ArticlePDF Available

Abstract and Figures

A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers.
Content may be subject to copyright.
sensors
Article
Smart Parking System with Dynamic Pricing,
Edge-Cloud Computing and LoRa
Victor Kathan Sarker 1,* , Tuan Nguyen Gia 1, Imed Ben Dhaou 1,2,3 and Tomi Westerlund 1
1Department of Future Technologies, University of Turku, 20500 Turku, Finland; tunggi@utu.fi (T.N.G.);
imed.bendhaou@utu.fi (I.B.D.); tovewe@utu.fi (T.W.)
2Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56453-2865,
Saudi Arabia
3Department of Technology, ISIMM, University of Monastir, Monastir 5000, Tunisia
*Correspondence: vikasar@utu.fi
Received: 9 July 2020; Accepted: 14 August 2020; Published: 19 August 2020


Abstract:
A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty
in the management of traffic in cities. One of the most significant reasons for increased traffic jams on
the road is random parking in unauthorized and non-permitted places. In addition, managing of
available parking places cannot achieve the expected reduction in traffic congestion related problems
due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance.
As the number of roads, highways and related resources has not increased significantly, a rising
need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use
of multiple sensors, appropriate communication network and advanced processing capabilities
of edge and cloud computing, a smart parking system can help manage parking effectively and
make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart
parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power
wireless communication technology and Edge-Cloud computation. The proposed system enables
dynamic management of parking for large areas while providing useful information to the drivers
about available parking locations and related services through near real-time monitoring of vehicles.
Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the
parking authority and optimum parking slot availability for the drivers.
Keywords:
sensor node; architecture; IoT; LoRa; edge; cloud; energy-efficient; smart; parking;
dynamic pricing; management; vehicle
1. Introduction
An increasing number of vehicles has become a problem lately and it is drawing significant
concerns worldwide. Congestion of traffic is causing problems for the authorities in properly managing
roads and highways to ensure timely trips; especially in developing countries, as the infrastructure is
not as technologically advanced as in developed ones. Traffic jams are marked as critical limitations
towards development [
1
]. In addition, cities and countries with large populations suffer even more as
the scarcity of available roads and sideways are even worsened by improper and random parking in
unexpected places. Furthermore, traffic jams due to lack of parking places and sporadic standing of
vehicles on the road at unplanned areas causes significant stress and frustration [
2
]. This can trigger
aggressive driving behavior to compensate for the lost time, which in turn, affects road safety [
3
].
For instance, studies reported in Norway [4] reveal that parking-linked accidents account for 2.4% of
the total injuries in the country.
Sensors 2020,20, 4669; doi:10.3390/s20174669 www.mdpi.com/journal/sensors
Sensors 2020,20, 4669 2 of 22
Drivers who want to lawfully park their vehicles are left in a helpless situation as they have
to spend longer times to find an available parking place when visiting, often ending up in a late
arrival or unnecessarily early departure if they want to reach their destination in time. Lack of
information about available spots in advance causes delays in parking and sometimes it is impossible
to find one. A surprisingly large number of working-hours is wasted due to traffic jams which
hinders economic growth [
5
]. Moreover, while stuck in the traffic, the vehicles waiting on the road
burn fuel unnecessarily resulting in an increase in greenhouse gases such as carbon-monoxide and
carbon-di-oxide [
6
]. Smart parking is part of the solution in reducing air pollution and such systems
play an indisputable role in the vision of smart cities worldwide [7].
The rise of Internet of Things (IoT) is a strong enabler for smart cities. IoT has facilitated real-time
monitoring and control with the power of connectivity, information exchange and intelligent processing
of data. In addition, through the evolution of IoT, advanced technologies such as machine learning,
sensor fusion and real-time analytics are now integrated in it. In the case of the vehicle parking problem,
an IoT-based system can play a significant role. By collecting real-time data about vehicles and
parking slots, it is possible to build a smart system which manages the allocation of parking places,
provides real-time monitoring data to the authorities and informs the drivers about availability of
nearby parking places [8].
Keeping in mind the aforementioned aspects, in this paper, we propose a smart solution
for simplified, reliable and easily manageable vehicle parking system as depicted in Figure 1.
We incorporate sensor nodes comprising of multiple sensors which allow us to detect vehicles and
to acquire contextual and environmental information. In addition, we implement edge computation
capable gateway. Edge computing brings Cloud-like computational resources closer to the source of
data [
9
]. By harnessing the advantages of an Edge computing architecture, the network load is reduced
to the minimum, lowering the required number of gateways that must be placed across a certain area.
To create a reliable and secure network for the parking system, we use the nRF [
10
] and LoRa [
11
]
wireless communication technologies which are optimal considering the amount of transferable data
and the use of network bandwidth. These technologies also reduce the installation costs significantly,
as compared to traditional approaches that use Wi-Fi of Bluetooth Low Energy (BLE) communication
technology which require a much larger number of gateways. We provide a proof of concept with a
specific realization to demonstrate the viability and usability of the proposed smart parking system.
Furthermore, we present a novel algorithm for dynamically setting parking fees for maximum revenue
by optimally balancing between available parking spots and parking requests while ensuring the
minimum parking fee is always collected. We perform experiments both in a real-life parking area and
simulate algorithm for evaluating the performance, analyze the results and discuss possible scopes
of improvement. The main novelty is at a theoretical level providing the following:
A layered architecture for smart parking system consisting of occupancy detection sensor nodes,
LoRa communication and Edge-Cloud computing.
Implementation of low-cost, energy-efficient and secure parking sensor nodes with real-time
multi-parametric measurements.
A novel algorithm for dynamically determining parking fee for maximum revenue.
The rest of the paper is organized as follows: Section 2goes through existing related works on
smart vehicle parking management systems and motivation for this work, Section 3presents our
proposed multi-layer architecture, Section 4presents our dynamic pricing algorithm, discusses features
and Edge services provided by our system, and points out design considerations. Section 5exhibits
the proof-of-concept primarily focusing on the lowest hierarchical levels, Section 6explains the
experimental setup and obtained results. Finally, Section 7concludes this paper and provides direction
towards future works.
Sensors 2020,20, 4669 3 of 22
Figure 1.
Use case of proposed system in a typical parking scenario. The nRF and LoRa communication
are shown in blue and yellow, respectively.
2. Related Work and Motivation
Parking management is a common challenge when battling with the problem of traffic
mismanagement and congestion. Several works have been performed to alleviate the difficulty
in managing parking of vehicles.
P. Sadhukhan [
12
] implemented an IoT-based e-Parking system to address real-time detection
of available parking slots using a meter which enables automatic collection of parking charges
from the users. A camera is used to recognize a vehicle by reading the number plate and
extracting the registration number. The meters connect via Wi-Fi to a nearest laptop acting
as a local server. The laptop is connected to the Cloud where a central parking management
software runs. The server sends short messages (SMS) to parking area operator and users about
various parking related information through a serial port-based GSM module. Although the
automatic system enables convenient parking for drivers and authorities; however, there are
several limitations. The use of camera and Wi-Fi communication in each meter is expensive and causes
high energy consumption.
Besides, the micro-controller (MCU)
interfacing the camera is inadequate
for on-board image processing. Moreover, the authors did not present any fail-safe mode of operation
when the system loses connectivity.
In another work, Aydin et al. [
13
] presented an IoT-based platform by using genetic optimization
technique for smart cities based on navigation and reservation. A device consists of an MCU,
a magnetic sensor, ZigBee communication module and a battery. It connects to a gateway
which forwards real-time parking space availability data to the Internet via GPRS. While using
a genetic algorithm in the Cloud to find nearest parking place is quite innovative, the system
has severe shortcomings. For example, using only a magnetic sensor is not enough to accurately
detect presence of a vehicle. The system does not authenticate vehicles for parking rights.
