Standard Variable Proprietary
Modulation
multiplexing GMSK TDMA 4FSK DPSK TDMA QPSK, QAM BPSK, QPSK,
16-QAM PSK QPSK, 16-QAM and
64-QAM (OFDM, SCFDMA)
QPSK,
16-QAM
(OFDM)
Std. and
OFDM FSK-PSK OFDM
Maturity Mature Mature in US Mature Widely
adopted
Mature, lead to
WiMAX 2 Mature Emerging Concepts like
’moving cell’
Mature
(N700)
Mature but
costly Mature
Market
support Until 2025–2030 US Almost
obsolete Yes Decreasing
support Moving to LTE Building standards Mature Japan, Europe
Europe
(Thalys,
SNCF)
Flarion

Sensors 2017,17, 1457 10 of 44
Table 3.
Comparison of the different WSN technologies. Color meaning: green (fully compliant with
railway requirements), yellow (partial fulfillment) and red (non compliant).
Wireless Technology Robustness Real-Time Performance Range Link Throughput Network Scalability Power Awareness
IEEE 802.11
IEEE 802.15.4
Zigbee
Zigbee Pro
IEEE 802.15.1
Bluetooth
WirelessHART
ISA 100.11a
WISA
3. Overview on the Railway Applications Offered by GSM-R
This section details the steps related to the adoption of GSM-R and reviews the main
railway-specific services and requirements.
3.1. GSM-R: The Solution Preferred
The Union Internationale des Chemins de Fer (
UIC
) selected the GSM technology after comparing
it with
TETRA
in terms of usability in railway scenarios. Moreover, GSM was supported by the
GSM Association (GSMA) and it was standardized by
ETSI
as GSM Release 99. After a thorough
analysis,
GSM-R
was eventually standardized by the European Railways and the
UIC
. In Europe,
a relevant initiative for the evolution of the communications was the European Integrated Railway
Radio Enhanced NEtwork (
EIRENE
) project. This cooperation was participated by the European
Commission (
EC
),
ETSI
and several railway operators. EIRENE was aimed at specifying the
requirements for railway mobile networks. To reach such a goal, a functional group and a project team
were established within the project. The functional group was responsible for defining the Functional
Requirements Specification (
FRS
), which guaranteed the interoperability across borders. Regarding
the project team, it was focused on defining the System Requirements Specification (
SRS
). The SRS
details the technical features related to operations, which involved the identification and specification
of supplementary Advanced Speech Call Items (ASCI) features [41].
In 1995, a first draft of the EIRENE specifications was released. At the same time, the UIC became
involved in the Mobile Radio for Railway Networks in Europe (
MORANE
) project, which also included
the participation of the EC, the major railways of Italy, France and Germany, and a number of GSM
suppliers. The objective of MORANE was to design and build prototypes of a new radio system
that met the functional specifications and the system requirements proposed. Railways from all over
Europe signed the Memorandum of Understanding (
MoU
) in 1998 and, in 2009, more companies were
added, including railways outside Europe. In 2000, seventeen railway companies signed an Agreement
on Implementation (
AoI
) to deploy national GSM-R networks no later than 2003. Thereafter, GSM-R
became the reference communication technology in railways until today, when the evolution of the
demand and the emergence of new technologies are fostering the research on alternative solutions.
It is publicly recognized that GSM-R is not well-suited for services such as automatic pilot
applications or for provisioning on-board Internet to the train staff and passengers [
42
]. GSM-R (based on
GSM Phase 2 and Phase 2+ recommendations) was designed aiming to provide the maximum redundancy
while achieving the maximum system availability.
GSM-R
provision two fundamental services: voice
communications and the transmission of European Train Control System (ETCS) messages.
The definition of European Rail Traffic Management System (
ERTMS
) was the result of the
European efforts to promote interoperability. ERTMS includes three levels. Among them, ERTMS
levels 2 and 3 employ GSM-R as the basis that supports communications. In Europe, a 4 MHz
bandwidth is reserved for such communications. The main elements of ERTMS are:

Sensors 2017,17, 1457 11 of 44
•
ETCS: it allows for automating train control. It consists of a Radio Block Center (RBC) and a
Lineside Electronic Unit (LEU). ETCS can be divided into three levels:
–
ETCS level 1: the location of the train is determined by traditional means (i.e., no beacons are
used for locating the train), whereas communications between fixed safety infrastructure and
trains are performed by means of beacons (transponders placed between the rails of a railway
track). GSM-R is only used for voice communications.
–
ETCS level 2: the communications between trains and the railway infrastructure are
continuous and supported by GSM-R technology. The location of the train is estimated
by means of fixed beacons.
–
ETCS level 3: the integrity of the train elements is checked at the train, thus no devices are
required in the track. Fixed beacons are used to locate the train.
•EURORADIO GSM-R: radio infrastructure.
•EUROBALISE: beacons allowing for locating the trains accurately.
•
EUROCAB: on-board management system that includes European Vital Computer (EVC),
Driver-Machine Interface (DMI), and measurement devices such as odometers.
The ERTMS/GSM-R project was initiated by the UIC to unite existing and future developers
to upgrade the GSM-R specifications. The collaboration continues today as an alliance between
ETSI and the GSM-R industry.
FRS
version 8.0.0 [
43
] and
SRS
version 16.0.0 [
44
] (European Railway
Agency (
ERA
)
GSM-R
Baseline 1 Release 0) were published in December 2015, representing the latest
specifications. The mentioned documents describe the Mandatory (M) requirements regarding the
interoperability of railways, according to Directive 2008/57/EC [
45
], and the requirements towards an
IP-based core network architecture [46].
3.2. Railway-Specific Services and Requirements
Following the last EIRENE specifications, the integrated wireless network should comply with the
general and functional requirements under these four categories: Mandatory for Interoperability (MI),
Mandatory for the System (M), Optional (O) or Not Applicable (NA), depending on the type of radio.
Specifically, the following system services are required [47]:
•Services: voice, data, and call related features (Table 4).
•
Voice Group Call Service (
VGCS
) conducts group calls between trains or Base Stations (BSs),
or between station staff and trackside workers.
•
Voice Broadcast Service (
VBS
) is used to broadcast recorded messages or announce operations
to certain groups of trains or BSs. The call set-up required times are shown in Table 5, it shall be
achieved in 95% of cases (MI). Furthermore, call set-up times for 99% of cases shall not be more
than 1.5 times the required call setup time (MI).
•Functional addressing (FA): a train can be addressed by a number identifying its function.
•Location dependent addressing (LDA): calls from a train can be addressed based on its location.
•Shunting mode for communicating to a group involved in shunting operations.
•
Railway specific features [
43
,
48
] include the set-up of urgent or frequent calls through single keystroke
or similar; display of functional identity of calling/called party; fast and guaranteed call set-up;
seamless communication support for train speeds up to 500km/h; automatic and manual test modes
with fault indications; control over mobile network selection; and control over system configuration.
EIRENE-compliant mobile devices must guarantee the core requirements specified in SRS,
together with network requirements and configuration. Furthermore, in the case of high-speed
railways [
49
], as it is presented in Table 1, speeds of at least 220 km/h shall be managed while enabling
speeds over 280 km/h under some circumstances. In general, speeds of 200–220 km/h represent the
threshold for upgraded conventional lines. Nonetheless, connectivity has to be guaranteed at a moving
speed of 500 km/h, or even more [50].

Sensors 2017,17, 1457 12 of 44
QoS mechanisms have to ensure the enhanced Multi-Level Precedence and Pre-emption (
eMLPP
).
Although current networks manage different
QoS
policies according to the traffic types,
QoS
for
real-time applications shall be checked.
QoS
control is needed for resource management. Besides,
as it will be further explained in the next Sections, strict latency requirements are needed for seamless
operation (i.e., train status and location), and the Movement Authority (
MA
) permission between the
in-service train and the control center (i.e., the connection establishment error ratio over one train line
should be less than 1% per hour and 99% of
ETCS
data should have a maximum latency of
<
0.5 s [
51
,
52
]).
Table 6shows a summary of the main GSM-R QoS parameters jointly with their availability.
Table 4.
Services to be supported according to the radio type. Note that Mandatory for
Interoperability (MI), Mandatory for the System (M), Optional (O) or Not Applicable (NA) [43,48].
Service Group Type of Service Cab ETCS Data Only General Purpose Operational Shunting
Voice-Call
Point-to-point MI NA M M M
Public emergency M NA M M M
Broadcast M NA M M M
Group MI NA M M M
Multi-party MI NA O O M
Data
Text message MI NA M M M
General data applications M O O O O
Automatic fax O NA O O O
ETCS train control NA MI NA NA NA
Specific features
Functional addressing (FA) MI NA M M M
Location dependent addressing (LDA) MI M O O O
Direct mode NA NA NA NA NA
Shunting mode MI NA NA NA M
Multiple driver communications within the same train MI NA NA NA NA
Railway emergency calls MI NA O M M
Table 5. GSM-R call set-up time requirements [43,48].
Call Type Call Set-Up Time
Railway emergency call <4 s (M)
High priority group calls <5 s (M)
Group calls between drivers in the same area <5 s (M)
All operational and high priority mobile-to-fixed calls not covered by the above <5 s (O)
All operational and high priority fixed-to-mobile calls not covered by the above <7 s (O)
All operational mobile-to-mobile calls not covered by the above <10 s (O)
All other calls <10 s (O)
Table 6. Main GSM-R QoS requirements.
Requirements Value
Connection establishment delay of mobile originated calls <8.5 s (95%), ≤10 s (100%)
Connection establishment error ratio <10−2(100%)
Connection loss rate <10−2/h (100%)
Maximum end-to-end transfer delay (of 30 byte data block) ≤0.5 s (99%)
Transmission interference period <0.8 s (95%), <1 s (99%)
Error-free period >20 s (95%), >7 s (99%)
Network registration delay ≤30 s (95%), ≤35 s (99%), ≤40 s (100%)
Call-setup time ≤10 s (100%)
Emergency call-setup time ≤2 s (100%)
Duration of transmission failures <1 s (99%)
4. Long Term Evolution (LTE): One Step Ahead of Broadband Communication Systems
As a narrowband system, the main GSM-R shortcoming relates to its limited provision of advanced
data services due to its lack of packet-switched transmissions (Table 7). For instance, in order to deliver
burst low-rate
ETCS
data, connections need to take network resources continually even though not

