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Internet of things for smart ports: Technologies and challenges

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Nowadays, the Internet of Things (IoT) can be considered an important technological revolution related to smart cities, smart homes, smart factories and smart ports implementations. As the presence of smart sensing systems in ports becomes a reality, different operation areas are working today in automatic mode. Examples of challenging projects related to smart ports in the IoT era can be found from Europe to Asia, to Australia, and to North America; in all of these new architecture implementations, sensing technologies play a key role. This paper highlights the main requirements and the key ideas for each ports, sensing solution and also the challenges related to the calibration and testing of distributed sensing systems associated with the main equipment that compose the world largest ports, such as quayside cranes, automated guided vehicles for container handling and yard cranes. Details of the architecture and operations and sensing systems for smart ports are described. Communication standards for smart ports are discussed, and smart ports implementation examples regarding structural health monitoring are considered. Conclusions and future research opportunities in the IoT era are addressed in the final section of the paper.
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34 IEEE Instrumentation & Measurement Magazine February 2018
1094-6969/18/$25.00©2018IEEE
Internet of Things for Smart Ports:
Technologies and Challenges
Yongsheng Yang, Meisu Zhong, Haiqing Yao, Fang Yu,
Xiuwen Fu, and Octavian Postolache
N
owadays, the Internet of Things (IoT) can be con-
sidered an important technological revolution
related to smart cities, smart homes, smart factories
and smart ports implementations. As the presence of smart
sensing systems in ports becomes a reality, different opera-
tion areas are working today in automatic mode. Examples of
challenging projects related to smart ports in the IoT era can be
found from Europe to Asia, to Australia, and to North Amer-
ica; in all of these new architecture implementations, sensing
technologies play a key role. This paper highlights the main re-
quirements and the key ideas for each ports, sensing solution
and also the challenges related to the calibration and testing of
distributed sensing systems associated with the main equip-
ment that compose the world largest ports, such as quayside
cranes, automated guided vehicles for container handling
and yard cranes. Details of the architecture and operations
and sensing systems for smart ports are described. Commu-
nication standards for smart ports are discussed, and smart
ports implementation examples regarding structural health
monitoring are considered. Conclusions and future research
opportunities in the IoT era are addressed in the final section
of the paper.
IoT in Smart Ports
Internet of Things (IoT), as defined by the IEEE, is a network
of items including sensors and embedded systems which are
connected to the Internet and enable physical objects to gather
and exchange data [1]. As IoT rises into dominance, sensors
are playing a pivotal role in measuring the physical charac-
teristics of objects and converting them into numerical values,
which can be read by another device or by the user. In recent
years, the global sensor market has expanded year by year, and
it is expected to maintain high growth rates in the future. If we
look at the future-oriented projects of various governments,
such as Industry 4.0 of Germany and Made in China 2025 of
China, the key to these projects is the data provided by sensors.
Sensors are widely applied in different fields such as smart
power grids, smart buildings, smart industries, smart cities,
and smart ports.
A smart port may be defined as a fully automated port
where all devices are connected via the so-called IoT Smart
Port. A network of smart sensors and actuators, wireless
devices, and data centers make up the key infrastruc-
ture of the smart port, which allows the port authorities
to provide essential services in a faster and more efficient
manner. The major drivers in smart ports are productivity
and efficiency gains. Various sensors such as inertial sen-
sors, ultrasonic sensors, eddy current sensors, radar, lidar,
imaging sensors, and RFID readers and tags are used to col-
lect the required data in order to transform the “port” into
a “smart port.” Pratama et al. proposed a positioning and
obstacle avoidance algorithm for Automatic Guided Ve-
hicles (AGV) in a partially known environment based on
laser measurement systems and encoders [2]. Li and Xu
proposed a novel fusion positioning strategy for land vehi-
cles, which integrated the micro electromechanical-based
inertial measurement unit and virtual sensor, i.e., a sliding-
mode observer [3]. Kaloop et al. presented steel container
crane movement analysis and assessment based on struc-
tural health monitoring, in which accelerometers were used
to monitor the dynamic crane behavior, and a 3-D finite
element model was designed to express the static displace-
ment of the crane under the different loads [4]. Carullo and
Parvis presented an ultrasonic sensor to measure the dis-
tance from the ground to selected points of a motor vehicle
[5]. Fu et al. proposed a computer vision-based procedure
with image sensors to determine the position of one con-
tainer in the horizontal plane [6].
This research is supported by the National Natural Science Foundation of China, Project 6154004, Shanghai Science
and Technology Commission Project 14170501500, Shanghai Science and Technology Commission Project 16DZ2340400,
Shanghai Science and Technology Commission Project 17595810300 and Instituto de Telecomunicações, IT-IUL and
Fundação para Ciencia e Tecnologia Portugal.
February 2018 IEEE Instrumentation & Measurement Magazine 35
Port Architecture and Operation
An automated container terminal consists of the berthing
area at the quayside, the travelling area of AGVs and a stor-
age yard. More specifically, the berthing area is equipped with
quay cranes (QCs) for unloading and loading containers, and
the travelling area is used by AGVs to move containers from
the berthing area to the storage yard where the storage yard
stores import and export containers before further delivery by
trucks or trains. An automated container terminal mainly uti-
lizes equipment, such as QCs, AGVs, and yard cranes (YCs),
for the loading and unloading operation of containers. Thus,
the QCs are used to discharge containers from the ship to the
AGVs or for loading containers from AGVs to the ship. AGVs
implement the horizontal transportation between the shore
operation and yard operation, and YCs are in charge of put-
ting the containers in the corresponding locations in the yard.