Besides, as road lengths differ dramatically, placing one gateway per street would not provide full
coverage. In some cases, it can result in inefficient placement of gateways, i.e., too many gateways in
close proximity. Moreover, there is no inter-gateway information exchange without first reaching the
Internet. Therefore, an interruption in the network could end up in data loss and error.
Sensors 2020,20, 4669 4 of 22
Managed parking systems often have automated parking fee collection and billing which allow
time-based booking. Hainalkar et al. [
14
] presented a smart parking pre-and-post-reservation billing
service. The system consists of a control unit and a local server unit. The control unit has an infrared
(IR) sensor, radio frequency identification (RFID) reader and an LCD display. The first type of control
unit uses a MCU while the other one employs Raspberry Pi as the controller. Connected through
Google cloud service, another Raspberry Pi acts as a local server. The parking slot availability and
its location data are accessible from an Android application. However, the system is not scalable as
each local control unit and server uses one single-board computer making it costly when the number
of the parking slots increases. Besides, IR sensor cannot reliably ensure presence of a valid vehicle.
Furthermore, the system does not provide any monitoring service to the authorities.
Grodi et al. [
15
] proposed a wireless sensor network (WSN)-based parking space monitoring
and visualization system. It has a 3-layer hierarchy: an XBee-based WSN among sensor nodes,
a local Internet gateway, and a cloud database with a Node.js-based server. The authors provided a
mobile application for viewing real-time parking occupancy. However, with no direct connection to
the gateway, performance and response time can degrade in a large parking area due to multi-hop
communication. Besides, use of ultra-sonic sensors is misleading, for example, a cat can trigger a
false feed that a parking spot is occupied. Ramaswamy et al. [
16
] proposed a parking system with
Raspberry Pi, camera and ultra-sonic sensor to reduce greenhouse emission. However, it is not
feasible for large parking areas and the detection performance is limited. Similarly, Wang et al. [
17
]
presented an optimal path guidance for the drivers for shorter access time to the nearest parking slot.
However, use of
one sensor type for detecting vehicle’s presence is insufficient. Besides, GSM
connection is costly, requires maintenance and network coverage might not be available everywhere.
Kanteti et al. [
18
] presented a system for commercial areas in smart cities focusing on improved
algorithm for parking slot management using Raspberry Pi, passive IR sensors, and IP cameras.
It detects the vehicle registration number at the parking entrance and sends it to a database in
the Cloud. Afterwards, billing starts and the user is notified by SMS. While it finds the nearest available
parking slot, PIR sensor-based detection is deficient. Besides, camera-based authentication can induce
legal problems and might be impractical due to weather conditions such as heavy snowfall or fog.
In addition, large-scale deployment is costly due to use of camera and Raspberry Pi.
Teller et al. [19]
developed a framework named SParkSys which forms a camera sensor network for effectively
recognizing the vehicles with image processing in Raspberry Pi. However, it is expensive, and a
failure in the central node or a network connectivity problem can lead to a complete system stall.
Differences in usage, weather, authority, local regulations, number of users, and related services
make it difficult to choose a specific type of sensor for detection. Besides, there are inherent challenges
when implementing IoT-based systems in terms of manageability and deployment of sensor nodes [
20
].
Pham et al. [
21
] proposed an architecture to unify software services for parking management based
on GS1 standard for prototypes deployed in different places. While it is scalable, the sampling rate
of the sensor node is quite high for securely transferring real-time information to the authorities.
Silar et al. [
22
] illustrated a similar system for large-scale deployment in smart cities. They analyzed
variation in occupancy and claimed good system performance when multiple detectors are placed
on road-side parking places. However, the system suffers from high-latency sampling when sending
data to server, i.e., the system registers vacancy long after a vehicle has left the parking slot. A detailed
survey by Hassounen et al. [
23
] investigated several parking systems and detailed on the sensor
and communication technologies used. In particular, they observed advantages such as higher
availability, cost reduction, efficient billing, reduced traffic congestion, fast system response, helpful
services and effective management. For vehicle detection, mostly IR sensors, ultrasonic sensors,
RFID tags and simple light sensors are used. More expensive systems use IP-based and CMOS
cameras for authenticating and validating vehicles, especially at the entrance and exits of a confined
parking area. MCU-based sensor nodes are common; however, Raspberry Pi and similar single board
computers are used for image processing-based authorization. Wi-Fi and ZigBee communication
Sensors 2020,20, 4669 5 of 22
are mostly used for networking among the sensor nodes and intermediate gateways. In contrast,
GSM-based communication is preferred when sending data to Cloud from individual sensor nodes
and the gateways.
While the existing systems use different methods and sensing techniques, their limited
functionality cannot meet today’s parking needs. Table 1enlists features and compares aforementioned
works with our proposed system. Implementing multi-parametric sensor nodes in a parking
system ensures higher accuracy of occupancy, real-time information and an opportunity for
vehicle classification. While making the system less expensive, it also greatly reduces computational
and energy requirements than when using a camera. This is crucial, especially in developing countries
where cost-effective and comprehensive traffic management is preferred. Such solutions aid to decrease
greenhouse emissions caused by unnecessary extra driving while searching for a parking space.
Table 1. Comparison of earlier and related parking systems.
System/ Vehicle Edge/Cloud Connection Coverage Estimated Operational System
Author Sensing Support Medium Area Cost Run-Time Scalability
P. Sadhukhan [12] Camera, Ultrasonic Edge Wi-Fi, GSM Large High Medium Good
Aydin et al. [13] Magnetic No Zigbee, GPRS Medium Low Medium Fair
Hainalkar et al. [14] IR, RFID No - Small Low Short Poor
Grodi et al. [15] Ultrasonic No Zigbee Medium Low Long Fair
Ramaswamy et al. [16] Camera, Ultrasonic Edge Ethernet Small Medium Medium Poor
Kanteti et al. [18] PIR, IP camera Edge Ethernet Small Medium Medium Poor
Teller et al. [19] Camera Edge Ethernet Medium Medium Medium Poor
Proposed System 3D IMU, Water-level, Edge & nRF, LoRa Large Low Long Good
Environment Cloud
By avoiding a single gateway-centered scheme, we can achieve higher fault-tolerance and yield
a more distributed system. In case of failure, another one can take over since all the edge gateways
are synchronized. Existing systems collect and send data directly to the Internet which can result in data
loss during a network interruption. By using reasonable buffer in the edge gateways, this is prevented
and the stored data is transferred to the Cloud once network is re-established. Since edge gateways
are architecturally situated near the sensor nodes, we harness Cloud-like capabilities while reducing
the data flow across the network and limitations of mobile-Cloud computing [
24
]. For connecting
the sensor nodes to the edge gateways, nRF is preferred over other short-range communication
technologies such as ZigBee since nRF has low connection complexity, requires less power and achieves
a high data rate. In contrast, LoRa provides long-range, inexpensive and low-power communication.
For example, SigFox requires a licensing fee for use, Narrowband IoT (NB-IoT) depends on 4G
LTE coverage which is not available everywhere, and GSM modules are expensive for large scale
deployment [
25
]. This makes LoRa suitable for inter-gateway network as gateways are placed far
away and require less data to be transferred compared to communication between sensor nodes
and gateways. Keeping the technological aspects of the sensor nodes, communication mediums,
energy efficiency, expense, available features and limitations of the aforementioned systems, there exists
a scope for developing a smart parking system. With our proposed system, we either eliminate or
lessen the existing shortcomings.