Sensors 2017,17, 1457 13 of 44
being used. The maximum transmission rate of GSM-R per connection is 9.6 kbps and the packet delay
is in the range of 400 ms, which is sufficient only for applications with low demands [42].
Possible solutions to enhance its limited capacity (i.e., ETCS in high traffic areas) include an
LTE micro-cell deployment or the usage of the ER-GSM band (includes standard and extended GSM
900 band) and changing to ETCS over packet-switched data using
GPRS
, Enhanced General Packet
Radio Service (EGPRS) or Enhanced GPRS Phase 2 (EGPRS2) [53].
Despite the commitment of the GSM-R Industry [
54
] to support GSM-R until 2030, these
shortcomings are encouraging the replacement for different system architectures mainly LTE/LTE-A.
This will be performed by introducing a framework for Control, Command and Signaling Technical
Specifications for Interoperability (CCS TSI) that will enable the migration of technologies that can be
used by the trackside and on-board systems from GSM-R to a next-generation system.
In 2011, the LTE-Advanced (
LTE-A
) specification (Release 10) was introduced. LTE-A meets
formally the requirements of International Telecommunication Union-Telecommunication
Standardization (ITU-T) 4G technology definition known as IMT-Advanced, and the needs
set by the operator-led alliance Next Generation Mobile Networks (
NGMN
). Release 10 provided
a substantial uplift to the capacity and throughput and also took steps to improve the system
performance for mobile devices located at some distance from a BS. Main features included: up to
3 Gbit/s (DL) and 1.5 Gbit/s (UL); carrier aggregation (CA), allowing for the combination of up to
five separate carriers (20 MHz) to enable bandwidths up to 100 MHz, higher order
MIMO
antenna
configurations, relay nodes to support heterogeneous networks deployments and enhanced Inter-cell
Interference Coordination (eICIC). Release 11, functional freeze date including stable protocols in
early 2013, included refinements to existing capabilities: enhancements to Carrier Aggregation,
MIMO, relay nodes and eICIC, introduction of new frequency bands, and coordinated multi-point
transmission/reception to enable simultaneous communication with multiple cells. Release 12
(functional freeze date in March 2015) introduces novel procedures for supporting diverse traffic
types, a number of features to improve the support of HetNets, enhanced small cells for LTE, inter-site
carrier aggregation, new MIMO and beamforming techniques and advanced receivers to maximize
the potential of large cells Proximity Services (
ProSe
), MBMS enhancements, M2M applications,
Self-organizing Networks and interworking between HSPA, Wi-Fi and LTE.
Table 7. System characteristics of GSM-R and LTE-R.
Parameter GSM-R LTE-R
All-IP in native mode No Yes
Frequency DL: 921–925 MHz, UL: 876–880 MHz 450 MHz, 800MHz, 1.4 GHz and 1.8 GHz
Bandwidth 0.2 MHz 1.4–20 MHz
Modulation GMSK QPSK and 16-QAM
Peak data rate DL/UL: 172 Kbps DL: 50 Mbps, UL: 10 Mbps
Peak spectral efficiency 0.33 bps/Hz 2.55 bps/Hz
Cell range 8 Km 4–12 Km
Cell configuration Single sector Single sector
Data transmission Requires voice call connection Packet switching, UDP data
Packet retransmission No (serial data) Reduced (UDP packets)
MIMO No 2 ×2
Mobility 500 Km/h 500 Km/h
Handover success rate ≥99.5% ≥99.9%
Handover type Hard Soft (no data loss)
4.1. Current Status of Standardization
The
UIC
General Assembly of 2008 proclaimed that the rise of the LTE communication
system was threatening the life-cycle of GSM technology and affecting the maintenance of the
equipment. As a consequence, the UIC presented a technical report with the results after examining

Sensors 2017,17, 1457 14 of 44
whether LTE communication system would be applicable to the integrated railway wireless network.
The main outcomes were that LTE technology might be relatively suitable for the near-future railway
communication network and meet various requirements of railway, but noted that additional research
would be required.
Consequently, UIC officially launched the Future Railway Mobile Telecommunication System
(
FRMTS
) project in 2013, which was aimed at developing the next-generation of railway communication
solutions. In particular, UIC further strengthened its cooperation with the 3GPP standard body in
order to reflect requirements of the next-generation network in LTE standards. Furthermore, the
future of transportation will rely on intermodal networks combining the railway, the subway, and
road transportation. For example, in this scenario train-to-ground communications systems will be
based on Wi-Fi and
LTE-A
systems. In a EU context, with the aim of develop cross-border connections
between neighboring countries, and foster innovation and competitiveness, the strategic long-term
policy includes the completion of the single European Railway Area (SERA, Directive 2012/34/EU).
The
ERA
(Regulation (EC) No 881/2004) was established to promote SERA and to help revitalize the
sector while reinforcing its essential advantages in terms of safety. As from 2016, the
ERA
will unify the
large number of national technical rules and develop an improved safety culture (common methods,
targets and indicators) under the Directive (EU) 2016/798. After a three year transition period, the ERA
will be empowered to issue single EU-wide certificates for rolling stock and railway undertakings.
Future wireless systems for railways need to address many issues like cost, spectrum allocation,
and interoperability. Depending on the point of view, current technology is very expensive and
sometimes it is not interoperable. GSM-R is, in some cases, a possible exception in terms of
interoperability. However, open standards like 3GPP LTE imply heavy costs and a possible dependence
on mobile operators, which is unlikely to be accepted by railway operators, apart from other
disadvantages. Despite all these issues, one of the aims of several research groups in Asia and
Europe, and projects all over the world (for example, the Roll2Rail Project [
55
]) is to study feasible
wireless communication technologies for both train-to-infrastructure, inter-train and inside-train
communications. 3GPP LTE introduced some functionality on its latest releases that targets the railway
sector, like mobile relays, or Device-to-Device (D2D) communications. The maturity of LTE standards
to address railway requirements is briefly summarized in Table 8. Moreover, it is also important to
notice the ongoing work on Cognitive Radio (CR) [
56
]. The concept of CR has been highlighted as an
attractive solution to the problem of the congestion of the radio spectrum occupied by licensed users.
Furthermore, it is able to integrate all the heterogeneous wireless networks deployed.
Besides,
LTE
will be the baseline technology for the next generation of broadband public
safety networks. National Public Safety Telecommunications Council (
NPSTC
), TETRA + Critical
Communications Association (
TCCA
), and Critical Communication Broadband Group (
CCBG
) are
contributing to the standardization processes [
57
]. This functionality will become available in
products from 2017 onwards in LTE Release 13 (functional freeze date 2016). Release 13, in addition
to enhancements to existing services and features, includes the completion of the first set of
specifications covering mission-critical services, in particular Mission Critical Push To Talk over
LTE (
MCPTT
). 3GPP continued to work on the characterization of carrier aggregation across additional
band combinations to provide increased bandwidth within the limited frequency allocations to
individual operators. Radio propagation was further improved with studies on MIMO antennas
and sophisticated beamforming techniques. Other major advances achieved included enhancements
to machine-type communications, public safety features, small cell dual-connectivity and architecture,
indoor positioning, single cell point-to-multipoint and work on latency reduction.
In 2015, 3GPP began to work on the next generation cellular technology or 5G, with the
aim of submitting a candidate technology to the IMT-2020 process. Meanwhile, work started on
Release 14 and numerous features and studies had been defined including Multimedia Broadcast
Supplement for Public Warning Systems, mission-critical video and data services, LTE support for

Sensors 2017,17, 1457 15 of 44
Vehicle-to-Anything (V2X), latency reduction, high power LTE for certain bands, channel model above
6 GHz and robust call set-up for Voice over LTE (VoLTE).
Table 8. Main specifications to address railway requirements.
Railway
Requirements Implementation
General specs.
•Detailed requirements for GSM operation on Railways; ETSI TS 102 281 V2.3.0 (2013-07).
•
Usage of the User-to-User Information Element for GSM Operation on Railways; ETSI TS 102 610 V1.3.0 (2013-01).
•Mobile communication system for railways (3GPP TS 22.289, Draft, Rel-15).
•Future Railway Mobile Communication System (3GPP TR 22.889 version 15.0.0 Rel-15).
•Application architecture for the Future Railway Mobile Communication System (FRMCS); Stage 2 (3GPP TS
23.790, Draft, Rel-15).
Voice
•Point-to-point calls; VoLTE (GSMA IR. 92 v 10.0).
•Proximity-based services (ProSe); Stage 2 (3GPP TS 23.303 version 14.1.0 Rel-14).
•Service requirements for the Evolved Packet System (EPS) (3GPP TS 22.278 version 15.0.0 Rel-15).
•Architecture enhancements to support ProSe (3GPP TS 23.703 version 12.0.0 Rel-12).
•Security issues to support ProSe (3GPP TR 33.833 version 13.0.0 Release 13).
•LTE device to device proximity services; Radio aspects (3GPP TR 36.843 version 12.0.1 Rel-12).
•3GPP enablers for OMA; PoC services; Stage 2 (3GPP TR 23.979 version 14.0.0 Rel-14).
•Emergency calls; MS emergency sessions:
−IP Multimedia Subsystem (IMS) emergency sessions (3GPP TS 23.167 version 14.3.0 Rel-14).
−IP based IMS Emergency calls over GPRS and EPS (3GPP TR23.869 version 9.0.0 Rel- 9).
•Group calls/Broadcast including emergency calls
−Voice Broadcast Service (VBS); Stage 2 (3GPP TS 43.069 version 14.0.0 Rel-14).
−
Group Communication System Enablers for LTE (GCSE_LTE); Stage 2 (3GPP TS 23.468 version 14.0.0 Rel-14).
−Mission Critical Voice Communications Requirements for Public Safety; NPSTC BBWG.
−
Public Safety Broadband High-Level Statement of Requirements for FirstNet Consideration, NPSTC Report
Rev B.
−Service aspects; Service principles (3GPP TS 22.101 version 15.0.0 Rel-15).
−Architecture enhancements to support GCSE_LTE (3GPP TS 23.768 version 12.1.0 Rel-12).
•Evolved Multimedia Broadcast Multicast Services (eMBMS) (3GPP TS 23.246 version 14.1.0 Rel-14); MBMS;
Protocols and codecs (3GPP TS 26.346 version 14.2.0 Release 14).
eMLPP
•QoS concept and architecture (3GPP TS 23.107 version 14.0.0 Rel-14).
•Service-specific access control; Service accessibility (3GPP TS 22.011 version 15.0.0 Rel-15).
•E-UTRA; RRC; Protocol specification (3GPP TS 36.331 version 14.2.2 Rel-14).
•IMS multimedia telephony communications service and supplementary services (3GPP TS 24.173
version 14.2.0 Rel-14).
•AT command set for User Equipment (UE) (3GPP TS 27.007 version 14.3.0 Rel-14).
•Multimedia priority service (3GPP TS 22.153 version 14.4.0 Rel-14).
•Enhancements for Multimedia Priority Service (3GPP TR 23.854 version 11.0.0 Rel-11).
Call related
•Call Forwarding supplementary services (3GPP TS 22.082 version 14.0.0 Rel-14).
•
Call Waiting (CW) and Call Hold (HOLD) supplementary services; Stage 1 (3GPP TS 22.083 version 14.0.0 Rel-14).
•Call Barring (CB) supplementary services; Stage 1 (3GPP TS 22.088 version 14.0.0 Rel-14).
•Numbering, addressing and identification (3GPP TS 23.003 version 14.3.0 Rel-14).
LDA
•LTE Positioning Protocol (LPP) (3GPP TS 36.355 version 14.1.0 Release 14) and Annex (3GPP TS 36.455
version 14.1.0 Rel-14).
•Functional stage-2 description of Location Services (LCS) (3GPP TS 23.271 version 14.1.0 Rel-14).
•Location Services (LCS); Mobile Station (MS) - Serving Mobile Location Centre (SMLC) Radio Resource LCS
Protocol (RRLP) (3GPP TS 44.031 version 14.0.0 Rel-14).
By the second half of 2017, the focus of 3GPP work will shift to Release 15 in order to deliver
the first set of 5G standards. For instance, the importance of forward compatibility in both radio
and protocol design was stressed. Its functional freeze date including stable protocols would be on
September 2018, thus the timeframe for a commercial deployment will be at the end of the decade.