An example of an automated container terminal is Xiamen
Ocean Gate, the first one in China, which has set the stan-
dard of a global automation terminal handling system. As
a novelty at Xiamen Ocean Gate, two Rail-mounted Gantry
Cranes (RMG) are deployed in the front and back of the yard.
The RMG gets the container from an AGV-mate and then un-
loads it into the storage area in the yard or gets the container
from the storage area and then unloads it onto a delivery truck.
A layout of a typical automated container terminal is illus-
trated in Fig. 1.
Generally speaking, the container terminal operation
mode can be divided into two processes: loading process and
unloading process. Fig. 2a shows the concrete operating pro-
cedure of loading and unloading a ship. The AGV receives
discharge instructions (loading instructions) and then puts the
container, which was unloaded by QCs, on the yard blocks. In
the unloading procedure: the main trolley first gets the con-
tainer from the ship and puts it onto the transfer platform
on the QC; then the portal trolley gets the container from the
transfer platform and unloads it to the AGV; finally, the AGV
puts the container on the AGV-mate in front of the block of the
yard by horizontal transport. The AGV receives the next task
Fig. 1. The layout of an automated container terminal.
Fig. 2. Automated port terminal operations: a) loading and unloading the ship, b) AGV container operation between quayside and storage yard [7].
36 IEEE Instrumentation & Measurement Magazine February 2018
instruction at the same time, so it will move on to do the next
mission. The operation sequence is shown in Fig. 2b [7]. The
use of an AGV-mate reduces the waiting time of the operation
of AGVs and improves the efficiency of the actual operation.
The AGV-mate has been widely used at the Xiamen Ocean
Gate automated container terminal and is considered part of
the implemented optimization process.
There are some new types of automatic equipment, such as
ZPMC's new gantry cranes, which were designed for container
ships with 24 transverse container rows. They have 74-me-
trejibs, weigh 2,400 tons and can handle a maximum payload
of 110 tons. They are very well-equipped to handle the first
18,000-TEU ships with their 23 container rows, which have
just gone into service. ZPMC's new QCs are over 138 m tall, the
speed of the main trolley arriving at 240 m/min, and the speed
of the gantry goes up to about 45 m/min. They weigh 1,850,000
kg each and can lift four 20-foot containers at one time, han-
dling up to 100,000 kg in one lift. They have a 69.5 m lifting
height and extended reach, capable of handling the 25 contain-
ers-wide new generation of ultra large container ships (ULCS).
Through the comparison and analysis between the tradi-
tional container terminal and automated container terminal
listed in Table 1, it is clear that the automated port possesses
many advantages and superiorities, especially in saving labor
costs, improving the operation efficiency and economic bene-
fits, reducing energy consumption, improving the level of safe
operation, and promoting the image of the port and even the
image of the city, etc. [8]. According to the estimations, the au-
tomated container terminal can save at least 25% more energy
and reduce 15% more carbon emissions than the traditional
terminal as a result of using power-driven vehicles. The whole
terminal's modules communicate with a central control unit of
the terminal control room. Therefore, the automated container
terminal has become the inevitable development trend of the
future, implementing technologies associated with distributed
smart sensors and actuators, data communication and Internet
connectivity for remote and automatic operating, control opti-
mization based on artificial intelligence, and big data analysis.
Sensing Systems for Smart Ports
The automatic container terminal implementation requires the
use of sensing systems that are employed in tasks such as the
structural health monitoring of the quayside cranes, container
position detection and handling, AGV localization, naviga-
tion and control, etc. The current developments in the field of
optical fiber sensors, highly sensitive magnetic sensors, and
MEMS inertial measurement units that enable the interoper-
able wireless protocols, including the latest developments of
Table 1 - The comparisons between the traditional and automated container terminal
Characteristics Port
Traditional Port Automated Port (e.g., Xiamen Port)
Operating subjects People and machines Automatic systems and equipment
Quayside operations Quayside cranes Semi-automatic/automatic
Quayside cranes
Horizontal transportation Container trucks
Straddle carriers
Container trucks
Straddle carriers
Automatic Guided Vehicles
Yard operations Rubber-tired gantry cranes Automatic rail mounted gantry cranes
Operation efficiency
Labor based operation
Limited efficiency
Low dispatching efficiency
Techniques/information based operation
High automation and intelligence
High and improvable efficiency
Intelligent and coordinated dispatching
Economic efficiency
Low construction costs
Low maintenance costs
High labor costs
High transportation costs
Low economic benefits
High construction costs
High maintenance costs
Low labor costs
Low transportation costs
High economic benefits
Security supervision and control
Low reliability
Slow response
High labor cost
High intelligence
High reliability
Fast response
More safety
Environmental protection High energy consumption
Heavy pollution
Sustainable development
Low energy consumption
Little pollution
Sustainability No Yes
February 2018 IEEE Instrumentation & Measurement Magazine 37
4G and 5G that will permit the extension of the Internet con-
nectivity of the sensing systems, represent big opportunities
for new developments in the field of smart ports. Thus, new
smart sensing architectures for container identification and
management, vehicle identification and management, loca-
tion and navigation services, and the safety of port terminals
equipment related to structural health monitoring are based
on contact and remote sensing solutions.