3. Smart Parking System Architecture
An IoT-based parking system can be structured in multiple hierarchical levels, each of which
serves a specific purpose. Typically, such a system senses the environment and/or actuates at the lowest
level of the hierarchy. Shown in Figure 2, the sensor node is usually directly connected to the Cloud
for data transfer, or, is connected to a nearby local gateway or server which enables data pass-through
to the Internet and finally to applications located at the Cloud. A fast communication from the nodes
to all the way up to the Cloud can result in a near real-time application experience. This is observed in
recent years [
23
] and it works well for single node setup. However, as the number of nodes increases
and ubiquitous sensor data becomes essential for IoT-based parking systems, the performance of this
Sensors 2020,20, 4669 6 of 22
approach starts to deteriorate. Correspondingly, the reliability of the system can diminish and cost
of maintenance may increase, especially in application scenarios in which real-time monitoring and
control are of utmost importance.
Gateway
Sensor
Node Cloud
Cloud
Sensor
Node
Figure 2. Typical system architecture in an IoT-based parking system.
To ensure real-time parking space monitoring and control, the existing and most commonly
used Sensor-node-to-Cloud hierarchical approach needs to be improved by integrating high
computation-capable computers or gateways working at the edge layer in the existing system.
This would improve the overall performance and manageability, while effectively reducing operational
latency and cost. In addition, by using LoRa communication technology where data is not required
to transfer frequently and in high volumes, the cost of network coverage and infrastructure can
be significantly lowered. Figure 3illustrates our proposed system architecture that can be used
to implement the typical parking scenario depicted in Figure 1. Our proposed system consists of
four layers: a sensor node layer (SN), an edge gateway layer (EG), a LoRa gateway layer (LG) and a
cloud layer (C). These layers and their corresponding functionalities are described as follows.
Sensor Nodes (SN) Edge Gateway (EG) Cloud (C)
*
Global Storage
Notification
Analytics
LoRa Gateway (LG)
Features, Services
Pre-processing
Compression
Encryption
Dynamic Pricing
*
*
User Terminal
nRF
Maps
Billing
1
2
N
1
2
N
LoRa
1
2
N
Figure 3. Proposed multi-layer system architecture.
3.1. Sensor Nodes
At the lowest and outer limits of the proposed system are sensor nodes which are responsible
for monitoring the parking area. A sensor node has multiple sensors to detect a vehicle and gather
other environmental data. Such nodes are placed either underground in a car park or at the ceiling in a
multistorey car park to detect parked vehicles using one or multiple sensors. For example, an infrared
light sensor in combination with an ultra-sonic rangefinder can be used to detect the presence of a
vehicle. However, car parks are exposed to different weather conditions, and therefore a magnetometer
to detect the specific variation in the magnetic field or a thermal camera to detect differences
in temperature should also be considered. In more sophisticated and privacy-invasive parking
systems [
26
], it is possible to clearly identify vehicles using visual sensors, e.g., RGB cameras. However,
this is expensive, and requires higher processing power and specific placement, thus limiting their
usage to particular scenarios.
In most cases, sensor nodes are run by batteries, and hence are quite limited in terms of resources
such as processing capability and communication methods. Therefore, the acquired data is sent
to a nearby edge gateway for further processing before it can be used at higher level applications.
Owing to the battery-run implementation, it is important to select an appropriate energy-efficient
communication technology to help extend the run-time of the parking sensor nodes. Two possible
Sensors 2020,20, 4669 7 of 22
low-power communication technologies to connect sensor nodes to edge gateways are Bluetooth
Low-Energy (BLE) and Nordic Semiconductor’s nRF, of which nRF has the highest operating range.
3.2. Edge Gateway
Edge computing enables relocation of some of the processing and analysis out of the Cloud
close to the lowest hierarchical layer where the data originates; edge computing brings the cloud
paradigm to the edge of the network [
9
]. This provides a robust solution for IoT-based systems
which require computation-intensive applications and services to fulfil the requirements and comply
with standards. Edge gateways are powerful computers whose resources are not limited by battery
capacity when compared to sensor nodes. Therefore, data pre-processing, filtering, compression,
and encryption can be done in edge gateways as these processes are relatively complex and require
higher computational power. With powerful computing, we can build a more robust system to tolerate
sporadic or intermittent network connectivity issues by implementing fallback algorithms to ensure
operation until network connection is re-established. Higher computation power also allows task
sharing among the high-speed network-equipped gateways if one of the gateways has too much
execution load. Furthermore, to optimally distribute processing in edge gateways, a cloudlet and
greedy auction-based computation can be implemented among the edge gateways [27].
The edge layer consists of multiple edge gateways, each of which can simultaneously handle
multiple sensor nodes. If the number of sensor nodes exceeds the maximum allowed simultaneous
connected devices, a collaborative task allocation and handover of sensor nodes can be implemented to
share the computational load among multiple edge computers for reducing overall operational latency.
Normally, sensor nodes are connected to the nearest edge gateway, but they can switch to another
edge gateway if there is a connectivity problem, thus increasing the robustness of the system.
3.3. LoRa Gateway
To cover a wide area such as urban, suburban and even rural areas at the time of events,
we propose a new LoRa gateway layer. As its name implies, this layer connects to the edge layer
using the LoRa communication technology that can cover a very wide communication range up to
several square kilometers of area [
28
]. To increase the range of a LoRa network, it is possible to
deploy its mesh network capability; one of the LoRa gateways is connected to a high-speed Internet
connection to transfer data to a cloud server. The LoRa gateway layer can be bypassed if there is no
need for long-range communication and an edge gateway can be directly connected to high-speed
Internet. This is possible because there is no data processing located in the LoRa layer. It does provide,
however, unbeatable benefits in terms of communication range and cost effectiveness. Owing to its
economic benefits, the LoRa gateway layer is used to increase robustness of the whole system by
providing an alternative access point to a network.
The LoRa specification has some specific guidelines for the transmission and use of the
frequency spectrum. A proper carrier frequency along with other LoRa configuration parameters
according to the local regulation should be chosen which is robust and provides reliable quality
of service (QoS) [
29
,
30
]. It should be also noted that LoRa has a maximum transmission power
limit of 25 mW and a maximum duty cycle of 1%. Furthermore, a typical LoRa transceiver chipset
is comparatively cheaper than other similar wireless communication modules and has a small
physical footprint. These make LoRa communication suitable for IoT-based applications where
it is not required to continuously send a large amount of data and when long-range operation
and high energy-efficiency is desired [
31
]. Since LoRa is an open access medium, other devices
can access the same frequency spectrum. Therefore, it is essential that security is inbuilt into
the system throughout the whole architecture starting from the sensor nodes [
32
] and that LoRa
gateways can distinguish, validate and authorize the edge gateways which connect to it. Accordingly,
edge gateways must use strong encryption and data validation when sending data to a LoRa gateway.
Sensors 2020,20, 4669 8 of 22
Furthermore, to ensure
appropriate data access and restriction of non-permitted operation, a suitable
authentication scheme [33] should be employed.
3.4. Cloud
The cloud layer is the topmost layer in the architecture and visible to the end-user by providing
different services including virtually unlimited storage and computational resources spread across
the globe. From our smart parking system point of view, the cloud layer enables features such as
usage analytics, revenue monitoring, automatic payment management and other related services
for enhancing user experience. Applications running in the web or installed in handheld smart
devices such as smartphones can provide real-time information and directions to nearby parking areas.
Besides, reservations can be securely made online for pay-as-you-go or on a monthly basis which saves
time and hassle. Anonymous payment schemes and driver information hiding can be employed to
further increase monetary transaction security and enhance location privacy of the drivers [34].
In the long run, Cloud-accumulated parking data can be used to develop smarter algorithms to
improve user experience. Authorities and involved industries can use this data to understand the
needs of the users, parking usage pattern and forecast parking trend, and accordingly design the
system to make the management more efficient [
35
]. This kind of big data is supremely useful for
different alliances among the authorities from multiple countries, companies and organizations which
work on standardizing parking, traffic management, and improvement of smart city solutions and
to reap the most possible benefits [
36
]. Automated analysis running across numerous servers in the
Cloud can enhance the system without too much human intervention. The Cloud also enables services
such as real-time situational feedback and push notifications for end users and case-specific emergency
alerts to the authorities. For example, along with other sensors, visual data from cameras connected to
the Edge and mounted on special places such as at the entrance and exits of a parking area can provide
highly useful information in situations such as when a parking rule violation or an accident occurs.