Sensors 2017,17, 1457 16 of 44
One of the most remarkable proposals for the definition of 5G is the utilization of Filter
Bank Multicarrier (FBMC) modulations instead of the well-known Orthogonal Frequency-Division
Multiplexing (OFDM). The next are the most important advantages offered by FBMC with respect to
OFDM for the railway environment:
•
FBMC offers higher bandwidth efficiency, which is very beneficial since the simultaneous
communications between different trains can be more efficiently allocated into the scarce spectrum
available in railway environments.
•
Coexistence between the current GSM-R and the new broadband systems is a major concern in
the railway industry. OFDM-based systems usually exhibit a high co-channel interference, leading
to a potential performance impact on current GSM-R systems. FBMC-based systems are much
more efficient, thus allowing for better coexistence with current systems.
•
Improved multiple-access facilities in the
UL
: due to the use of close-to-perfect subcarrier filters
that ensure frequency localized subcarriers, FBMC does not require sophisticated synchronization
methods for avoiding multiple-access interference. Nevertheless, while OFDMA is suitable for
allocating efficiently a subset of subcarriers per user in the
DL
, the situation is different in the UL,
because user signals must arrive at the Evolved NodeB (eNodeB) synchronously, both in terms
of symbol timing and carrier frequency. For a practical deployment, a close-to-perfect carrier
synchronization is necessary, which is affordable in a stationary network, but becomes a very
difficult task in a network that includes mobile nodes.
•
Suitability for doubly dispersive channels: the waveforms used in FBMC can be optimized for
doubly dispersive channels like the ones present in high-speed train communications, hence
allowing for a compromise between time and frequency channel response.
However, there are some drawbacks. It must be noticed that channel estimation is more
challenging in most FBMC schemes with respect to OFDM. Moreover, whereas OFDM offers full
flexibility regarding MIMO structures, FBMC can only be used in certain MIMO schemes. Only schemes
such as Filtered MultiTone (FMT) offer the same flexibility as OFDM, but FMT suffers from the same
bandwidth loss as OFDM. Alternatives to FBMC such as Generalized FDM (GFDM) and Filtered
OFDM (fOFDM) are also being considered as candidates for 5G systems.
For inter-car communications, IEEE 802.11p is planned to be deployed in smart cars in the near
future. Therefore, IEEE 802.11p can be an option for inter-train communications if high data rates are
not required. Other solutions based on UWB technology or mmWave solutions in the range of 60GHz
carrier frequencies are expected.
Furthermore, spectrum allocation is always a challenge. Industrial Scientific and Medical (ISM)
bands at 2.4 and 5 GHz are always available but they imply potential problems, in terms of security.
Additionally, there is some discussion on the possibility of using the Intelligent Transportation
System (ITS) band at 5.9 GHz for urban rail systems [
58
]. Facing the problem from the business
perspective, partnerships with mobile operators to deploy mobile networks and also to provide some
non-safety services to operators and stakeholders is possible but implies some regulatory challenges
that should be addressed.
4.2. Migration Roadmap
Recently,
UIC
has started the migration from GSM Phase 2+ to
LTE
ensuring that the life cycle
of
GSM-R
will be extended. Some researchers point out that the coexistence between
GSM-R
and
LTE-Railways (
LTE-R
) is expected to last years [
42
]. Furthermore,
LTE
migration is envisaged to move
at different paces. Considering the lack of a global standard for Communications Based Train Control
(
CBTC
), metro railway operations are likely to adopt LTE quickly, particularly in new lines. In main
lines under international standards, the transition will occur probably in two phases. Initially, the non
mission-critical services will be carried out by the LTE networks, while safety and mission-critical
services and features will keep using legacy networks. Following the maturity of
LTE
, all services will

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be then gradually transferred [
59
]. At the moment that suppliers standardize
ETCS
on IP networks,
LTE-R will replace GSM-R. A schedule of LTE-R deployment is shown in Table 9.
Before considering different scenarios for future communications in railways, a number of
hypotheses have been proposed to determine where the changes to the current environment are
likely, and may influence options for the future. These hypotheses consider the period relevant to the
study (i.e., the next 15+ years) and are listed in Table 10. As it can be observed, communications and
applications are the ones that are expected to evolve at a higher pace.
Table 9. Time frame of LTE-R in Europe.
Phase 2008–2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
System definition
Transition strategy
Develop new-generation terminals
New-generation terminal trials
New-generation terminal roll-out
New-generation infrastructure trials
New-generation infrastructure transition
Transition complete
Table 10. Hypotheses influencing the future railway environment (next 15+ years).
Parameter Expected Evolution [59]
Organizational
model
In Europe, the scenario will not change substantially. Regulation for all member states will
come from the EU, but overall responsibility will continue to be held at a national level.
Voice
requirements
It may change over time. Some stakeholders have indicated some interest in making use of
voice communications which are barely used today (e.g., for communications with train crew
and/or passenger announcements independently of the communications between driver and
controller). Some of the voice functions of GSM-R, such as the REC, may cease to be critical
voice requirements if alternative solutions are available (e.g., if the emergency call and halt to
train movement is handled through data/signaling).
ETCS
It currently uses GSM circuit-switched data and it is being evolved to allow the operations over
IP packet networks. ETCS operation over GSM-R GPRS is ongoing.
Signaling
requirements It will not change substantially over the next 15+ years.
Communications
The technologies in use will continue to change rapidly with a major evolution in networks,
services and devices over 3–5 year cycles.
Applications
The demand for more data applications will increase. Innovative services needed to
increase profits.
Radio spectrum
In key bands, spectrum for mobile use will continue to be in high demand, becoming
increasingly scarce and costly to acquire.
5. The Rise of the Internet of Trains
Long before IoT was coined, railway operators and infrastructure managers were actively using
M2M technology and data analysis to improve the maintenance and performance of their assets.
The IIoT has had a major impact on the transportation industry, with the advent of autonomous
vehicles and improved cargo management. Nevertheless, although they may have been pioneers, the
reality is that the rail industry has barely scratched the surface of what is possible. As IIoT continues
to evolve, it is bringing greater standardization, openness, and scalability to the information provided
to operators: they gain insight into how their assets are performing, which opens up many new
possibilities to use big data in more creative and effective ways. Nonetheless, the fact that trains
operate at such high speeds through tunnels and extreme weather conditions, presents real challenges
when it comes to deploying IIoT systems.
Regardless of the challenges, IIoT has the potential to revolutionize the railway industry. A rail
network comprises thousands, if not millions of components, from rolling stocks to signals, rails and

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stations. All elements need to work cooperatively. The Internet of Trains holds the promise that rail
systems can leapfrog interoperability, safety, and security issues, while modernizing rapidly. It refers to
the use of networks of intelligent on-board devices connected to cloud-based applications to improve
communications and control systems. The same network that strengthens safety has enough capacity
to deliver data that serves a variety of applications across the rail system to reduce costs and improve
operations. The usage of IIoT is possible thanks to advances in the following underlying technologies:
•
Telecommunications networks are becoming dedicated to IIoT applications and, as it was
described in Section 2, broadband communications are getting inexpensive, faster, and ubiquitous.
Train companies run fiber along their tracks and have relationships with mobile operators to use
their networks to maintain continuous mobile connectivity. M2M technology can boost efficiency
by using sensors embedded into different objects and systems to automate tasks and deliver
real-time monitoring and analysis.
•
Sensors for data acquisition are getting smaller, more affordable, and now consume less energy.
In some cases, battery life can be extended to up to five years, which is important, because it is not
always possible to be close to an electrical supply.
•
Cloud-based services have become more pervasive, fueled both by fast connectivity and
ever-smarter devices. They can be used to store sensor data and to provide the computation
required for big data analytics.
•
Big data and the Cyber-Physical System (
CPS
) enabled by Industrial IoT (
IIoT
) allow the different
transportation modes to communicate with each other and with the surrounding environment,
paving the way for truly integrated and intermodal solutions.
Industrial IoT Developments in the Rail Industry
Renowned commercial companies have been investing recently in IIoT. Next paragraphs outline
opportunities for the railway ecosystem (i.e., technology vendors and operators), including some maintenance
and monitoring initiatives that today are making the smart railway ecosystem vision a reality.
Trenitalia’s Frecciarossa is working with SAP to develop a Dynamic Maintenance Management
System (
DMMS
) [
60
]. The system presents cost savings between 8% to 10% of its maintenance bill.
In this case, hundreds of sensors collect data in real time (from braking systems to the sliding doors),
uploading them into SAP’s cloud every ten minutes. Trenitalia runs 8000 trains per day using a fleet
of 30,000 locomotives, coaches, and freight cars. Once the data are in the cloud, they are analyzed
using SAP Predictive Maintenance and Service software and they are processed by the predictive
analytics tool SAP HANA. Thus, Trenitalia can build predictive models using machine learning and
also trigger actions (for example, when engine temperature hits a particular threshold, to help keeping
trains running without delays). Key metrics, and diagnostic and management data are accessible by
engineers and are visualized in real-time: the number of trains that are out of service, alerts that imply
a maintenance action, the status of trains on the track, or the number of passengers. DMMS will be
fully up and running across all Trenitalia’s rolling stock in 2018 and it will generate a full petabyte
of data annually. Next, Trenitalia is hoping to automate the few remaining parts of diagnostics and
maintenance that cannot be spotted by sensors, such as the roof and undercarriage of the trains, which
still need a visual inspection. In the future, these tasks will be automated using cameras, instead of the
visual inspection required today.
In 2013, the Finnish state-owned railway company [
61
], in order to improve its competitiveness,
started to embed sensors into its systems to monitor possible failures related to the weather conditions.
Previously, such a company performed maintenance in two ways. First, there was a scheduled
maintenance that affected the most critical systems (e.g., bogies and wheels). Due to this type
of maintenance, parts were replaced even when they still could be used more time. The second
maintenance procedure consisted of fixing parts after they broke down. This kind of maintenance
could not be predicted easily and derived in missed routes and unsatisfied customers. In order to
prevent the problems that arose from these two maintenances procedures, the company developed