Basically, the objective of structural health monitoring
(SHM) is to ascertain if damage is present or not based on
measured dynamic or static characteristics of a system to
be monitored. In smart ports, the contents of SHM focus on
the identification of stress and strength reserve of the metal
structure in cranes and RMG and on flaws detection, such
as micro-cracks, weld cracks and plastic deformation, in the
process of structural failure where the stress under load is
obviously beyond the allowable value. The SHM must be im-
plemented during the entire life-cycle of the equipment. For
example, the oblique rods of QCs undertake significant load
and their installation symmetry must be ensured in the manu-
facturing stage with strain monitoring technology [9]. During
the operation and maintenance process, the health monitoring
of the bearing structure in the crane and RMG should be con-
tinuously executed.
The primary sensing technologies used for SHM in smart
ports are shown in Table 2. Among these technologies, the
strain gauge is the most popular sensing element. The gauge
translating the structural strain into resistance change is in-
expensive and stable. Fiber Bragg Grating (FBG) is used to
sense the influence of strain through a frequency shift and
magnitude change of the reflected beam and is a more ex-
pensive long-term monitoring technology than the strain
gauge. In addition, the eddy current probes and ultrasonic
probes are two on-line defect detection technologies. In the
electromagnetic field, cracks and other defects will affect
the size and shape of the eddy current in electric conductive
materials that are detected using eddy current probes that
include exclusively coils or coils and magnetic sensors such
as the giant magneto resistors [10]. For the ultrasonic sen-
sor, the presence of flaws in the uniform material will cause
the discontinuity of the material, which can cause the acous-
tic impedance to be inconsistent. With the reflection theorem,
it is known that the ultrasonic wave will be reflected at the
interface of two different acoustic impedances, and the mag-
nitude of the reflected energy is related to the difference in
the acoustic impedance of the interface, the orientation and
the size of the interface [11]. Therefore, a crack situation in the
metal structure of QCs and RMG can be inferred by analyz-
ing the signal measured by inductive eddy current sensors
and ultrasonic sensors.
The proximity, level and distance measurement sensors are
mainly for anti-collision monitoring and location applications
of cranes, RMG and AGV in smart ports, where the ultrasonic,
inductive, laser and IR sensors are the most common, as de-
tailed in Table 3.
Navigation sensors for AGV and unmanned container
trucks, which are the main container handling equipment
in smart ports from shore area to container yard, mainly in-
clude RFID-based navigation systems (HF and UHF RFID
solutions), differential GPS systems, laser-based navigation
systems, inertial navigation systems and encoders, compared
in Table 4 [12].
Camera and laser sensors are also used for container moni-
toring, operation safety, non-destructive testing and diagnosis
[13]. In smart ports, the transport and storage of containers
has to be under whole-process monitoring, where the identi-
fication of the container number can be achieved by camera,
the location of the container in an AGV or truck can be deter-
mined by a laser sensor (Fig. 3a), and the automated grab of the
container with the crane spreader and the automatic position
alignment between the AGV and container can also be granted
by camera and image processing (Fig. 3b).
Table 2 - Main sensing technologies used for structural health monitoring in smart ports
Type
Parameter
Relative
Cost Working Range Environmental Adaptability Applications
Strain gauge $Long-term monitor Sensitive to water, humidity and
electromagnetic interference
Stress and strength reserve of the
metal structure in cranes and
RMG
FBG $$$ Long-term monitor
Resistant to dust, water,
humidity, and electromagnetic
interference
Stress and strength reserve of the
metal structure in cranes and
RMG
Inductive eddy
current sensor $$ Short-time monitor
Resistant to dust, water, and
oil interference; Sensitive to
surface roughness, surface
coating and material
Flaws detection, such as micro-
cracks, weld cracks and plastic
deformation
Ultrasonic sensor $$ Short-time monitor
Sensitive to reflection problem,
noise with the same frequency
and cross problem
Flaws detection, such as micro-
cracks, weld cracks and plastic
deformation
38 IEEE Instrumentation & Measurement Magazine February 2018
Table 4 - The comparison between the navigation sensors in smart ports
Type
Parameter
Relative
Cost Accuracy Measurement
of the position Environmental adaptability
HF and UHF RFID solutions $$ High Absolute position Sensitive to foundation
settlement
Differential GPS systems $$$ Very high Absolute position Sensitive to metal and other
shelter
Laser- based navigation systems $$$ Very high Absolute position
Sensitive to dust, water,
humidity, oil, sun and
reflector interferences
Inertial navigation systems $$ High Relative position Sensitive to cumulative error,
vibration and slip
Encoders $ Low Relative position Sensitive to cumulative error,
vibration and slip
Table 3 - The comparison between the distance measurement sensors in smart ports
Type
Parameter
Relative
Cost Effective Range Environmental Adaptability Applications
Ultrasonic sensor $$ High
Sensitive to water, humidity
and wind interference; Long
response time
Anti-collision of quayside crane
and RMG in track
Laser and Lidar $$ Very high
Sensitive to dust, water and
oil interference; Very short
response time
Anti-collision of spreader, main
trolley, portal trolley, quayside
crane, AGV and RMG
Electromagnetic
induction sensor $Very low
Resistant to dust, water,
humidity, wind and oil
interference; Short response
time
Anti-collision of quayside crane
and RMG in track
Infrared radiation
sensor $ Low
Sensitive to sun and reflector
interference; Short response
time
Anti-collision of spreader, main
trolley and portal trolley in
quayside crane
Fig. 3. Container monitor with a) lidar setup, and b) camera system setup.