4. Features, Services and Considerations
A typical parking system becomes smart when it can sense vehicles, and collect, analyze and use
data for adaptive decision making resulting in a reliable and convenient user experience. In this section,
we discuss features, services and design considerations of our proposed system.
4.1. Dynamic Pricing
One of the most important aspects of a parking system is how much a customer or a vehicle
owner has to pay for using the parking space and how the fee is determined. If managed appropriately,
this has twofold advantage- one for the parking customers and the other for the parking
management authorities. Depending on the demand of the parking spaces, especially during peak
hours of the day, the authority can set a slightly higher price to keep a balance between the demand and
available parking slots while offering customers a lower parking charge when more slots are available.
For example, such a scheme can be very fruitful in a large shopping mall parking where there are
limited parking slots but lots of cars and drivers are required to be guided to the nearest parking slot.
Furthermore, it can help prioritize parking requests based on importance, time of request and
special requirements (e.g., drivers with some disabilities).
Dynamically pricing the parking slots is a relatively recent concept; however, it has already started
gaining popularity in many places in the world such as in Washington DC [
37
]. Parking management
system aims at maximizing the revenue for the operating company. For the user side, the system
helps to reduce the fuel consumed for commuting from the current location to the parking slot.
The work reported in [
38
] defined functions which affect the cost of a parking: driving, waiting,
parking and walking. The optimization problem is solved using the greedy parking slot
allocation algorithm. Parking management using dynamic pricing arises due to two objectives.
The first one is to match supply and demand. In case the number of vehicles requesting parking
Sensors 2020,20, 4669 9 of 22
services in one area exceeds the number of available parking slots, the price should be increased such
that only commuters with an urgent need can access the parking. The second objective is to reduce the
traffic congestion by reducing the parking price in less congested area. To fulfil these two objectives,
we propose a novel algorithm which dynamically assigns the hourly price for each parking area.
Let
M
be the number of parking slots and
N
be the number of vehicles requesting the parking.
Our goal is to maximize fwhere Pjis the hourly price of the jth parking lot.
f=
M
j=1
Pj(1)
When the number of vehicles exceeds the number of slots in a particular parking area of the
city, the parking management companies seek to maximize the objective function (1). This can be
expressed as (2) where
Nopt
is the number of users who can afford the current cost of parking, and
Uj
is the maximum cost that the
j
th driver can afford. The bidding is enabled when there are fewer
available slots
M
than the number of drivers
Nopt
. From (2), for all parking slots (indexed with
j
),
until up to
M
number of available slots, it prioritizes the highest ones from the list of drivers when
the bid value is more than the current set price of an individual slot. In the case when a driver fails
to win the bid, the system will put them in the next nearest parking area request queue where there
is comparatively less demand. This happens in real time, as it is processed at the time of the request
is made. The parking system’s algorithm running in edge and cloud gateways will perform this in a
synchronized manner so that drivers do not have to bid multiple times, nor does it become a hassle.
max f
such that MNopt
UjPj;jM
(2)
During an hour, a parking area with 12 slots gets online parking requests from 20 drivers.
Based on needs, drivers also notify the maximum price that they can afford. To enforce fairness
of usage, an upper bound of bidding is set by the parking authority so that a parking request with
an unfairly high bid is not allowed. Consequently, a pricing scheme is selected so that the revenue
is maximized. The algorithm starts by assigning the maximum value and then gradually reduces the
price so as to maximize the revenue and match demands with supply. For example, if 2 drivers bid 15
e
,
3 drivers bid 10
e
and 7 drivers bid 3
e
, Table 2shows three different pricing schemes, from which the
price of 10 eis clearly the optimal one.
Table 2. Example pricing schemes for our dynamic pricing algorithm.
Price Nopt Value of f(e)
15 2 30
10 5 50
3 12 36
The pseudo-code for computing the dynamic price is described in Algorithm 1. The algorithm
gets all
N
bids from the drivers and sorts them in a descending order. For each bid, it determines the
number of drivers who can afford the bid and records as
Nopt
. Afterwards, the objective function is
evaluated and the optimal value is then taken as the maximum value stored in the vector
fi
. If the
number of
Nopt
is larger than
M
, the algorithm prioritizes the users based on time-stamp or descending
order of bids for parking (not shown in the pseudo-code). The complexity of the algorithm depends
on the complexity of the sorting algorithm. Merge sort or heap sort [
39
] is used for achieving a linear
run-time (
Nlog(N)
) for searching maximum value using
N
number of comparisons, thus significantly
reducing the overall processing time.
Sensors 2020,20, 4669 10 of 22
Algorithm 1 Dynamic pricing.
1: procedure DYNAMIC PRICING(M,N,Pbid)
2: Discard unfair bids from Pbid
3: Sort Pbid
4: for i1, Ndo
5: Nopt number of drivers who can afford Pbid,i
6: Mimin(M,No pt )
7: fiMiPbid,i
8: end for
9: fopt max(f)
10: return (fopt,Popt )
11: end procedure
4.2. Edge Services
Staying in close proximity of the sensor nodes, the acquired data from the nodes is sent as it
generates, and is received at the Edge. After receiving, the data is checked for errors and recovery
process is run if required. Following the pre-processing and summarization of data, to maintain
integrity while reducing the overall packet size, the gateways compress data using a lossless algorithm
before sending over the network. Unlike lossy variants, a lossless compression cannot achieve high
compression ratio which should be taken into consideration when determining the maximum allowable
nodes connecting to a single gateway [
40
]. At the Edge, intelligent algorithms employ greedy planning
of data-flow reducing the total path which data travels resulting in an increased performance and
lower response time of the system. In addition, it offers better reliability due to redundancy and
smarter task reallocation in case of a hardware failure.
Multiple services run at the edge gateway layer for analyzing and extracting authority and
customer-specific features and information from the raw data. In the next step, access control service
validates or rejects the use of parking system depending on the status of the customer. If the edge
gateway does not have previous information, it sends a request to the LoRa gateway for authentication.
Being connected to the cloud database through high speed Internet connection, the LoRa gateways
collect the information and then pass it back to the parking control system at the Edge. The dynamic
price computation service based on Algorithm 1then sets a total payable fee for the parking request
and awaits confirmation. Consequently, the real-time map service in the cloud layer provides direction
to the vehicles, significantly easing and shortening the time to reach the parking space otherwise.
When the parking slot is emptied, the billing and payment service charges the fee from the customer,
if not paid earlier due to uncertain parking duration.
Specific automated data and system maintenance routines are periodically run in the edge
gateways to accumulate information on hardware status of parking sensor nodes. Monitoring of
network connectivity parameters, proper bandwidth and channel allocation, availability of a node
and fallback configuration services enhance the quality of service (QoS). For instance, if there is a loss
of connectivity, the edge gateway layer continues to perform most of the operations and when network
communication is re-established, it updates the cloud layer accordingly. Although not realized in
this work, another possibility to use aggregated system data is to create digital-twin-based predictive
maintenance; detailed real-time updates on hardware status criterion such as number of general
run-time faults, number of critical errors, battery health and board temperature can be monitored by
running a background service at idle times with a lower priority. Predictive maintenance is essential to
reduce the operational and maintenance cost over prolonged periods of time. Altogether, the services
running at the Edge ensure that the parking management is simplified and costs are reduced
while having optimum revenue, and also leave room for easily adding more services in future.