Sensors 2017,17, 1457 19 of 44
a predictive maintenance program that monitors the state of the most relevant parts constantly.
This system estimates through mathematical models when a part is likely to fail, so that it can be
replaced before to avoid unplanned downtimes. To optimize the time between maintenance events,
the company analyzes the data collected through a Statistical Analysis System (SAS) and determines
if critical elements like the turning wheels or the wheel-and-axle sets need to be replaced. With all
the improvements carried out, the company estimates that maintenance work will be reduced by
35 %. Moreover, since the cause of a failure can be identified more easily, the reliability of the trains is
enhanced and savings are obtained in terms of time and money. Furthermore, the knowledge obtained
through the system allows the company to minimize their spare part stock, buying and keeping only
what is predicted that will be needed in the near-future.
Predictive maintenance is also encouraged by Siemens together with Teradata [
62
]. They expect
predictive maintenance will evolve towards next-generation maintenance, creating a whole new
business model to provide completely new services with up-time guarantees, risk-sharing models,
and performance-based contracts for mobility systems.
Another example is represented by the French SNCF [
63
], which is also using IIoT powered by
IBM Watson’s deep learning analytics platform and SigFox’s IIoT network. These alliances are part of
the company’s 2020 strategy to become an industrial leader striving for operational excellence and
optimum efficiency.
SNCF has developed a prototype where data acquisition devices are fitted into the transmission
system on a Train à Grande Vitesse (TGV). Data are transmitted over GSM-R and can be accessed
remotely at the train depot, enabling technicians to see how well the gearbox is performing. SNCF also
uses Sigfox communications devices to measure the water level tank in TGV toilets, what speeds up
the turnaround time when the train arrives at a depot. Besides, engineers can connect to running trains
in real-time, enabling SNCF to figure out whether a component is likely to fail, which could lead to the
train being taken out of service. The cloud enables SNCF to run distributed calculations, whose results
can be reinjected into its train and rail maintenance processes.
6. IoT-Enabled Services: From More Efficient Operations to New Business Models
Legacy infrastructure is gradually being replaced by Train Management Systems (
TMS
), which
transform trains in communications hubs that exchange data among them and with network control
centers. Moreover,
M2M
communications allow operators to optimize and make safer use of equipment
and infrastructure. The following subsections describe examples of IIoT-enabled services (a general
vision is shown in Figure 3).
Figure 3. Industrial IoT-enabled services relevant to the rail industry.

Sensors 2017,17, 1457 20 of 44
6.1. From Reactive to Predictive Maintenance
It is expected that maintenance costs will rise in the next years due to the aging of the infrastructure
and the increasing number of passengers and freight transported. Due to this trend, there is a demand
for monitoring complex maintenance operations related to the different elements that conform the
railway system [64].
Maintenance decisions about critical items of infrastructures can be improved by using the precise
location of the train, its speed, weight, data from vibration sensors located alongside the track, weather
reports, and details on how long the power connector is disconnected from the catenary during
operations [
65
]. The fusion of this information with other meta-data, such as catenary dilation factors
and track temperature, can further enhance the decision-making process and help to create more
sophisticated rail scheduling software. For instance, Firlik et al. [
66
] studied how to monitor the state
of light rail vehicles and their tracks. The researchers made use of sensors embedded into the axle
boxes to adapt dynamically maintenance requirements and speed limits.
For instance, in [
67
] it is analyzed how to schedule preventive maintenance depending on different
strategies. Furthermore, in [
68
] the authors studied the application of big data techniques to make
decisions on the maintenance of railway tracks. A similar approach is described in [
69
,
70
], where
the authors propose the use of an expert and a Decision Support System (
DSS
) to plan and schedule
different common rail activities.
In recent years, ballasted tracks have been replaced by systems based on slabs, which are more
secure and sustainable in high-speed railways. Preventive maintenance can be carried out in such
infrastructures by embedding sensors that track movements and vibrations [
71
,
72
]. The design of
such systems has to consider large areas in remote places that have no Internet access or electricity.
In such scenarios the information collected is sent to passing trains that transmit the data later to a
Remote Control Center (
RCC
). Another research article focused on the evaluation of the influence
of train vibrations is presented in [
73
]. In the case of [
74
], the system proposed includes threat
detection. For instance, Sa et al. [
75
] present a shape-based method for analyzing the normalized
electric current patterns in Railway Point Machines (consisting in a motor, reduction gear, several
bearings, derive-detection rods, and switches) in order to detect the replacement conditions with
acceptable accuracy.
Ngigi et al. [
76
] describe the benefits of using predictive control systems to monitor different
activities. Such a kind of systems is used for making better decisions when determining maintenance
procedures. Specifically, there is a type of algorithms called Model Predictive Control (MPC) that has
been devised explicitly for monitoring actions related to certain assets in order to anticipate events.
The complexity of the vehicle dynamics usually involves using approaches like extended Kalman
filters, which are able to estimate the dynamic performance of certain elements as the train moves, or
particle filtering, which can assess the state in non-linear non-Gaussian scenarios in order to detect
imminent faults. Furthermore, Ngigi et al. point out that
Wheelset Condition Monitoring (WCM)
systems can be applied to estimate the deterioration of the wheels through different sensors and
automatic identification systems. Finally, the authors conclude that, in order to monitor the conditions
in high-performance real-time scenarios, it is required to make use of WCMs and simulation techniques.
Another example of DSS is presented in [
77
]. Saa et al. propose a smart tool that helps railway
infrastructure designers to create more secure and more efficient electrification systems. The tool
prevents dangerous situations associated with errors during the design stage. The main novelty of the
article is its holistic approach, which includes all railway systems in order to optimize infrastructure
design through a knowledge system based on rules that incorporate expert knowledge. However, the
researchers indicate an obvious disadvantage: the tool depends on the information received from the
railway companies, which are usually not very collaborative among them. It is expected that this
problem will be tackled in part by the European initiative for railway interoperability.

Sensors 2017,17, 1457 21 of 44
Key Findings
IIoT can increase the safety and efficiency of the rail traffic by providing preventive maintenance.
Economic savings can also be obtained through the simplification of processes and making better
decisions by using analytics based on sensor fusion of the data collected from trains and other
infrastructures. Information concerning the categorization of faults can be analyzed across multiple
assets, even multiple operators, to spot trends and identify areas for preventive maintenance.
Additionally, data analytics can speed up root cause analyses, reducing labor time.
It is also required to increase the level of automation on maintenance procedures and other routine
tasks, like ballast replacement, tamping, and track relaying. For example, remote monitoring helps to
reduce maintenance time in the train depot. Another example can be train windshield water tanks
equipped with a level sensor. Thus, a technician is able to access this information via a web application
on a tablet to see whether the water needs topping up. The amount of data that has to be collected
requires high-capacity wireless train-to-ground communications.
The following are a list of improvements that can be achieved:
•Increased up-time through a significant reduction of unplanned downtime.
•Extension and flexibility of maintenance intervals because the risk is understood.
•Improved utilization of assets (e.g., more mileage with fewer cars).
•Enhanced planning, with streamlined Supply Chain Management (SCM).
•
Maintenance can be performed at the least costly location. IIoT will have an important role in
applications for dynamic maintenance as a provider of additional sources of data collected by
sensors. In this way, in a Computer Integrated Manufacturing (CIM) context, an Enterprise Resource
Planning (ERP) will act as an ad-hoc software extension that will manage the collected data.
•Uptime guarantees can be provided.
•
Increased service contract capture rate, recurring revenues, and higher percentage of the total
service revenue.
6.2. Smart Infrastructure
Infrastructure monitoring (bridges, viaducts, tunnels, rail gaps, frozen soil, leaky feeders) can
provide significant benefits in different aspects like efficiency or safety.
Furthermore, the lack of safety and security monitoring of railway infrastructure increase the risk
of train collision, derailment, terrorism and failures in the wagons. For instance, with 35% of train
delays still caused by infrastructure or rolling stock failures, this is one obvious area where IIoT could
offer vast improvements in performance.
6.2.1. Advanced Monitoring of Assets
Sensors can be used to decrease the failure rate and enhance the reliability of trains, signals,
and tracks. Such sensors are able to monitor equipment with the objective of generating alerts about
the need for attention on critical elements of the train. In this way, costs are decreased and asset
usage is optimized by lowering the number of trains taken out of service for inspection, preventive
maintenance, or for replacing certain parts after a deficiency is detected. The most common systems to
be monitored are represented in Figure 4.
As it was previously explained in Section 2.6, infrastructure is usually monitored by using WSNs,
which are able to assess the condition of tracks, track beds, bridges and the equipment placed on
the tracks. Moreover, WSNs can be used to monitor tunnels or to detect intrusions and abandoned
items in stations. It is worth mentioning Structural Health Monitoring (SHM), which is currently an
essential field for the railway industry [
78
] and which has been reviewed recently in the literature [
79
].
Traditionally, SHM systems made use of sensors wired to data acquisition systems, but, thanks to the
evolution and the lower cost of wireless devices, in recent years researchers have proposed solutions
based on WSNs [
80
]. A relevant requirement is the need for a precise time synchronization with a

Sensors 2017,17, 1457 22 of 44
resolution of microseconds [
81
]. This requirement is due to the fact that certain measurements, like
vibration monitoring, demand accurate timing and synchronized sensing at high sampling rates [82].
Figure 4. Systems usually monitored in a train.
The reflections derived from the metallic structures of the train also pose a problem for
WSN communications due to the multipath effect. This issue can be addressed by modeling the
communications channel and then selecting a proper physical layer. Different researches have
studied this problem in different SHM scenarios, including bridges [
83
] and railway tracks [
84
].
Another example of a WSN deployment at railway tracks to analyze the vibration patterns caused
by trains is described in [
85
]. Moreover, existing monitoring methods are studied in [
86
], where the
authors integrate three methods to monitor rail damage in the turnout (railroad switch) zone (i.e., fiber
optic detection systems, optical imaging and Lamb guided wave detection systems).
WSN-based condition monitoring for the rail industry is the object of the survey presented in [
87
].
Such a review analyzes the most commonly used sensors for condition monitoring, their configuration
and the main network topologies. Another interesting WSN-based system for condition monitoring
is proposed in [
88
], where it is collected data on the infrastructure structural health during the trip
to later send the information to a
BS
. Then, such a
BS
makes use of the next trains as data mules to
upload the information.
Another real-time WSN-based railway SHM system is presented in [
10
]. In such an article the
researchers focused on designing a customized MAC layer and a synchronization algorithm. According
to the results, the sensor nodes that belonged to the same BS presented jitter values within 1
µs
, while
nodes from different BSs had a maximum jitter of 2
µs
. It is also interesting the work of Li et al. [
89
],
who describe models and algorithms for optimizing the physical topology of a sensor network aimed
at monitoring the condition of the infrastructure.
A railway bridge is monitored by a WSN-based solution in [
90
]. In such a system eight nodes
collect data that are transmitted to a TmoteSky that acts as BS. The system uses accelerometers to detect
when a train approaches and crosses the bridge monitored. The system uses a self-organizing routing
protocol whose objective is to make the data reach the BS, which sends them to the
RCC
through
a UMTS transceiver. Other authors proposed similar WSN-based systems for monitoring railway
infrastructure either by using Wi-Fi [91] or Zigbee [92].