February 2018 IEEE Instrumentation & Measurement Magazine 39
Communication Standards for Smart Ports
In automated ports, the tendency to “go wireless” is increas-
ingly obvious due to its flexible-deployment advantage. But
there are still some challenging issues during the implementa-
tion of wireless transmission. The most challenging one is that
these wireless devices are prone to be affected by big metal
parts and high-power electrical appliances. To tackle this issue,
anti-jamming antenna technologies are developed, in which
antenna gains can be adjustable self-adaptively according to
the interference situation. With the development of anti-jam-
ming technologies, the application of wireless communication
will become wider and wider.
ZigBee protocol: ZigBee communication protocol is one of the
protocols with the highest applicability in wireless sensor net-
works. Due to its easy-deployment and ad-hoc features, it has
been widely applied as part of IoT [1] architectures. As far as
an automated port is concerned, the application of ZigBee or
WSN mainly focuses on structural health monitoring. In this
case the ZigBee nodes are equipped with a strain gauge sensor
and deployed on the crucial parts of the port equipment (e.g.,
yard crane). The ZigBee sensors transmit the sampling data to
the base station via multi-hops periodically. Since ZigBee pro-
tocol supports the ad-hoc paradigm, ZigBee sensors organize
into a network with each other automatically, significantly fa-
cilitating the deployment of the monitoring system and later
lowering the maintenance costs.
Wi-Fi protocol: Wi-Fi protocol seems to be the most welcom-
ing wireless protocol in the area of automated ports. Due to its
wide-coverage and broad-bandwidth advantages, Wi-Fi pro-
tocol is mainly used for video surveillance and AGV remote
control [14]. For instance, the camera deployed on the front of
a yard crane will deliver a continuous video flow to the surveil-
lance center via Wi-Fi. Through video recognition, the coding
information of containers can be easily known. It is worth not-
ing that to resist the signal interference from regular Wi-Fi (i.e.,
2.4 G UHF), the frequency of Wi-Fi in an automated port is al-
ways 5G SHF ISM.
RF long range data communication: RF long range data com-
munication is a point-to-point wireless communication
technology. The advantages of RF communication are twofold:
1) the wireless transmission of RF can achieve 5 km, much far-
ther than other wireless transmission solutions; and 2) due to
the point-to-point feature of RF, users communicate with each
other through RF terminals directly and without extra infra-
structure support. But the disadvantages of RF are also evident.
The anti-jamming ability of RF is poor, and data transmit speed
is only 9.6 kbps. In the automated port, RF is mainly applied in
the communication between ships and ports and among staff in
remote areas without extra infrastructure support.
4G and 5G solutions: As the fourth generation of mobile tele-
communications technology, the role of 4G in an automated
port tends to be more and more important. Compared with
Wi-Fi, it presents two advantages: 1) the cost of infrastructure
is lower since the infrastructure is often developed by national
operators; and 2) its coverage and flexibility is much better. Ex-
cept in some extreme cases, most areas can be covered by base
stations of 4G. Whether it is inside or outside the port area,
4G can provide sustainable transmission service. Therefore,
most truck-carried terminals are equipped with 4G modules.
Since the bandwidth of 4G is relatively limited, transmitting
high-resolution video stream is still a challenging task. But the
fast development of 5G technology, “tactile Internet technol-
ogy” with broader bandwidth, can make up this performance
shortcoming.
Table 5 presents a comparison list of mainstream wireless
technologies and relevant applications involved in an auto-
mated port.
Implementation of Smart Port Sensing
Systems
In this section, details are provided about the identification,
localization, tracking and quayside crane health structure mon-
itoring applications in smart ports. Identification of stress and
the strength reserve of the metal structure in cranes and RMG is
a very important aspect of SHM. Yao et al. developed a portable
wireless strain monitoring system with some IoT technologies
such as ZigBee and WSN (Fig. 4a) [9]. This system combines
self-organization, self-recovery and low power consumption
and aims to improve the monitoring efficiency and meet the se-
rious challenges within SHM of quayside cranes (Fig. 4b).