Sensors 2020,20, 4669 11 of 22
4.3. Considerations
We used the nRF communication technology for connecting the nodes to the nearby edge gateway.
It serves well in terms of communication range and energy-efficiency. However, when the number of
connected sensor nodes increases in a parking area, a single gateway solution is not suitable anymore.
One limiting factor for the number of sensor nodes is the nRF communication technology based
on nRF24L01+ [
10
] which can handle a maximum of 126 channels. If required, two or more nRF
modules can be used in a single edge gateway to accommodate more nodes per edge gateway.
Alternatively, using more
than one edge gateway reduces the number of connected sensor nodes and
ensures better signal strength between communicating parties. This is preferred because there might
be other 2.4 GHz frequency band-based devices, such as cordless telephones and Wi-Fi routers in the
vicinity. Therefore, it is recommended to keep the number of connected sensor nodes well below the
maximum possible to avoid data corruption due to interference. Besides, depending on the minimum
accuracy and data rate of the sensors which are required in the parking application, other technologies
such as the latest Ultra-wideband (UWB) communication can also be used.
The use of the LoRa communication technology provides a cost-effective, low bandwidth
solution for connecting edge gateways to a LoRa gateway. However, some important aspects
must be ensured for proper operation in the long run. In particular, the airtime of the transceivers
must follow the local regulations and recommended transmission guidelines set by the law and
telecommunication authority. When trying to achieve closer real-time-like behavior with LoRa,
it is not possible to transfer unprocessed raw data directly to the Cloud through the LoRa gateway.
Compression and data fusion are not the only techniques to target real-time-like behavior but also a
sub-band frequency and channel hopping technique [
41
] can be applied. In the sub-band frequency
and channel hopping, the communicating parties jump from one carrier frequency to another within
the permitted frequency spectrum. This can increase the total realized data rate without violating the
LoRa standard regulation of maximum duty cycle of 1% at a specific carrier frequency.
5. System Implementation
We provide a proof of concept with a specific realization to demonstrate the viability and usability
of the proposed smart parking system. We mainly focus on the sensor node and the overall architecture.
We implemented multiple similarly configured edge gateways and a LoRa gateway to test transmission
of data from parking sensor node all the way to the cloud layer. Specific implementation details are
described in respective sections as follows.
5.1. Sensor Node
A sensor node has a control unit and multiple sensors for collecting data in the parking area.
For our proof-of-concept experiment, a Pico-power series AVR MCU from Atmel is used as the sensor
node’s controller to minimize power consumption. For sensing, we used a 9-DOF (degree of freedom)
MPU9250 [
42
] inertial measurement unit (IMU) from Invensense and a BME280 [
43
] environmental
sensor from Bosch. The MPU9250 is used for detecting acceleration due to vibration when the engine of
a vehicle is running during parking. Additionally, the magnetometer inside the MPU9250 measures the
variation in magnetic field as a result of large amount of metal in the chassis of the vehicle. The BME280
measures temperature, humidity and barometric pressure for understanding the weather conditions of
the parking area. Based on the sensor readings, the sensor node achieves context awareness and reports
it to one of the edge gateways. Furthermore, connected to an internal ADC of the MCU, a sensor
placed on the outer surface of the node detects any water that may have accumulated in the parking
area. This enables identifying flooded slots and reporting to the authority for appropriate maintenance
action, and to the drivers for avoiding those slots.
Sensors 2020,20, 4669 12 of 22
5.1.1. Connectivity
For connecting to the nearby edge gateway, an nRF24L01+ based communication module from
Nordic Semiconductor [
10
] is used which allows simultaneously connecting several parking sensor
nodes to the same gateway. Shown in Figure 4, a specific packet structure is defined for reliable
and identifiable data transmission from sensor nodes to the edge gateway. The nRF communication
protocol provides 126 channels in the range of 2.4–2.525 GHz. Each module can use 6 logical data-pipes
for receiving simultaneous individual data streams from other nRF transmitters. This enables
multi-node connectivity to a single edge gateway. To reduce total transferable data from a sensor
node to an edge gateway, a dynamic packet consisting of only required data parameters is used.
Furthermore, least significant
bits (LSB) of some data parameters such as acceleration and magnetic
field are safely ignored as those fluctuate randomly. The data collection from a sensor node is
switchable between one shot, streaming and on-demand streaming mode. In idle state, i.e., no vehicles
are introduced to the parking slot, a periodic one-shot data is sufficient. When an approaching vehicle
is detected, the sensor node can collect a single-set of data or stream data continuously for a certain
period to get up-to-date and detailed information.
Temperature (T)
Pressure (P)
Altitude (A)
Humidity (H0) Humidity (H1)
Gyroscope (Gx) Gyroscope (Gy) Gyroscope (Gz)
Accelerometer (Ax) Accelerometer (Ay) Accelerometer (Az)
Magnetometer (Mx) Magnetometer (My) Magnetometer (Mz)
Extension
Note: Extension can include one or more additional sensor data.
Figure 4. Sensor node to edge gateway data transmission packet format.
.
5.1.2. Energy-Efficiency
To save energy, a greedy approach is applied in the sensor node to maximize the time in sleep
mode and complete other operations such as measurement and data processing within shortest
possible time. The operational time-line is shown in Figure 5. Essentially, the time-line is divided into
two parts: a one-time non-recurring part which consists of start-up routines and initialization of sensors
and communication module, and a recurring one which contains measurement, data processing,
data transmission and sleep operation. The sleep duration in the latter part depends on the frequency
(e.g., once every 10 s) at which the parking slot needs to be monitored for a parked vehicle. To achieve
maximum battery life, all of the components including the MCU are kept in sleep mode for most of the
time during operation and are only activated for the duration when required.
Sensors 2020,20, 4669 13 of 22
MCU
Sensor 1
Sensor 2
Non-repetitive Repetitive
Sleep
Initialization
Start-up
Sleep
Sleep
nRF Sleep
Uninitialized
Uninitialized
Uninitialized
Measure
Measure
Active
Wake-up TX / RX
Sleep
Wake-up
Wake-up
Wake-up
Sleep
Figure 5. Operational time-line of the parking sensor node (Image not scaled to time).
5.1.3. Data Security
To secure the collected data from these sensor nodes before sending it upwards to an edge gateway,
a suitable encryption is applied based on the operation mode. For instance, the AES [
44
] block cipher
is used when only a single data collection is performed, while ChaCha [
45
] stream cipher is better
when streaming continuously. As ChaCha is optimized for streaming data and has lower latency
than AES, it is more suitable for the limited-resource MCU used sensor nodes. We used the Arduino
Crypto library [
46
] to implement these cryptography algorithms in our sensor node. Figure 6shows
comparison of the operational latency in different operations of encryption process between the two
algorithms when varying key sizes at different MCU clock frequency. Consequently, this approach
also reduces energy consumption and helps to achieve longer sensor node battery life.
0
500
1000
1500
2000
2500
3000
3500
AES-128-ECB
AES-256-ECB
ChaCha20 128
ChaCha20 256
AES-128-ECB
AES-256-ECB
ChaCha20 128
ChaCha20 256
AES-128-ECB
AES-256-ECB
ChaCha20 128
ChaCha20 256
Set Key Encrypt Decrypt
Latency (μs)
Operation
8 MHz 16 MHz
Figure 6.
Comparison of latency in different operations of AES and ChaCha cryptography algorithms.