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6.2.2. Video Surveillance Systems
These systems are able to show high-resolution images or videos within the train (onboard, during
the operation of the train), along the tracks (for the advanced monitoring of assets) or in the station [
93
].
Intelligent CCTV cameras not only provide a record of events in case of an incident, but actively
provide real-time alarms on the occurrence of potential problems, allowing for obtaining timely intervention
responses and potentially reducing service outages. Moreover, if video recording are required by a law
enforcement agency, there is no need to send personnel on-board to obtain the hard drive manually.
The images collected can be either stored in a local server or transmitted in real-time to TCCs.
In the past, operators detected the lack of a surveillance on-board solution, particularly because
of the absence of broadband wireless communication systems between trains and the control center.
Moreover, there are just a few examples in the literature that study video surveillance in railways.
An example is the Security Management System proposed by Bochetti et al. [
94
], who integrated
devices for access control and surveillance. Their work also includes the development of a security
platform that uses a middleware to embed heterogeneous sensors and that is able to adapt QoS
requirements to the priority of the data transmitted.
A video surveillance platform deployed in subway of Beijing (China) is presented in [
95
].
The development includes different modules that allow the platform to manage different elements of
the system, setup alarms, detect failures automatically or visualize all the data on a GIS map.
Apart from the subway lines, there are few CCTV systems for conventional trains and high-speed
trains due to the lack of effective wireless train-to-ground communications. An exception of such
systems is the one proposed by Flammini et al. [
96
], which is aimed at correlating data from different
sources like environmental sensors, intrusion detection sensors, positioning systems, identification
systems, video-surveillance devices and even from CBRNE (Chemical Bacteriological Radiological and
Nuclear) sensors. Regarding this last type of radioactivity sensors, they are deployed for preventing
attacks based on the so-called dirty bombs. To detect explosives, there exist specific devices usually
installed near the turnstiles that can detect evidence of gun powder on the hands and clothes.
Furthermore, to detect weapons and explosives on passengers, it is currently under research the
use of terahertz cameras.
6.2.3. Operations
The same infrastructure put in place to provide safety applications can also be used for non-safety
applications in order to leverage the investment made by the operators.
Subways and suburban rails can make use of the data sent by trains to indicate the customers
through smartphone applications when such trains are scheduled to depart, arrive, or if there are
delays. Additionally, IIoT can modify current railway business models: the use of analytics based
on the usage determined by sensors may transform providers from sellers to leasers of equipment.
This change yields constant sources of revenue for the providers, while altering the expenses of the
operators from CAPital EXpenditure (CAPEX) to Operating Expenditure (OPEX).
A remarkable example of operations enhancement is the design of an Electric Multiple Unit (
EMU
)
IIoT-system presented in [
97
]. It is oriented to the Maintenance, Repair and Operation (
MRO
) of
high-speed trains in China. Massive seamless embedded RFID tags and sensors in train-ground
transmission networks are able to perceive the status of high-speed trains in real-time, using
holographic train visualization and delivering transit alerts. The use of multi-source and multi-level
raw data in maintenance and repair processes, collecting various production aspects, such as train flow,
part flow, labor flow, and equipment, helps to monitor productive processes and logistics during the
whole life-cycle. This study is expected to increase 25% the productive output of EMU operation-level
maintenance and 20% the overhaul-level maintenance which includes dismantle, repair and assembly.
Nowadays, railway Information and Communications Technology (
ICT
) system implantation
is growing, while the deployment of DSS based on the data collected is still emerging. There are
currently interoperability issues among the different systems, what derives into missed opportunities.

Sensors 2017,17, 1457 24 of 44
The use of models based on semantic data is one of the ways to improve interoperability, since they
allow for an easy integration of data coming from diverse sources. For example, Briola et al. [
98
] use
ontologies and natural language interfaces to handle the information collected from a traffic control
center. Moreover, Tutcher et al. [
99
] study the most relevant design patterns to provide extensibility
and interoperability, and introduce the concept of Asset Monitoring As A Service (AMaAS).
Operation scheduling is a complex problem that is influenced by multiple factors, like track capacity
or travel distance. Some researchers have proposed its modeling through solutions able to create line
plans by specifying different parameters, like the train capacity, passenger demand, the line frequency,
the number of transfers or the stopping patterns [
100
]. The interested reader can find further information
in [101–105], where the problem of real-time scheduling is considered under different approaches.
6.2.4. Key Findings
The railway industry can benefit from the WSN ability for carrying out easy deployments that can
reach places where a wired solution may not be installed. Additionally, such deployments can harness
the flexibility, scalability, and self-organizing capabilities of WSNs. Nevertheless, WSNs have to deal
with several issues specific for railway environments, like communications reliability, the necessity for
higher sampling rates for measuring fast changing dynamic signals (e.g., vibrations), fast transmission
rates, the capacity of managing high-volumes of data, energy efficiency, energy harvesting and the
possibility of managing heterogeneous sensor signals (data fusion).
Regarding video surveillance systems, the existence of a real-time viewing mode, a record mode,
and a search/playback mode allows security managers to avoid threats. These systems support
video analytics, intelligent incident response, and emergency communications. Cameras can increase
passenger safety and protect assets by integrating video surveillance systems across a network
infrastructure. By integrating heterogeneous subsystems (environmental and intrusion detection
sensors, positioning and identification systems) and potentially thousands of cameras, an overall view
of the whole infrastructure (i.e., trains, tracks, depots, and stations) can be obtained by operators
and management systems at the TCC or by the staff operating in the field, with video analytics and
real-world maps identifying, locating, and recording threats.
Train delay is one of the most relevant factors that affect the perceived quality, since it affects the
capacity of the system, its punctuality, its reliability and even its safety. Moreover, the availability of
precise train positioning is essential for setting routes, controlling the traffic, rescheduling, and for offering
accurate information to the passengers and the maintenance operators. Thanks to the transmission of
real-time positioning data to control centers, the systems embedded into the train can help to reduce
congestion by optimizing the deployment of the equipment and managing the track capacity.
6.3. Information
Railway industry is also currently challenged by the improvement of the experience of the
passengers and the management of the freight. On the one hand, passengers usually demand better
train punctuality, precise scheduling information and improved on-board entertainment. On the other
hand, logistics often require cost-effective solutions that include the whole monitoring of the freight.
Thus, taking the characteristics of the information into consideration, two types of targets have to be
distinguished: passenger and freight.
6.3.1. Passenger Information System (PIS)
PIS is a key communications link between operators and passengers. PIS represents an electronic
operating tool that provides, at any given time, visual and acoustic information to passengers
on a route, both automatically or programmed manually. PIS includes real-time train tracking,
route information and scheduling, travel planning, passenger infotainment (real-time HD video
for entertainment or business, video conference, live broadcast), and online connectivity solutions.
Along with system safety and reliability, the ability of the operators to provide accurate and useful

Sensors 2017,17, 1457 25 of 44
information (i.e., departure/arrival times), and more comprehensive services, as well as the feeling of
being in control or participating, is a key component of passenger satisfaction.
PIS architecture spans across three different environments: rails, fixed installations such as stations
and depots, and a centralized control center. This architecture is shared with the security, control and
monitoring, and network functionalities. A wireless or wired connection is used for communication
between the display device, the station computer, and the main server. The current position of trains is
transferred to the relevant computer stations through the main server, where data are displayed, and
new data for next stops can be calculated. The TCC is used for controlling and monitoring the trains.
A PIS example could be a trip planner application that could recommend the fastest or most
comfortable trip, showing live train times, available car parking or passenger loading. Passengers will
make informed choices about what option will provide them with the best experience according to
their personal preferences (i.e., whether it is more important to have the shortest trip time, or to reserve
a seat). The inclusion of historic data will enable the evaluation not only for a current trip, but also in a
predictive way for a trip planned in the future.
The combination of passenger loading information from trains with social networking applications
will help to spread demand peaks [
106
]. For example, this can be achieved by offering the most efficient
passenger exit considering the loadings of other inbound trains. In the case of interoperable tickets
(valid for metro, buses, and bikes), intermodal travel could be encouraged by providing seamless
connections to other modes [107].
Moreover, fusing status information from diverse on-board public-facing assets such as toilets,
chillers and ovens, and presenting it to service organizations with current positional information, can
improve the customer experience and reduce the penalty costs associated with having these assets
out of service. Toilets can be automated to reduce costs and provide better service to the passengers.
Currently, most operators are no able to determine in real time the state of toilets without performing
a manual checking. Regarding the food, it could be replenished at a station if information about
the items sold are available in real time. Furthermore, to avoid problems with the refrigerators,
which cannot be in service constantly, temperature might be monitored and controlled remotely.
Traditional hand-held ticketing systems are being updated slowly to more sophisticated solutions
that alleviate the crowdedness and ease the passenger trip. Furthermore, considering that several
smart cards used in public transportation present faults [
39
,
40
], innovative solutions are emerging, like
electronic ticketing systems that use QR codes, RFID and NFC [
108
], and even ticket-free solutions,
like the pilot, due to begin in July 2017, between the UK-based rail operator Chiltern Railways and
the travel technology company SilverRail Technologies [
109
]. Such a pilot will use Bluetooth sensors
that activate an geolocation tracking app used to open ticket gates and determine the trips performed.
In this case, the customer is billed at the end of the day with a best-value guarantee ensuring they
are charged the appropriate fare for their trips. Note that in order to provide security to the ticketing
system, dedicated communication lines should be deployed.
6.3.2. Freight Information System (FIS)
Railways offer an alternative for freight transport that has low external costs and a reduced
environmental impact. In fact, trains consume less energy and emit less CO
2
than the other means
of transport by road, air and water. However, currently there are legal, operational and technical
constraints that reduce its capacity and performance. Moreover, the reliability of this specific services
need to be improved. The modal share of rail transport is modest, with rail accounting for 11%
transportation in Europe, and 6% of intra-European passenger transport according to reports of the
EC in 2014. There are two major challenges that have to be faced. First, there has to be created a new
specific profile aimed at on-time deliveries. Second, a growth of productive capacity and an increase of
cost competitiveness by addressing current challenges, such as interoperability, the optimization of
existing infrastructure, and the promotion of synergies from other sectors.