Table 5 - Wireless communication technologies comparison
Wireless Technologies ZigBee Wi-Fi RF 4G
Popularity + +++ ++ +
Speed 250 kbps 300 Mbps 9.6 kbps 100 Mbps/1 Gbps
Relative Cost $ $$ $$ $$
Frequency 784 MHz 2.4 GHz/5GHz 433 MHz 1700-2100 MHz,
2500-2700 MHz
Range (outdoor) 100 m 100 m 20 km -
Public Access - + + ++
Compatibility IEEE 802.15.4 IEEE 802.11 ac/n 802.11 ac LTE
40 IEEE Instrumentation & Measurement Magazine February 2018
Computer Vision Application for
Container Identification
Serial numbers are the basic container information for man-
agement in terminals, yards and customs (Fig. 5a). Computer
vision can be applied for automatic identification of the
container number and to collect this information as part of
automatic real-time logistics, solving in this way the critical
issue of container automated management as was imple-
mented in some Chinese ports [15]. Using this vision system
can provide automatic identification of the container num-
ber, seal integrity, the state of the door handle and the lock
and door orientation. The system architecture is shown in
Fig. 5b. The packing position and door location information
are also important for the automatic transport of containers
which have been monitored by the camera and laser sensor.
This system provides the packing position and door location
information of containers on AGV or trucks for transport op-
erations in port, which can facilitate automated management,
reduce manpower and improve the container operation
efficiency.
Finally, an example of AGV navigation based on RFID is
detailed. In this application, an RFID tag array is installed on
the ground of an AGV operating area, and the AGV obtains
the geographic location information stored in the RFID tags
through two readers, which are respectively installed in the
front and back of the AGV, as shown in Fig. 6. Thousands of
RFID tags working in 13.56 MHz band with 1 ms reading cycle
are used for AGV positioning and AGV path definition using
matrix RFID tags in cells 2 m x 2 m. A drawback of this tech-
nology is that the foundation settlement caused by heavy duty
conditions can lead to the RFID reading failure and the possi-
bility of collision between the AGV during the transportation
process. Besides AGV navigation, this RFID tag is also used for
the localization and anti-collision of the quayside cranes.
Conclusions
Currently, smart ports represent a reality where automatic con-
tainer terminals deploy smart sensing systems to improve the
performance associated with different terminal tasks. Modern
remote sensing technologies such as RFID for identification
Fig. 4. Wireless Sensor Network for strain monitoring on a quayside crane: a) the wireless sensor network kit for strain monitoring, b) the distribution of the
WSN nodes on the level of the metallic structures [9].
February 2018 IEEE Instrumentation & Measurement Magazine 41
and localization and cameras and embedded computer vision
algorithms may contribute to safer and reduced time handling
operations compared to classical container terminals. Safety
conditions are improved using several possible solutions for
structural health structure monitoring for quayside cranes.
Robust wireless networking and Internet connectivity present
important challenges in the port terminal scenario, consider-
ing the metallic obstacles present. Different communication
solutions that are currently used in automated terminal were
mentioned, giving special attention to the ZigBee WSN for
strain monitoring. Besides the sensing solutions that we pre-
sented that currently are part of the optimization of container
Fig. 5. Automatic identification of container in a smart port: a) container ID and camera system, b) distributed container identification system.
42 IEEE Instrumentation & Measurement Magazine February 2018
handling activities in smart ports, we are convinced that smart
sensing and IoT technologies together will play an important
role today and in future port development.
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Yongsheng Yang received the Ph.D. degree from Nanjing Uni-
versity of Aeronautics and Astronautics, Nanjing, China. He
is currently a Professor in the Logistics and Engineering Col-
lege, Shanghai Maritime University, China. He is Director of
the Science and Technology Department, China Mechanical
Engineering Society.
Fig. 6. RFID application for AGV navigation in a smart port: a) 13.56 MHz RFID
tag, b) RFID tag and RFID readers distribution.
February 2018 IEEE Instrumentation & Measurement Magazine 43
Meisu Zhong (zhongmeisu@163.com) is currently pursuing
the Ph.D. degree at the Institute of Logistics Science and Engi-
neering, Shanghai Maritime University, Shanghai, China. She
received her master's degree in the field of engineering and
management of logistics.
Haiqing Yao (haiqing8612@163.com) received the Ph.D.
degree from the East China University of Science and Technol-
ogy, Shanghai, China. He is a Post-doctoral Researcher at the
Shanghai Maritime University in the Institute of Logistics Sci-
ence and Engineering.
Fang Yu (fangyu1985@hotmail.com) completed post-doctoral
work at Kobe University in Japan. She is Assistant Professor in
the Institute of Logistics Science and Engineering of Shanghai
Maritime University, Shanghai, China.
Xiuwen Fu (fuxiuwen1987@163.com) received the Ph.D. de-
gree from Wuhan University of Technology in China. He is
Assistant Professor in the Institute of Logistics Science and En-
gineering of Shanghai Maritime University, Shanghai, China.
Octavian Postolache (opostolache@lx.it.pt) received the Ph.D.
degree from the Technical University of Iasi and Habilitation
from Universidade de Lisboa, Portugal. He is currently Senior
Researcher at the Instituto de Telecomunicacões, Lisboa, and
Professor at Instituto Universitário de Lisboa (ISCTE-IUL), Gen-
eral Chair of IEEE IMS Portugal Chapter and Chair of IMS TC-13.
... On the other hand, Nita & Mihailescu (2017) stated, the integration of big data and internet of things technologies might help decision-makers and managers to make efficient future decisions. In addition to this, Yang et al. (2018) evaluated different perspectives on the internet of things. They explained the internet of things applications on automated ports. ...