5.2. Edge Gateway
An edge gateway is a significantly more powerful device than a sensor node or the LoRa
gateway. We used a Raspberry Pi3 B+ (hereafter referred to as Pi3) [
47
] running Linux operating
system as the computing resource. The edge gateway provides services such as authentication,
data encryption/decryption and data compression. For connecting the sensor nodes to Pi3, an nRF
communication module with external antenna of 2 dB gain is used to ensure proper signal strength
when a parking slot is occupied. For connecting to a LoRa gateway, a Dragino LoRa GPS HAT shield
Sensors 2020,20, 4669 14 of 22
version 1.0 [
48
] containing an SX1276-based LoRa module is used which operates at 868 MHz carrier
frequency. For better signal reception with LoRa, an external antenna is used. The Dragino LoRa shield
also contains a GPS module; however, it is left unused. The GPS module can be used, for example,
to provide localization of the gateways during setup and maintenance of the system. The gateway
along with the communication modules is powered from a wall-adapter which runs at 220V AC and
provides the system a regulated output of 5V DC at 4 A supply current.
As the edge gateway analyzes, processes, and extracts required information from the raw data
collected from the sensor nodes, an encrypted summarized status of the parking slots is sent to a
LoRa gateway. A simplified data packet payload structure comprising of 4 bytes is shown in Figure 7.
For example, if 50 sensor nodes are connected to an edge gateway, 200 bytes of total data can be
transferred to a LoRa gateway once every 31 s with 125 kHz bandwidth, spreading factor 7, a preamble
of 8 bytes, coding rate of 1.25 and CRC checks. However, due to the implemented compression,
the actual number of transferable bytes is lower than the calculated one.
078 15 16 23 24 31
Auth. key / Flag Sensor Node ID Edge Gateway ID Parking Slot Status
Figure 7. Edge gateway to LoRa gateway data transmission packet payload format.
We used the LZW lossless compression method [
49
] when transferring data from the edge
gateway to the LoRa gateway. With LZW, we have the least possible amount of data transmitted
while preserving data integrity helping to comply with the LoRa airtime regulation (i.e., maximum 1%
duty cycle) by reducing the data size. Table 3shows the compression and decompression latency for
different number of connected nodes. Two different sets of data- a real-life sensor node data set and
another non-repetitive true-random data set are used in the test to understand the normal and worst
case results, respectively. Also, the test was run for 1000 times to get a true-to-reality average as Pi3
also runs other services and results can vary by few micro-seconds depending on the instantaneous
processing load. It can be observed that despite being a lossless compression method with low
compression ratio, as the number of parking sensor nodes increases, the real data achieves higher ratio
and lower average latency compared to the theoretically generated worst-case true-random data.
Table 3.
LZW compression and decompression latency on edge and LoRa gateway, respectively,
in different configuration options.
Data Set
Number
of
Nodes
Size Avg. Latency Avg. Latency/Byte
[bytes] [ms] [µs]
Original Compressed Compress Decompress Compress Decompress
Real-life
1 4 6 0.1755 0.0204 43.8695 5.0913
10 40 47 0.1850 0.0366 4.6256 0.9139
50 200 194 0.1591 0.0603 0.7959 0.3017
100 400 395 0.3683 0.1568 0.9208 0.3920
True-random
1 4 194 0.1221 0.0143 30.5475 3.5720
10 40 157 0.1610 0.0314 4.0252 0.7862
50 200 395 0.2111 0.0801 1.0554 0.4005
100 400 268 0.2431 0.1331 0.6078 0.3328
5.3. LoRa Gateway and Cloud
The emphasis of this work is at the lower hierarchical levels, especially at the sensor nodes and
edge layer. The LoRa gateway and cloud layers are abstracted more to cover the whole data flow from
a sensor node to an end user. A LoRa gateway in the experiment connects to all the edge gateways for
receiving extracted, summarized and processed data. To simplify the realization, another Pi3 is used
Sensors 2020,20, 4669 15 of 22
as the LoRa gateway with similar configuration as of the edge gateway. However, this Pi3 is connected
to a local 100 Mbps bandwidth Internet connection via the on-board RJ-45 Ethernet port.
At the cloud layer, we used a Linode [
50
] Cloud-based control and information display panel
similar to [
51
] which runs parking information application and real-time notification service. The Cloud
server aggregates all the data from different LoRa gateways spread around over a large geographic area.
It provides powerful computing, storage capacity as needed and ability to run applications and services
required by the system. For security, it employs advanced OpenSSH features, secure file transfer
protocol (SFTP), dm-crypt-based file encryption and log-parsing application for detection of automated
attacks to the server [
50
,
52
]. In this work, our Cloud implementation enables us to view the near
real-time information of the parking slots.
6. Experimental Results
We ran the implemented smart parking system to assess its performance regarding
energy-efficiency, data acquisition and vehicle-type sensing capability of the sensor node. We also
tested our dynamic pricing algorithm to demonstrate its effectiveness for increased revenue.
6.1. Energy-Efficiency
The energy consumption of our sensor node with different sensing intervals is shown in Figure 8.
The measurements show average current and average power consumed when 3.0V and 3.3 V voltage
supply was used as the power supply. It can be observed that the overall energy requirement of the
prototype sensor node is low because both the MCU and sensor nodes use low-power sleep mode.
Among the test configurations, the lowest average current and average power are achieved at 3.0V
when vehicle detection is performed once every minute. This translates to an operating life of more
than 1 year and 8 months with a 10,000 mAh battery when it works 24/7. However, if we switch
to vehicle-detection-based transmission mode, lower number of transmissions occurs, and thus we
can achieve a higher battery life compared to periodic vehicle monitoring mode, especially during
the off-peak hours. Figure 9shows how battery life is affected when a sensor node is run in these
two modes: Figure 9a shows battery life for monitoring interval in seconds and Figure 9b for varying
number of vehicles parked in a day. It is noticeable that in the periodic monitoring mode, the battery
life can have higher variation depending on the monitoring interval. When calculating the battery
life of the parking sensor node, a nominal 1% monthly battery self-discharge rate is used. Since the
parking sensor node is mostly kept in sleep mode, the battery has enough time to recover the capacity
lost due to switching from sleep to active mode [53].
2
1.12
1.03
1.02
2.18
1.17
1.09
1.07
5.97
3.35
3.09
3.05
7.17
3.86
3.59
3.53
0
1
2
3
4
5
6
7
8
110 30 60
Sensing Interval (s)
Avg. Current (mA) at 3.0V Avg. Current (mA) at 3.3V
Avg. Power (mW) at 3.0V Avg. Power (mW) at 3.3V
Figure 8.
Average current (mA) and average power (mW) consumed by the prototype parking sensor
node at different voltage levels and sampling intervals.
Sensors 2020,20, 4669 16 of 22
0 20 40 60 80 100 120
1.4340
1.4360
1.4380
1.4400
·104
Monitoring interval (s)
Battery life (h)
(a) Periodic monitoring
010 20 30 40
1.44112
1.44113
1.44114
1.44115
·104
Vehicles parked per day
Battery life (h)
(b) Detection-based monitoring
Figure 9. Sensor node’s battery (10,000 mAh) life during Periodic and Detection-based monitoring.
For the experiment, the sensor node was composed of off-the-shelf modules and sensor boards.
As these boards have extra components such as voltage regulators and other passive components,
the sleep mode current of 0.69mA at 3.0 V supply is undoubtedly high. This can be reduced
by designing a single board PCB, hence eliminating redundant components and shortening wire
connection lengths. During multi-voltage testing, it was found that a sensor node can also
reliably operate at 2.8V supply with brown-out detection enabled at 2.7 V. If the supply level is
ensured with appropriate regulation, this can be lowered until 2.4V without hindering the normal
functionality of the node and the brown-out detection feature can be safely disabled. The battery
life can be further optimized to prolong the battery life by aggressive use of advanced sleep
modes, using minimal hardware components, applying selective data acquisition from sensors,
implementing true shutdown technique, and, furthermore, with a fabricated System-On-Chip (SoC).