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FIS delivers real-time information on freight traffic to provide a significant picture of freight
transportation movements, effectiveness, and planning. FIS is subdivided into two solutions: operation
management solutions for capacity and freight management, which ranges from booking to rolling stock
planning; and tracking solutions for real-time location information of cargo containers. FIS helps freight
operators to make infrastructure and planning decisions based on robust, reliable, and consistent data.
Several research articles have dealt in recent years with freight trains. For example,
Scholten et al. [
110
] focused on monitoring their integrity. In [
111
] it is presented another system for
the transport of dangerous materials by rail. In such an article it is evaluated the generation of business
rules from a semantic knowledge system using the information collected about different elements and
parameters of the rail system.
Among the numerous elements to be monitored, rolling bearing is specially interesting, since
it is used in freight trains and it is considered an important part whose fault can affect train safety.
Infrared and acoustic monitoring techniques have been tested for monitoring the rolling bearing, but
they have disadvantages, like the detection of false positives. Additionally, on-board monitoring
solutions are not useful in freight trains, because cars are not attended and there is not a constant
power supply. Nan et al. [
112
] propose a WSN-based solution for freight trains that allows for
monitoring rolling bearing through accelerometers, which are actually used to measure the vibrations.
Another application for the monitoring of freight trains transporting hazardous materials is presented
in [
113
,
114
]. The application uses a WSN to measure environmental parameters using heterogeneous
sensor technologies.
Tunnels, bridges and highway crossings are examples of the elements that can be encountered by
a train on its way. The most common problems that occur in these scenarios are due to differences
in substructure and loading conditions. For instance, if the track is deformed substantially at
these points, the dynamics of the train change, what eventually derives into a deterioration of the
structural elements. The identification of the factors that contribute to this deterioration, as well
as its mitigation through maintenance procedures, are essential for safety and economic reasons.
For instance, Tutumluer et al. [
115
] evaluated track transition performance in different high-speed
scenarios, analyzed the reasons behind the deteriorations and proposed diverse methods for improving
track performance.
Rail freight operations planning together with revenue management has not been reported
generally in the literature. Crevier et al. [
116
] present a new bi-level mathematical formulation that
combines both pricing decisions and network planning policies (e.g., car blocking and routing as well
as the assignment of blocks to trains and scheduling). Besides, Bilegan et al. [
117
] propose a strategy
for increasing revenues by accepting or rejecting transportation requests in order to accommodate
the future foreseen demands with higher potential profits. In [
118
] it is presented a model for
evaluating the decisions taken in inter-modal transportation that includes the contributions related to
operators, providers and users. The optimal policy is characterized by Luo et al. [
119
], showing how
dynamic forecasting coordinates capacity leasing and demand acceptance in intermodal transportation.
Furthermore, Wang et al. [
120
] studied how to optimize the benefits of container transportation
operators by allocating resources when the capacity and the effects on the network are unknown.
Finally, other authors focused on specific applications, like Masoud et al. [
121
], who analyzed the
optimization of sugarcane rail transport systems.
6.3.3. Key Findings
PIS tools improve passenger experience while allowing for offering informed choices, determining
the status of main facilities or using innovative solutions of smart ticketing. A FIS also improves
the labor utilization and productivity, and nowadays is widely adopted by logistics companies for
better customer support and loyalty. The following are the main advantages of FIS for railways:
improved dynamic train performances; real-time information provision, which is especially important
in the case of hazardous goods and to plan revenue management; it enables the intercommunication

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and exchange of information from train-to-ground; and remote real-time diagnosis using sensors
embedded into wagons.
6.4. Train Control Systems
6.4.1. Autonomous Systems
There are two types of autonomous systems: semi-automatic and fully autonomous. The former
are related to operations like signaling and train braking systems. The latter make use of artificial
intelligence techniques like genetic algorithms and fuzzy logic. There are not many references in the
literature about fully autonomous trains and most are focused on subways or light rail systems [
122
].
6.4.2. Safety Assurance and Signaling Systems
The improvement of the safety is one of the major goals when applying IIoT in railway
environments. For instance, an accurate on-board positioning system is essential in order to determine
the position of other trains and then avoid collisions, perform safer operations in close proximity and
optimize the use of the tracks. Another safety application is related to the measurement and control of
the speed [
123
]. There are currently systems that show the train speed to the drivers and later report it
to central control systems. Some of such system are able to interact with wayside signaling systems
with the objective of regulating the train velocity and they are even able to stop the train completely if
certain conditions are met.
There are four major systems where automation and IIoT can bring significant advantages:
signaling, level crossing control, interlocking, and dispatching.
Signaling systems can adjust remotely the speed and braking of the train. Signaling systems are
usually equipped with RFID devices that are embedded into the tracks, but wireless ground-to-train
signaling is becoming habitual. Most of the new European lines are equipped with
ETCS
level 2, as it
was explained previously in Section 3, which requires train-to-ground communications.
Level crossing control has also a huge impact on safety. According to ERA, in 2010 more than
300 people died in incidents that took place at level crossings, representing 30% of all the deaths related
to European railways. IIoT can help to decrease those statistics by deploying cameras and sensors for
increased safety. One example in the literature relying on video is presented in [
124
]. Other alternatives
use Ultra-Wide Band (UWB) technologies [125].
Interlocking works together with the signaling system to avoid collisions at crossings
and junctions. It essentially makes use of traffic lights and signals that prevent trains from moving
forward if a scenario is not safe. IIoT enables the automation of the interlocking system and enhances
it by integrating the data received from the signaling system.
Furthermore, comprehensive dispatching information including text, data, voice, image, and
video, can be provided by drivers and yards to the dispatcher. Supporting functionalities such as
voice trunking, dynamic grouping, temporary group call, short messaging, and multimedia messaging,
is also needed. For instance, in case of automatic driving, dispatching video stream of doorways is
required to ensure that doors are clear prior to the train departure.
The data collected from on-board and wayside embedded devices provide a large amount of
information that can be exploited through data mining techniques, which allow for the identification
of structural patterns that cannot be discovered easily. Several researchers studied the application of
such techniques in railway scenarios. For example, Goverde et al. [
126
] exploited the information
from the describer records of a train to evaluate potential conflicts associated with the scheduling or
the capacity of the railway system. Furthermore, such information is also used in [
127
] to develop a
model for predicting the timing of certain events. The same authors from the previous reference also
assessed the viability of using data mining techniques to exploit rail data like business processes [
128
].
Similarly, in [129] different real-time techniques are studied, but for controlling train traffic.

Sensors 2017,17, 1457 28 of 44
Recently, the authors of [
130
] proposed the use of fast scheduling and routing metaheuristics for
managing train traffic in busy situations, taking special care of the control efficiency in conflictive
traffic situations (e.g., multiple train delays). Furthermore, there is a recognized need for providing
train locations in real time, which should be compliant with the railway safety requirements [
131
]
(e.g., with the EN 50126 standard). Thus, some researchers evaluated the use of
GNSS
receivers for
train positioning [
132
]. The results presented by the researchers show that, in a forest scenario, the
GNSS-based system does not fulfill the requirements, and that it is required a sensor fusion structure
composed by on-board positioning sensors. Therefore, a positioning system composed by a Doppler
radar sensor and a GNSS receiver will meet the requirements.
A WSN architecture focused on secure railways is described in [
133
]. Such a system measures
acceleration and makes use of ultrasounds to identify spoilage on railroads. Another system for
detecting objects on the tracks is presented in [
134
], where the researchers apply image processing
and electromagnets for the detection. Additionally, Wang et al. [
135
] use a WSN for early earthquake
detection in high-speed railways that is able to send fast warnings to the control center.
6.4.3. Cyber Security for Railways
As it was shown in previous Sections, rail systems have evolved significantly towards new
technologies and communication-based systems led primarily by the technological progress. Despite the
fact that security in the railway industry has been always related with operational safety, due to the
increasing integration of
ICT
into land transport, mobile units and infrastructure, the number of
potential cyber risks has risen steadily during the last decade. In the same way, train control systems
are relying more and more on ICT systems and radio communications, even for the ones automated.
Cyber security is about protecting information systems against theft or damage, thus defending
them against external and internal attacks and risks, in particular as a result of criminality. For this
reason, future research related to rail security should focus on the rising of new cyber threats. For
instance, the generalization of automation and computerization in the rail vehicles and signaling
systems, could also become a high potential risk.
Every railway operator faces the massive challenge of protecting its own infrastructure reducing
its vulnerability to cyber-attacks. In most cases, heterogeneous
ICT
technologies and software solutions
are used and result in wide-ranging and diverse data sets. The protection of such environments is
complex and multi-dimensional. A proper design of the architecture of the infrastructure will help to
improve resilience, but it is essential to integrate safety into every aspect of the solution throughout
its life-cycle. Cyber systems used on rail networks may be subject to unauthorized access through
various means: remotely, via the Internet, or unsecured communication networks; through direct
contact with infrastructure (e.g., through a USB port); locally, through unauthorized access to physical
infrastructure, or an insider threat (e.g., infiltration).
Rail operators have to comply with a set of international industry and government standards
on the topic of security (e.g., ISO 27001, NIST SP800-53, ISA/IEC 62443 or APTA). Nonetheless, each
infrastructure and each security solution is unique. While meeting national and international security
regulations, it is necessary a comprehensive analysis on how to design and protect information systems.
It is also essential to develop, implement and maintain integrated solutions, and added-value services
to protect sensitive information at any given time.
The most relevant vulnerabilities are related to weaknesses in control systems, information
systems, system procedures, configuration and maintenance, software development, the communications
network, or in the lack of training and awareness. All of them can be exploited by threats that can be
originated by many sources, including:
•
Connecting physical infrastructure (e.g., tracks, tunnels, bridges/viaducts, switches/rail junctions).
•Mobile units (e.g., locomotives, rolling-stock system).
•Train stations (e.g., exterior, interior or restricted areas) and areas outside the train station.
•Control systems (e.g., signaling, central and local rail traffic management).