... In the maritime sector, it is significant to reveal the prioritisation level of the maritime business components Table 3. On the other hand, components of the maritime sector and its short definitions, which are also alternatives of the study, were given in Table 4. Components of the maritime business were taken from expert opinions and literature (Takahashi, 2016;Stanić et al., 2018;Yang et al., 2018). ...
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Industry 4.0 technology has affected almost every sector in the world. Maritime sector is one of them that were affected by this technology. In this study, components of the maritime sector were prioritized to find out which of them should comply with this transformation primarily. Many different criteria were taken into consideration for the solution of such problems. Therefore, multi-criteria decision-making (MCDM) methods required to solve this problem. Fuzzy AHP (Analytic Hierarchy Process) and VIKOR (VlseKriterijumska Optimizacijia I Kompromisno Resenje) hybrid method was employed for revealing the prioritization ranking for maritime sector components. A group of experts assessed and compared 22 criteria and scored them for each alternative. Proposed methodology was employed through the experts’ assessments. Results were displayed and suggestions were given for the further studies
... new technologies, senses new information, incorporates new functions, realizes intelligent upgrading, green transformation, integration and innovation, creates an infrastructure system that meets the requirements of high-quality port development, strongly promotes the transformation of the new and old dynamic energy of the port and high-quality development, and realizes the digital transformation of the port and intelligent upgrading [2]. With the guidance of national policies and the rapid development of ports, the construction of automated ports and intelligent port information systems is becoming increasingly important. ...
... Taewon et al. designed various types of evaluation systems for smart ports [5,[11][12][13]. In contrast to the establishment scheme for smart port evaluation systems, the factors influencing the performance of port information system construction are complex and nonlinear, including new technologies, port conditions, green security, the social environment, and other factors [2]. The current literature on smart port index design and evaluation mostly focuses on smart port infrastructure construction [12] and competitiveness [14]. ...
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With the continuous development of smart ports, the construction of port information systems has become the construction focus of the future development of such ports. However, the existing performance evaluation system for port information systems represents a research gap. It cannot effectively achieve specific improvement in the construction of information systems for port services and capabilities. In this paper, an evaluation index system was forged from the three perspectives of the operating level, management capability, and economic efficiency. As a first step, starting from the current situation of Beibu Gulf Port (Port B) construction, the three dimensions of the evaluation index system were determined using the scheme of literature review, questionnaire research, and fieldwork. Additionally, through adversarial interpretive structural modeling (AISM), it is concluded that the port loading and unloading operation capability and service level are the fundamental factors for measuring the construction performance of the container terminal operation system (CTOS). The results are used as input to construct an analytic network process (ANP) model to obtain the index weights. Finally, the gray clustering method (GCM) is introduced to construct a quantitative evaluation model to quantitatively evaluate the construction performance of the Port B CTOS. The quantitative benefits brought by the construction of the CTOS are finally verified. The scores of the corresponding dimensions of the port before and after the construction of the CTOS system in Beibu Gulf Port are obtained experimentally. The conclusion shows that the construction of CTOS makes the port operating level module improve the most, from 40.023 to 70.733 points; at the same time, it is found that two aspects, i.e., green security and economic benefits, in current construction are directions requiring further work in future port construction. Finally, a visualized quantitative analysis and evaluation method for the performance of smart port construction is proposed.
... Nonetheless, IoT-based solutions have a broad range of applications in marine environments, as seen in Fig. 7. For instance, consider a smart port, which enables authorities to provide their customers with more reliable information and innovative services [125]. Also, maritime IoT applications can serve in other situations like weather prediction, pollution control, and oil platform monitoring [126], [127]. ...
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Water covers 71% of the Earth’s surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and space networks. This paper presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. The paper first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, the paper presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.
... C. Specific Application Scenarios 1) Smart Port: How ASVs will berth and maneuver around densely trafficked ports has gradually become an imminent problem for modern maritime security and management. Existing work has already applied the latest technologies to smart ports [225]. There are many computer vision tasks in port, such as container identification, image and object recognition, situational awareness, monitoring and surveillance [223]. ...
Preprint
Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error. Compared to software development for other autonomous vehicles, maritime software development, especially on aging but still functional fleets, is described as being in a very early and emerging phase. This introduces very large challenges and opportunities for researchers and engineers to develop maritime autonomous systems. Recent progress in sensor and communication technology has introduced the use of autonomous surface vehicles (ASVs) in applications such as coastline surveillance, oceanographic observation, multi-vehicle cooperation, and search and rescue missions. Advanced artificial intelligence technology, especially deep learning (DL) methods that conduct nonlinear mapping with self-learning representations, has brought the concept of full autonomy one step closer to reality. This paper surveys the existing work regarding the implementation of DL methods in ASV-related fields. First, the scope of this work is described after reviewing surveys on ASV developments and technologies, which draws attention to the research gap between DL and maritime operations. Then, DL-based navigation, guidance, control (NGC) systems and cooperative operations, are presented. Finally, this survey is completed by highlighting the current challenges and future research directions.
... The more objects, the smarter the environment could perform (Pradhan et al., 2017;Asghari et al., 2019). In this respect, products, goods, cars, trucks, industrial equipment, industries (electricity, telephone, etc.), sensors, and other components are combined daily through Internet connections and powerful data analysis features to transform our lives and performances (Tiwary et al., 2018;Yang et al., 2018). Projects based on IoT have had a striking influence on the Internet and the economy in a way that the forecasts show that by 2025, more than 30 billion devices will be connected to the Internet (Symanovich, 2019). ...