6.2. Sensing
To sense the change in magnetic field and the level of vibration when an empty parking slot
is taken by a vehicle, the sensor node was placed on the ground keeping the sensor side near
the surface. The sensor node clearly senses when a vehicle enters and leaves the parking slot;
the readings of the acceleration and magnetic field changes are shown in Figure 10. Each of the
parameters accurately registers a vehicle occupying a slot, but fusing the data increases the robustness
of the system. Figure 11 shows the acquired data at a sampling rate of 4Hz. During the test, the vehicles
were parked and removed twice, subsequently. For presence detection, the data is filtered using a
simple sample-and-hold transient filter, i.e., any sudden change shorter than a preset time period
is ignored. Afterwards, the magnetometer data-points are cross-validated with acceleration data-points.
For example, when there is a change in magnetometer readings, there is also a simultaneous fluctuation
in the acceleration readings. This change happens due to deceleration and acceleration when a
vehicle gets in and out of the parking slot, respectively, and engine vibrations. It is observable
that different kind and make of vehicles result in different magnetic field disturbance. This is
used to determine vehicle types, i.e., a large covered van or a small car. For example, the VW
Truck causes higher degree of change in magnetic field, as shown in Figure 11, compared to other
vehicles tested. Figure 12 lists the difference in Z-axis magnetic signatures calculated from the collected
data using Fréchet distance [
54
] at the edge gateway. Evidently, the VW Truck has the highest magnetic
signature difference compared to other vehicles, as also indicated earlier in Figure 11. This vehicle
classification data can be used in the proposed dynamic pricing algorithm to set an additional fee for
commercially used vehicles. Furthermore, since parking behavior (e.g., speed, duration etc.) differs,
to compensate the time-wise variation, dynamic time warping [
55
] can be used to align sensor data
across different vehicles.
Sensors 2020,20, 4669 17 of 22
0 50 100 150 200
1.5
1
0.5
0
Acceleration (g)
Ax
Ay
Az
0 50 100 150 200
50
0
50
Samples
Mag. field (mG)
Mx
My
Mz
Figure 10.
Variation in accelerometer and magnetometer readings sensed by our prototype parking
sensor node when a vehicle enters and leaves the parking slot. Here, Ax, Ay, Az indicate 3-axes
acceleration changes, and Mx, My, Mz indicate 3-axes magnetic field changes.
0 50 100 150 200 250
100
80
60
40
20
Samples
Mag. field (mG)
Ford Focus
Mazda 5
Opel Vectra
Toyota Auris (as)
Toyota Yaris
VW Golf (as)
VW Taro
VW Truck
Figure 11.
Variation in magnetic field (Z-axis) among vehicles when entering and leaving the parking.
Figure 12. Difference in magnetic field signature (Z-axis) among vehicles.
The edge gateway is physically placed in a suitable place in the parking area such that the
variation among distances from individual sensor nodes and the gateway is minimum. In other words,
to ensure the best signal reception, the gateway should be at a uniform distance from each sensor
node as possible. In our 45 m by 20m experiment parking area, we tried four placement options for
the edge gateway as shown in Figure 13. The edge gateway works well for all the placement options
Sensors 2020,20, 4669 18 of 22
a,b,cand d,
with option agetting the lowest connection signal. The reason is a large metallic dumpster
(not visible in the figure) nearby slot 13 interfering the signal propagation when the gateway is placed
at 1 m height from the ground. Placing the gateway higher with antennas kept upright improved
signal reception, which should be considered during a permanent installation of the parking system.
201918
17
1615
14
13
1 2 345 6 78 9 10 11 12
a
bc
dIN
OUT
Figure 13.
Edge gateway placement suitability in a parking slot during experiment. Gateway placement
options are marked with a–d, and parking slots are marked with numbers 1–20.
6.3. Dynamic Pricing
As shown in Figure 14, we simulated parking requests from drivers for a total of 15 parking
slots and performed a comparison of dynamic and fixed pricing modes as the number of parking
requests increases. In particular, when the number of requests is greater than the number of available
parking slots (shaded in Gray), the dynamic pricing scheme increases revenue significantly. It should
be noted that, while the dynamic price fluctuates depending on the bids for a certain number of
parking requests, it always ensures the collection of minimum parking fee.
Figure 14. Comparison of fixed and dynamic parking fee scheme.
7. Conclusions
Traffic management and accordingly convenient vehicle parking is a big challenge because of
the booming number of vehicles worldwide. Therefore, a proper solution is required to mitigate
the problem, ensure manageability, increase usability of parking slots and operate the traffic
system informatively. In this paper, we presented the smart vehicle parking system architecture
consisting of four layers: sensor nodes, edge gateway, LoRa gateway and Cloud. The sensor nodes
are energy-efficient, secure, and collect multi-parametric data about the parking slots in near real-time.
Sensors 2020,20, 4669 19 of 22
The edge layer processes sensor data before transmitting it to the cloud layer, and consequently to the
end users. The LoRa gateway layer provides communication to ensure robust connection between the
edge and cloud layers. We not only discussed the theoretical aspects of the smart parking systems
but also provided a proof-of-concept with a specific realization. We demonstrated viability and
usability of the proposed system for managing a parking area, and also discussed related services and
design considerations. Even using off-the-self components, the sensor nodes achieve 20+ months of
operation time which can be easily extended for a production ready sensor node. We took the latest
communication technology LoRa along with nRF into use to effectively increase the energy-efficiency
and coverage area. Furthermore, we presented a dynamic pricing algorithm for maximizing profit for
the parking management authorities.
In future, we plan to implement AI-based computation in edge gateways for optimized
management through cooperative decision making and running QoS services based on the vehicle
parking data. This would facilitate better task allocation, enhance inter-gateway data synchronization
and reduce network traffic by using AI-predicted parking usage patterns.
Author Contributions:
Conceptualization, V.K.S.; methodology, V.K.S.; software, V.K.S.; validation, V.K.S. and
T.N.G.; writing—original draft preparation, V.K.S.; writing—review and editing, V.K.S. and T.W.; visualization,
V.K.S. and T.W.; supervision, I.B.D. and T.W.; funding acquisition, T.W. All authors have read and agreed to the
published version of the manuscript.
Funding: This work is supported by Academy of Finland (Grant No. 328755).
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
AC Alternating Current
ADC Analog-Digital Converter
AES Advanced Encryption Standard
AI Artificial Intelligence
AVR Alf-Egil Bogen Vegard Wollan RISC
BLE Bluetooth Low Energy
CMOS Complementary metal–oxide–semiconductor
CRC Cyclic Redundancy Check
DC Direct Current
DOF Degree of Freedom
GPRS General Packet Radio Service
GPS Global Positioning System
GSM Global System for Mobile
ID Identity
IMU Inertial Measurement Unit
IoT Internet of Things
IP Internet Protocol
IR Infra-red
LCD Liquid Crystal Display
LSB Least Significant Bit
LTE Long Term Evolution
LZW Lempel-Ziv-Welch
MCU Micro-controller Unit
NB Narrow-band
PCB Printed Circuit Board
PIR Passive Infra-red
Sensors 2020,20, 4669 20 of 22
QoS Quality of Service
RFID Radio Frequency Identification
RGB Red Green Blue
SFTP Secure File Transfer Protocol
SMS Short Message Service
UWB Ultra-wideband
WSN Wireless Sensor Network
References
1.
Jain, V.; Sharma, A.; Subramanian, L. Road Traffic Congestion in the Developing World. In Proceedings of
the 2nd ACM Symposium on Computing for Development, Atlanta, GA, USA, 11–12 March 2012; Volume 11,
pp. 1–10. [CrossRef]
2.
Hennessy, D.A.; Wiesenthal, D. Traffic congestion, driver Stress, and driver aggression. Aggress. Behav.
1999
,
25, 409–423. [CrossRef]
3.