Sensors 2017,17, 1457 29 of 44
•Communication systems and communication network.
•Power supply (e.g., catenaries, power supply, national grid, diesel stations).
•
Staff (e.g., driving personnel, handling personnel, maintenance personnel, information
processing personnel).
•Freight (e.g., non-dangerous, explosive, toxic, flammable).
•Passengers.
Regarding the classification of assets presented, access, construction techniques, control command
and communication systems are considered to be the most vulnerable elements of the railway
transportation systems. Indeed, these central elements are easily exposed to malicious uses leading to
serious threats.
6.4.4. Key Findings
Train control systems include signaling and safety assurance processes. Level crossing
control, dispatching video stream or on-board positioning systems are examples of improvements.
Furthermore, opportunities for research exist on scheduling and maintenance planning and on event
prediction, among other new activities, considering autonomous and semi-autonomous operation.
Cyber security is another field to be studied with more depth, since cyber risks are exacerbated by the
enormous quantity of data resulting from the increasing number of devices, processes and services integrated.
Even more, there are several sources of vulnerabilities, whose countermeasures have to be designed.
6.5. Energy Efficiency
IIoT can play an important role to promote energy efficiency taking the EU environmental,
financial and regulation concerns into account. Moreover, the techniques required for optimizing
energy efficiency are strictly related to other solutions previously described for tackling issues like
advanced asset monitoring. However, note that up to four energy-efficiency levels can be distinguished
in railway scenarios [
136
]: energy-efficient driving, the coordination and re-scheduling of multiple
trains in real time, the creation of energy-efficient timetables, and energy-saving planning.
Regarding the coordination, Xun et al. [
136
] propose an autonomous system to coordinate trains
by optimizing the time spent between contiguous stations. Before departure, every train is able to
determine the optimum running time by estimating when the preceding train will depart from the
next station. Thus, the system has to achieve a good balance between energy efficiency and the time
waited by the passengers.
Energy efficiency can also be determined through smart metering methods. With a knowledge
of the different consumers it is possible to perform an efficient energy management. Smart metering
also allows for improving the management of assets and increases capacity. Three elements can be
distinguished in smart metering systems: sensors, the communications between the different sensors,
and the train-to-ground communications that require broadband links.
For instance, wheel bearings can be monitored through WSNs [
137
]. In such a paper two issues
are investigated experimentally: propagation and energy efficiency. First, the electromagnetic wave
propagation characteristics around a train for high signal reliability. Measurements show that the
path-loss exponent is different depending on the scenario. In general, the path-loss exponent is lower
on top of the train than beside the train. The value of the path loss exponent with narrowband
at 434 MHz is on average 3.67 with antennas are located under the train, compared to 2.27 on top.
The communications were evaluated during a five-week field trial onboard a train in bad weather
conditions. The number of messages transmitted successfully per day was in average about 92%.
The lost messages were due to fading or mechanical damages of the sensors. Second, energy scavenging
for minimum maintenance of the sensor network was investigated. The researchers verified that the
sensors could be powered by solar power. However, a theoretical study indicates that the most suitable
method to power the sensors is energy scavenging by vibration.

Sensors 2017,17, 1457 30 of 44
The implementation of optimized train trajectories is also a topic under research. Speed profiles
reduce energy consumption by avoiding running at reduced speed or unessential braking while
arriving at planned times. An optimized train trajectory can be realized using a driver advisory system
or Automatic Train Operation (ATO). Furthermore, the optimized trajectory needs input data, such as
the train’s position, gradient, direction, speed and maximum speed, dwell time, and station locations.
In [
138
] it is proposed a genetic algorithm for optimizing the train speed profile. The results obtained
following the advice generated by the DAS when updating the system every meter, showed that
the optimized trajectory could save energy up to around 25%. However, a train positioning system
error under 100 m increases the energy consumption by less than 0.3%, while an error under 500 m
increases it by less than 1.5% for uphill lines; and 1.3% and 5.2%, respectively, for a downhill line.
These results imply that it is sufficient to locate a train through a GPS to save energy and that, for such
a purpose, it is not necessary to make use of high-precision positioning data. Further research includes
dynamic DAS to recalculate an optimized trajectory when a correction of the train location occurs in
real-time. Additionally, a DAS connected to the TCC can be evaluated regarding the effect of train
positioning errors when following an updated timetable, as well as the impact of such errors on the
traffic management system. Bocharnikov et al. [
139
] examined the effects of varying the acceleration
and braking performance in electrically powered suburban railways. Their solution makes use of a
genetic algorithm that it is able to determine the optimal trajectory of the train from a set of simulations.
Key Findings
Energy-efficiency is cross-related to the other explained advanced services. For instance, WSNs
can include energy scavenging capabilities for monitoring assets. Furthermore, additional energy
savings and emission reductions can be achieved by considering the implementation of timetable
optimizations (coordination and re-scheduling of trains in real time), the use of wayside devices for
the storage of energy, smart metering methods, or energy-efficient driving (optimized train trajectories,
enhanced vehicle comfort control and speed profiles).
6.6. Summary
The main benefits explained in Section 6, summarized for the interested readers in Table 11, are
just the tip of the iceberg and many other areas that could offer potential benefits have probably not
even been identified yet. This table serve as reference to compare the different scenarios, objectives,
technologies and architecture of the most relevant systems. However, please note that, due to the
diversity of the systems analyzed, a straightforward comparison of experimental results would
not be fair. Indeed, as it can be seen in Figure 5, enabling technologies, massive data aggregation,
correlation, and analysis using highly-sophisticated algorithms have the potential to change operations,
maintenance, yield management, and even passenger services in the future. As it was shown in this
comprehensive survey, the IIoT is set to revolutionize train operations, enabling to improve customer
service and the competitiveness of trains.

Sensors 2017,17, 1457 31 of 44
Table 11. Advanced services for the IoT-connected railways.
Service Reference Techniques Main Contributions
Predictive maintenance
Rabatel et al. [
64
]
Expert systems
Anomaly detection in complex maintenance operations. Precision
is in all cases above 90% limiting both the number of false alarms
and the number of undetected anomalies.
Thaduri et al.
[65]
State-of-the-art, analytics,
sensor fusion and Big
Data
Precise location of a heavy freight train and its main parameters.
Firlik et al. [66] Sensors, optimization
procedures
Adjust the maintenance needs and track speed limits
dynamically using embedded sensors. Experimental results of
the implementation.
Soh et al. [67] State-of-the-art
Different strategies for preventive maintenance scheduling
problem: hybrid genetic algorithms, ontology-based modeling,
heuristic approaches and strategic gang scheduling.
Nunez et al. [68] Big Data
Maintenance decisions regarding railway tracks, all parts of
the track can be monitored with appropriate intervals while
maintaining the processing load within feasible limit.
Turner et al.
[69,70]
Expert systems, DSS,
ontologies
Knowledge based systems to develop a prototype for
maintenance scheduling.
Canete et al.
[71,72]
WSN, Zigbee
Monitoring system for slab track infrastructures using an energy
consumption optimization strategy.
Xu et al. [73]
WSN, remote monitoring
Monitor the slope deformation, the variation in the internal stress
and the PPV (Peak Particle Velocity) in an existing slope adjacent
to a railway track.
Flammini et al.
[74]
WSN
Early warning system for infrastructure surveillance and
threat detection.
Sa et al. [75] Shapelet algorithms
Detecting replacement of Railway Point Machines (RPMs) using
an electric current sensor.
Ngigi et al. [76] State-of-the-art
Applications of modern predictive control methods, analysis tools
and techniques for condition monitoring systems.
Saa et al. [77] Ontologies, knowledge
rules-based system
Tool to design complex infrastructures.
Advanced monitoring
Ostachowicz et al.
[78]
State-of-the-art Trends in SHM
Kouroussis et al.
[79]
State-of-the-art
Overview about the static and dynamic behaviour of ballasted
railway tracks in SHM. Estimation of stress transfer from the train
passage to the track using predictive numerical models.
Aygün et al. [80] State-of-the-art, WSN
General applications, SHM network topology and deployments,
hardware/software properties, communication protocols and
standards; and energy harvesting solutions.
Wang et al. [81] State-of-the-art, WSN Integration of different types of sensors for SHM.
Giannoulis et al.
[82]
State-of-the-art, WSN
Qualitative and quantitative analysis of WSN requirements,
accurate timing and synchronized sensing for high sampling
rate sensors.
Kolakowski et al.
[83]
Sensors, ultrasonic
probeheads, numerical
models
Tests over a railway truss bridge.
Lai et al. [84] Sensors
Development and experimental results of a liquid level sensor
based on a fiber Bragg grating for monitoring differential
settlement of railway track.
Berlin et al. [85] WSN, feature extraction Analysis of the vibration patterns caused by trains passing by.

Sensors 2017,17, 1457 32 of 44
Table 11. Cont.
Service Reference Techniques Main Contributions
Advanced monitoring
Chen et al. [86] Sensors, optical imaging,
knowledge-based
systems
Monitor rail damage in the turnout zone.
Hodge et al. [87] State-of-the-art Sensors,
WSN
Review of network design for condition monitoring.
Chen et al. [88]
High-level programming
abstraction, WSN,
middleware
Practical application for SHM, results obtained using the
Cooja simulator.
Val et al. [10] WSN
Time-synchronized network for SHM, the design includes channel
measurements, network topology and architecture, physical
and MAC layer design and network discovery. Performance
evaluation show maximum sampling synchronization jitter values
within 1
µ
s for sensor nodes belonging the same base station, and
2µs for nodes of different base stations.
Li et al. [89] Artificial intelligence,
dynamic programming
and genetic algorithms
Modeling the physical topology optimization for SHM.
Bischoff et al.
[90]
WSN
Bridge structural monitoring based on events to achieve energy
efficient operation.
Franceschinis et al.
[91]
WSN
Predictive monitoring of train wagon conditions. Performance,
based on ns-2 simulation results, suggests that the combined
use of WSN and Wi-Fi in a hierarchical architecture is adequate
for long trains (e.g., several coaches) and a large number of
sensing nodes.
Anjali et al. [92] WSN
Zigbee-based collision avoidance system that relies on
vibration sensors.
Video security
Ambellouis et al.
[93]
State-of-the-art
Analysis of surveillance systems, architectures, detection and
analysis of complex events, onboard surveillance, applications to
railway transport and review of the main worldwide projects.
Bochetti et al.
[94]
Video analytics, artificial
intelligence
Security management system integrating heterogeneous intrusion
detection, access control, intelligent video-surveillance and sound
detection devices. Probability of detection of at least the 80% for
most alarms (including motion detection, unattended luggage,
yellow line crossing) and a false alarm rate of less 10 nuisance
alarms per day.
Li et al. [95] System framework
Comprehensive video surveillance and management platform,
successfully applied in the operation of Suzhou Subway Line 1.
Flammini et al.
[96]
Bayesian networks
Framework with detection models for the evaluation of
threat detection.
Operations
Zhang et al. [97] IoT, complex event
processing
Design of Electric Multiple Unit (EMU) IoT-system oriented to
Maintenance, Repair and Operation (MRO) including holographic
train visualization and alerts.
Briola et al. [98] Ontology, natural
language processing
Management of data collected from the centralized traffic control,
improvement of the user interface through the exploitation of
natural language queries.
Tutcher et al. [
99
]
Ontology, natural
language processing
Asset Monitoring As A Service (AMaAS).
Fu et al. [100] Decision support system,
heuristics
Integrated hierarchical approach for creating line plans
Yang et al. [101] Human-computer
interaction, mathematical
models
System for completing cyclic train timetables in high-speed
railway scenarios
Wegele et al.
[102]
Decision support
systems, rescheduling
algorithms
Dispatching support tools for re-ordering trains in case of delays.