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The global demands for clean and sustainable energy are rapidly increasing because of population and economic growth. The future of energy essentially requires novel thinking and new systems to transform energy generation, distribution, and consumption. The Internet of Energy (IoE), as a new concept, transforms the way of energy production, supply, and consumption to fulfill high-energy demands via a smart network of industrial energy producers and consumers. The main objective of this paper is to address how the Internet of Things (IoT) would meet the requirements of smart and distributed power generation. We did a comprehensive literature review to provide insights into the IoE applications and enlighten the current challenges. Furthermore, the paper provides deep insights into the existing research challenges to address the current limitations of the IoE security issues, and potential directions are also pointed for future work. The findings of this study include identifying the requirements and enabler factors influencing the IoT-based distributed generation that would be useful for policymakers and decision-makers in the field.
... Ports are a national critical infrastructure, key for international trading, supply chain and the overall sustenance of a country's economy. The digitalisation of industrial processes brought about by Industry 4.0 has seen the port operation and management being aided by a network of smart sensors, actuators, communication devices, data centres, and decision support systems (Yang et al., 2018). The port's efficiency is often influenced by the availability and condition of the QCs that moves containers between the shore and shipping vessels. ...
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Infrastructure systems in today's increasingly interconnected world employ the capabilities of the Internet of Things (IoT) technologies for their monitoring, operational control, and asset management. IoT devices can be defined as sensors (of different types) collecting, processing, and sharing time series of data. The analysis of such data often face challenges as a consequence of the high frequency of data collection and the increasing number of sensors placed on infrastructure. Power related issues, timestamp misalignment, and heterogeneous sampling designs are among the most common issues that the IoT data collection may suffer alongside the inherent complexities of large scale databases. This paper provides an overview of time series mining techniques adapted to tackle such issues in IoT data. The aim is to have a pattern recognition tool-set for developing anomaly detection algorithms. Particularly, the paper investigates how to efficiently handle large-scale time series coming from multiple sensors in a stream and following an unevenly spaced - irregular - sampling. The analysis is demonstrated through a case study of time series data mining of sensors installed for supporting the predictive maintenance of quay-cranes at the Port of Felixstowe, the largest container port in Britain.
... BIM-based technologies are widely adopted for the maintenance and operation management of ports (Valdepeñas et al., 2020), by which operation and maintenance information can be conserved in a database established through BIM, which is convenient for port engineers to formulate daily management plans visually. Using IoT (Yang et al., 2018), combined with cloud computing and GIS, an online platform called 'SamrtPort' ...
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The transportation infrastructure (TI) is a vital link for and critical component of societal and economic development. A new area, called intelligent construction for transportation infrastructure (IC/TI), is emerging with the integration of traditional TI construction and new technologies, including artificial intelligence (AI), big data, virtual reality (VR), remote sensing, building information modeling (BIM), digital twins (DTs), and the internet of things (IoT). This paper reviews the research in the area of IC/TI published since 2017. A total of 191 journal articles in the area of IC/TI were obtained from the Web of Science database and reviewed, including 23 review articles and 168 research articles. This paper aims to provide an up-to-date literature review of IC/TI to further facilitate research and applications in this domain. Based on the results of this review, current research trends, applications, technologies, research gaps, and future needs are discussed.
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In recent years, the application and development of the Internet of Things (IoT) has increased significantly. In general, the IoT requires many technologies. Usually, a large amount of IoT sensor information is collected through IoT devices, and devices are connected to each other through IoT communication technology. The Internet exchanges and transmits information, issues instructions and commands to the device, and then conducts prediction and decision making by artificial intelligence (AI) after big data analysis, which is called the AI of Things ( AIoT ) system.
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The concept of smart port is still disputed in the literature, appearing in dispersed contributions, often not aligned with its intelligent systems background. In this sense, this paper proposes a theoretical framework for smart port terminals by presenting a literature review and conceptual definition, proposing a maturity model, and exploring a research agenda. We investigate defining characteristics and relations between enablers, applications, and outcomes for smart port terminals. The proposed maturity model evaluates these main aspects in six areas of interest, providing maturity indexes that enable identifying the current state of intelligence in a port terminal, hence aiding its improvement. The application of the maturity model in two Brazilian container terminals provided feedback to the theoretical proposition and supports its use as a tool for general assessment of smart port terminal maturity. As such, the paper can serve as a comprehensive guide for researchers and port managers to understand the field, fostering the development of roadmaps and smart applications in port terminals.
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This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error. © 2016 Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg
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Automatic container code recognition from a captured image is used for tracking and monitoring containers, but often fails when the code is not captured clearly. In this paper, we increase the accuracy of container code recognition using multiple views. A character-level integration method combines recognized codes from different single views to generate a new code. A decision-level integration selects the most probable results from the codes from single views and the new integrated code. The experiment confirmed that the proposed integration works successfully. The recognition from single views achieved an accuracy of around 70% for the test images collected on a working pier, whereas the proposed integration method showed an accuracy of 96%.