Sensors 2017,17, 1457 33 of 44
Table 11. Cont.
Service Reference Techniques Main Contributions
Operations
Ho et al. [103] Particle Swarm
optimization (PSO)
The performance of PSO is evaluated by comparing the service
quality of the resulting timetables obtained from a sequential
timetable generation approach.
Albrecht et al.
[104]
Heuristics
Space search to re-schedule timetable in case of infrastructure
maintenance to minimize total delay and maximum train delay.
Tan et al. [105] Discrete-event
optimization model
Optimization algorithm for the real-time management of a
complex rail network.
PIS
Ai et al. [106] State-of-the-art
Combination of passenger loading information from trains with
social networking.
Stelzer et al.
[107]
Architecture design
Information exchange for connection dispatching, optimization
of the interchange times for existing connections in
intermodal transport.
Fingar et al. [
108
]
Sensors, RFID, QR and
NFC
Solution that enables the use of phones for acquiring electronic
public transport ticket.
Chiltern
Railways [109]
Sensors, bluetooth Application that open gates and determine the journeys taken.
FIS
Scholten et al.
[110]
WSN Monitoring integrity of cargo trains.
Zarri et al. [111] Business rules,
knowledge
representation, W3C
languages
Checking rail transport of hazardous materials.
Nan et al. [112] WSN
Monitoring of rolling bearing in freight trains, comparison of
different routing protocols and use of data compression and
coding schemes based on lifting integer wavelet and Embedded
Zerotree Wavelet (EZW) algorithms.
Casola et al.
[113,114]
WSN, embedded
systems, cryptography
Monitoring of freight trains transporting hazardous materials.
Analysis on network performance by measuring the packet loss
rate on different nodes in two working conditions: train standing
in the station and train running.
Tumuler et al.
[115]
Instrumentation,
numerical analysis
Performance monitoring of track transitions under different
loading environments. Identification of different factors
contributing towards this differential movement, as well as
development of design and maintenance strategies to mitigate
the problem.
Crevier et al.
[116]
Operations planning,
bilevel optimization
Revenue management for rail freight using bilevel mathematical
formulation which encompasses pricing decisions and
network planning.
Bilegan et al.
[117]
Multi-commodity flow
problem, probabilistic
mathematical model
Revenue management policy to dynamically accept/reject
transportation requests in favor of forecasted demands with
higher potential profit.
Sirikijpanichkul
et al. [118]
Agent-based modelling,
ontologies
Model for evaluating decisions on the positioning of road-rail
inter-modal freight hubs.
Luo et al. [119] Dynamic forecasting,
stochastic comparison
Revenue management in intermodal transportation.
Wang et al. [120] Stochastic resource
allocation
Resource management for containerized cargo transportation.
Masoud et al.
[121]
Mixed integer
programming, heuristics
Scheduling optimization of the performance of sugarcane rail
transport system.

Sensors 2017,17, 1457 34 of 44
Table 11. Cont.
Service Reference Techniques Main Contributions
Autonomous systems, safety assurance and signaling systems
Dominguez et al.
[122]
ATO speed profile
A computer aided procedure for the design of optimal speed
profiles for automatic subway and light rail systems. The newly
designed profiles result in 20% of savings versus the one already
in use. Taking into account the implementation of an on board
storage device, up to 47.5% of savings could be expected.
Guo et al. [123] ATP driver-machine
interface, GUI model
Interface for controlling over-speeding automatically.
Salmane et al.
[124]
Dempster–Shafer, hidden
Markov model
Detecting hazard situations at level crossings with video analytics.
Govoni et al.
[125]
State-of-the-art, fixed
object scanner algorithm
Surveillance of railway crossing areas with UWB.
Goverde and
Meng [126]
Data collection and
processing
Detection of conflicts due to timetable flaws or
capacity bottlenecks.
Kecman et al.
[127]
Timed-event graph
model, prediction
algorithm
Model for predicting accurately the timing of certain train events.
Kecman et al.
[128]
Process mining
Automatic identification of route conflicts with conflicting trains,
arrival and departure times/delays at stations, and train paths on
track section and blocking time level.
Corman et al.
[129]
Advanced mathematical
models, automatic tools
for rescheduling traffic in
real-time
Real-time control of railway traffic.
Sama et al. [130] Alternative graph,
disjunctive
programming,
metaheuristic algorithms
Fast scheduling and routing trains in complex and busy
railway networks.
Marais et al. [21] State-of-the-art GNSS-based solutions for signaling applications.
Lu et al. [132] Stochastic Petri net
model
GNSS and sensor fusion in train localization.
Aboelela et al.
[133]
WSN, fuzzy data
aggregation
Multi-layered and multi-path routing architecture to predict
inclinations in track.
Daliri et al. [134] WSN, fuzzy logic,
sensors
Image processing and electromagnetic detection of
hazardous objects.
Wang et al. [135] WSN Monitoring system for early earthquake detection.
Wu et al. [140] Key management
protocols, cryptography
Secure train-to-train communication schemes: autonomous
train-to-train channel with asymmetric cryptographic primitives
and quasi-autonomous train-to-train channel with symmetric
cryptographic primitives.
Chan et al. [141] Key update scheme Secure key establishment for train-to-infrastructure networking.
Bennetts et al.
[142]
State-of-the-art Securing railways: plans against the identified threats.
Greenberg et al.
[143]
Simulation tools
Models that replicate rail passenger traffic flows, model to trace
chemical plumes released by a slow-moving freight train, model
that estimates the regional economic consequences of a variety of
rail-related hazard events.
Energy efficiency
Xun et al. [136] Analytical methods of
coordinated train control
Fully automatic operation system by modifying the running time
between adjacent stations.
Gruden et al.
[137]
WSN, remote sensing,
energy scavenging
Monitoring the wheel bearings, the number of successfully
transmitted messages per day is in average about 92%, lost
messages are caused by fading dips or mechanical damages of
the sensors.
Hamid et al.
[138]
Genetic algorithms
Design of an optimized train trajectory, energy by up to around
25% can be saved.
Bocharnikov
et al. [139]
Genetic algorithms
Optimal train trajectories in electrically powered suburban
railways. Energy savings of up to 40% may be achieved for a
10% increase in journey time.

Sensors 2017,17, 1457 35 of 44
Figure 5. Enabling technologies for the IIoT of railways.
7. Conclusions
This survey examined the role of enabling technologies to revolutionize the railway industry.
Broadband technologies, like LTE, provide the capacity needed to create novel services. A formal
analysis regarding GSM-R requirements and services was presented to provide an understanding of
future customer needs. LTE Release 11 includes the first feature for public safety (i.e., high-power UE).
Starting from LTE Release 12, the standard adds characteristics such as IMS emergency calls, ProSe, PoC,
GCSE, and eMBMS that will evolve LTE/LTE-A to be used as part of a broadband public safety network.
LTE Release 13 includes the first set of specifications for mission-critical scenarios including MCPTT,
enhancements of ProSe and GCSE, and the isolated E-UTRAN operation. Although the feasibility
of LTE in the railway environment is evaluated, the deployment of a brand-new ecosystem will also
require the design of a thorough migration strategy. In addition, WSNs constitute an essential part of the
protection of the infrastructure, and M2M technology can boost efficiency by using sensors embedded
into objects and systems to deliver real-time analysis and monitoring while enabling automation.
The fast pace of ICT technologies (e.g., cloud computing and big data) and communication
networks enable the adoption of Industrial IoT to integrate the thousands, if not millions of components,
from rolling stocks to the station. The Internet of Trains paradigm holds the promise that rail systems
can leapfrog interoperability, safety, and cyber security issues, while modernizing rapidly. It refers to
the use of networks of intelligent on-board devices connected to cloud-based applications to improve
communications and control systems. The same network that strengthens safety has enough capacity
to deliver enhanced data that serves a variety of applications across the rail system to reduce costs and
improve operations.
Furthermore, the adoption of the paradigm opens a wide area of short- and medium-term potential
applications. Examples like predictive maintenance, smart infrastructure, advanced monitoring of
assets, video surveillance systems, railway operations, Passenger and Freight Information Systems
(PIS/FIS), train control systems, safety assurance, signaling system, were detailed in order to expose
the IoT capabilities to reinforce competitive advantages, to create new business models, and to change

Sensors 2017,17, 1457 36 of 44
railways. For each of the services, the latest technologies and the main academic and commercial
developments were thoroughly examined.
After all the analyses performed, it can be stated that the Internet of Trains and IIoT still face
many challenges, such as standardization, interoperability, scalability, energy efficiency and cyber
security, which would have to be addressed by researchers that will have to cope with the additional
issues posed by railway environments and the specific nature of the operations and the networks.
Acknowledgments:
This work has been funded by the Xunta de Galicia (ED431C 2016-045, ED341D R2016/012,
ED431G/01), the Agencia Estatal de Investigación of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REDC,
TEC2016-75067-C4-1-R) and ERDF funds of the EU (AEI/FEDER, UE).
Author Contributions:
Paula Fraga-Lamas and Tiago M. Fernández-Caramés contributed to the overall study
design, data collection and analysis, and writing of the manuscript. Luis Castedo contributed to the overall
writing of the manuscript. All of the authors approved the final version of the manuscript.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
3GPP 3rd Generation Partnership Project
ASCI Advanced Speech Call Items
BS Base Station
BSC Base Station Controller
CA Carrier Aggregation
CBTC Communications Based Train Control
CCBG Critical Communication Broadband Group
CCTV Closed-Circuit Television
CoMP Coordinated Multi-point
DL Downlink
DSS Decision Support System
EGPRS Enhanced General Packet Radio Service
EIRENE European Integrated Railway Radio Enhanced NEtwork
eLDA enhanced Location Dependent Addressing
eMBMS Evolved Multimedia Broadcast Multicast Service
eMLPP enhanced Multi-Level Precedence and Pre-emption
EMU Electric Multiple Unit
eREC enhanced Railway Emergency Call
ERA European Railway Agency
ERTMS European Rail Traffic Management System
ETCS European Train Control System
ETSI European Telecommunications Standards Institute
FA Functional Addressing
FRS Functional Requirements Specification
GCR Group Call Register
GNSS Global Navigation Satellite Systems
GSMA GSM Association
GSM-R Global System for Mobile Communications-Railways
HMI Human-Machine Interface
IMS IP Multimedia Subsystem
IMT-Advanced International Mobile Telecommunications - Advanced
IoT Internet of Things
QoE Quality of Experience
QoS Quality of Service

Sensors 2017,17, 1457 37 of 44
LAS Link Assurance Signal
LDA Location Dependant Addressing
LTE-A LTE-Advanced
M2M Machine-to-Machine
MAC Medium Access Control
MBMS Multimedia Broadcast Multicast Service
MBSFN Multicast and Broadcast over Single Frequency Networks
MCPTT Mission Critical Push To Talk over LTE
MRO Maintenance, Repair and Operation
MS Mobile Station
OFDM Orthogonal Frequency Division Multiplexing
PoC Push-to-Talk over Cellular
ProSe Proximity Services
RAMS Reliability, Availability, Maintainability and Safety
SIL Safety Integrity Level
TCC Train Control Center
TEDS TETRA Enhanced Data Service
TETRA Trans European Trunked RAdio
UIC Union Internationale des Chemins de Fer
UL Uplink
VBS Voice Broadcast Service
VGCS Voice Group Call Service
VoLTE Voice over LTE
WiMAX Worldwide Interoperability for Microwave Access
WLAN Wireless Local Area Network
WSN Wireless Sensor Networks
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