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Intelligent Autonomous Vehicles (IAVs) constitute one of the component systems of Intelligent Transportation System (ITS) that can operate in confined private spaces, as well as in open and public spaces. The seaports or container terminals are one of the important confined spaces that have attracted extensive research interests over the last decade in the use of information communication technology to improve the operation of ITS. The main goal of research works undertaken so far in this area was to improving the efficiency and cost-effectiveness of the indoor traffic, by transporting optimally and sustainably freight from ship to the logistics and unloading areas. The use of a team of IAVs with wireless communication capabilities by rearranging efficiently all operations of handling, routing,…is a strategic objective for seaport authorities and their customers. In this paper, we consider inter-Vehicles communication system in which IAVs can communicate and cooperate to avoid collision problem in the predetermined intersection areas in the yard. We investigate the performance of our solution through simulations using Omnet++/Veins Simulation framework. We show that the implemented cooperation mechanism can significantly reduce the unloading time in the seaport.
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As presented in [1], the fourth industrial revolution is coming: one which promises to marry the worlds of production and network connectivity in an Internet of Things (IOT). "Smart production" becomes the norm in a world where intelligent information and communication technology or ICT-based machines, systems and networks are capable of independently exchanging and responding to information to manage industrial production processes. Smart industry refers to the technological evolution from embedded systems to cyber-physical systems. The deployment of cyber- physical systems in production systems creates the smart factory. In this context, it is expected that smart factories, smart products, smart materials and smart machines are characterized by cyber-physical systems. There is a need to understand smart products in this context.
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How to achieve reliable and accurate positioning performance using low-cost sensors is one of the main challenges for land vehicles. This paper proposes a novel fusion positioning strategy for land vehicles in GPS-denied environments, which enhances the positioning performance simultaneously from the sensor and methodology levels. It integrates multiple complementary low-cost sensors not only incorporating GPS and MEMS-based IMU, but also a “virtual” sensor, i.e., a sliding mode observer (SMO). The SMO is first synthesized based on nonlinear vehicle dynamics model to estimate vehicle state information robustly. Then, a federated Kalman filter (FKF) is designed to fuse all sensor information, which can easily isolate and accommodate such sensor failures as GPS ones due to its decentralized filtering architecture. Further, a hybrid global estimator (HGE) is constructed by augmenting the FKF with a grey predictor (GP), which has the advantages of dealing with the systems with uncertain or insufficient information. The HGE works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outage to realize accurate and reliable positioning. The experimental results validate the effectiveness and reliability of the proposed strategy.
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With the development of container port automation, the automated vision systems for containers have been widely used in automated ports. This paper presents a rapid automated vision system for container corner casting recognition. The histograms of oriented gradients (HOG) descriptors are used to preprocess the image of the container and the vectors of HOG are then built. A trained support vector machine (SVM) classifier is applied to recognize the right corner casting of the container. At last, through symmetry, a flipping mirror algorithm is used for quick left corner casting recognition. The experimental results show this algorithm scans and detect the two corner castings of the container almost twice as fast as the traditional algorithms.
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In the health monitoring of large metal structure, the traditional strain measurement systems with wired and bulky instruments face the challenges of harsh environment, changing network topology and limited energy supply. To meet these requirements and improve the efficiency, a portable wireless strain monitoring system combined with wireless sensor network technology and portable design has been developed in this paper. Consisting of handheld coordinator, router and sensor, this novel system takes miniaturization, portability and low-power design as core capabilities in both software and hardware design. With tests in laboratory environment, its time domain response and power consumption have been analyzed to verify its performance and efficiency. Additionally, after calibration on an equal strength cantilever, comparative tests on quayside cranes have been explored to show that this system has an equivalent accuracy compared with the traditional measurement system. However, it improves the work efficiency and can meet the serious challenges within health monitoring of large metal structure.
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Automation is a trend for large container terminals nowadays, and container positioning techniques are key factor in the automating process. Vision based positioning techniques are inexpensive and rather accurate in nature, while the effect with insufficient illumination is left in question. This paper proposed a vision-based procedure with image sensors to determine the position of one container in the horizontal plane. The points found by the edge detection operator are clustered, and only the peak points in the parameter space of the Hough transformation is selected, in order that the effect of noises could be much decreased. The effectiveness of our procedure is verified in experiments, in which the efficiency of the procedure is also investigated.
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The usage of eddy current probes (ECP) with a single magnetic field sensor represents a common solution for defect detection in conductive specimens but it is a time consuming procedure that requires huge amount of scanning steps when large surface specimens are to be inspected. In order to speed-up the nondestructive testing procedure, eddy current probes including a single excitation coil and an array of sensing coils present a good solution. The solution investigated in this paper replaces the sensing coils for giant magneto-resistors (GMRs), due to their high sensitivity and frequency broadband response. Thus, the ECP excitation coil can be driven at lower frequencies than the traditional ones allowing defects to be detected in thicker structures.
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Container terminals are continuously facing the challenge of strong competition between ports. In this study, we present the terminal of Normandy-Le Havre port. We consider a mixed integer programming problem for the problem of assigning optimal delivery tasks to lifting vehicles. There are a lot of algorithms designed for this problem such as B&B method, cutting plane,... By using an exact penalty technique we treat this problem as a DC programme in the context of continuous optimisation. Further, we combine DCA with the classical Branch and Bound method for finding global solutions.