Lee, Y.; LaVoie, N. Relationship between frustration justification and vehicle
control Behaviors—A simulator study. In Proceedings of the Human Factors and Ergonomics Society
Annual Meeting, Chicago, IL, USA, 27–31 October 2014; Volume 58, pp. 2235–2239. [CrossRef]
4. Elvik, R. The Handbook of Road Safety Measures; Emerald: Bingley, UK, 2009.
5.
Commuters in These Cities Spend More Than 8 Days a Year Stuck in Traffic. 2019.
Available online: https://www.weforum.org/agenda/2019/02/commuters-in-these-cities-spend-more-
than-8-days-a-year-stuck-in-traffic/ (accessed on 25 June 2020).
6.
Talbot, N.; Lehn, R. The Impacts of Transport Emissions on Air Quality in Auckland’s City Centre.
2018. Available online: http://knowledgeauckland.org.nz/assets/publications/TR2018-028-Impacts-
of-transport-emissions-Auckland-city-centre.pdf (accessed on 30 August 2019).
7.
Diamandis, P.H. Imagining the Smart Cities of 2050. 2019. Available online: https://singularityhub.com/
2019/03/15/imagining-the-smart-cities-of-2050/ (accessed on 30 August 2019).
8.
Dhaou, I.B.; Kondoro, A.; Alsabhawi, A.H.; Guedhami, O.; Tenhunen, H. A Smart Parking Management
System Using IoT Technology. In Proceedings of the 22nd Conference of Open Innovations Association,
Helsinki, Finland, 11–13 April 2018; Volume 43, pp. 309–312.
9.
Premsankar, G.; Di Francesco, M.; Taleb, T. Edge Computing for the Internet of Things: A Case Study.
IEEE Internet Things J. 2018,5, 1275–1284. [CrossRef]
10.
nRF24 Series. Available online: https://www.nordicsemi.com/Products/Low-power-short-range-wireless/
nRF24-series (accessed on 30 January 2020).
11.
LoRaWAN Specification. 2017. Available online: https://net868.ru/assets/pdf/LoRaWAN-v1.1.pdf
(accessed on 30 January 2020).
12.
Sadhukhan, P. An IoT-based E-parking system for smart cities. In Proceedings of the 2017 International
Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India,
13–16 September 2017; pp. 1062–1066.
13.
Aydin, I.; Karakose, M.; Karakose, E. A navigation and reservation based smart parking platform using
genetic optimization for smart cities. In Proceedings of the 2017 5th International Istanbul Smart Grid and
Cities Congress and Fair (ICSG), Istanbul, Turkey, 19–21 April 2017; pp. 120–124.
14.
Hainalkar, G.N.; Vanjale, M.S. Smart parking system with pre & post reservation, billing and traffic app.
In Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS),
Madurai, India, 15–16 June 2017; pp. 500–505. [CrossRef]
15.
Grodi, R.; Rawat, D.B.; Rios-Gutierrez, F. Smart parking: Parking occupancy monitoring and visualization
system for smart cities. In Proceedings of the SoutheastCon 2016, Norfolk, VA, USA, 30 March–3 April 2016;
pp. 1–5. [CrossRef]
16.
Ramaswamy, P. IoT smart parking system for reducing green house gas emission. In Proceedings of
the 2016 International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India,
8–9 April 2016; pp. 1–6. [CrossRef]
Sensors 2020,20, 4669 21 of 22
17.
Wang, M.; Dong, H.; Li, X.; Song, L.; Pang, D. A novel parking system designed for smart cities.
In Proceedings of the 2017 Chinese Automation Congress (CAC), Jinan, China, 20–22 October 2017;
pp. 3429–3434. [CrossRef]
18.
Kanteti, D.; Srikar, D.V.S.; Ramesh, T.K. Smart parking system for commercial stretch in cities. In Proceedings
of the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India,
6–8 April 2017; pp. 1285–1289. [CrossRef]
19.
Telles, E.; Meduri, P. SParkSys: A Framework for Smart Parking Systems. In Proceedings of
the 2017 International Conference on Computational Science and Computational Intelligence (CSCI),
Las Vegas, NV, USA, 14–16 December 2017; pp. 1396–1399. [CrossRef]
20.
Du, R.; Santi, P.; Xiao, M.; Vasilakos, A.V.; Fischione, C. The Sensable City: A Survey on the Deployment and
Management for Smart City Monitoring. IEEE Commun. Surv. Tutor. 2019,21, 1533–1560. [CrossRef]
21.
Pham, N.; Hassan, M.; Nguyen, H.M.; Kim, D. GS1 Global Smart Parking System: One Architecture to
Unify Them All. In Proceedings of the 2017 IEEE International Conference on Services Computing (SCC),
Honolulu, HI, USA, 25–30 June 2017; pp. 479–482. [CrossRef]
22.
Šilar, J.; R˚užiˇcka, J.; Bˇelinovà, Z.; Langr, M.; Hlubuˇcková, K. Smart parking in the smart city application.
In Proceedings of the 2018 Smart City Symposium Prague (SCSP), Prague, Czech Republic, 24–25 May 2018;
pp. 1–5. [CrossRef]
23.
Hassoune, K.; Dachry, W.; Moutaouakkil, F.; Medromi, H. Smart parking systems: A survey. In Proceedings
of the 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA),
Mohammedia, Morocco, 19–20 October 2016; pp. 1–6. [CrossRef]
24.
Liu, F.; Shu, P.; Jin, H.; Ding, L.; Yu, J.; Niu, D.; Li, B. Gearing resource-poor mobile devices with powerful
clouds: Architectures, challenges, and applications. IEEE Wirel. Commun. 2013,20, 14–22.
25.
NB-IoT vs. LoRa vs. Sigfox. 2018. Available online: https://www.link-labs.com/blog/nb-iot-vs-lora-vs-
sigfox (accessed on 25 October 2019).
26.
Luque, L.; Michel-Torres, D.A.; Lopez-Neri, E.; Carlos-Mancilla, M.A.; González-Jiménez, L.E. IoT Smart
Parking System based on the Visual-Aided Smart Vehicle Presence Sensor: SPIN-V. Sensors
2020
,20, 1476.
[CrossRef] [PubMed]
27.
Chen, S.; Jiao, L.; Wang, L.; Liu, F. An Online Market Mechanism for Edge Emergency Demand
Response via Cloudlet Control. In Proceedings of the IEEE INFOCOM 2019—IEEE Conference on
Computer Communications, Paris, France, 29 April–2 May 2019; pp. 2566–2574.
28.
Extreme Range Links: LoRa 868/900 MHz SX1272 LoRa Module for Arduino Waspmote and Raspberry Pi.
Available online: https://www.cooking-hacks.com (accessed on 15 January 2020).
29.
Haxhibeqiri, J.; Moerma, I.; Hoebeke, J. Low Overhead Scheduling LoRa Transmissions for
Improved Scalability. IEEE Internet Things J. 2019,6, 3097–3109. [CrossRef]
30.
Sallum, E.; Pereira, N.; Alves, M.; Santos, M. Performance optimization on LoRa networks through assigning
radio parameters. In Proceedings of the 2020 IEEE International Conference on Industrial Technology (ICIT),
Buenos Aires, Argentina, 26–28 February 2020; pp. 304–309.
31.
Sarker, V.K.; Peña Queralta, J.; Gia, T.N.; Tenhunen, H.; Westerlund, T. A Survey on LoRa for IoT: Integrating
Edge Computing. In Proceedings of the 2019 Fourth International Conference on Fog and Mobile Edge
Computing (FMEC), Rome, Italy, 10–13 June 2019; pp. 295–300.
32.
Sarker, V.K.; Gia, T.N.; Tenhunen, H.; Westerlund, T. Lightweight Security Algorithms for
Resource-constrained IoT-based Sensor Nodes. In Proceedings of the IEEE International Conference
on Communications 2020, Dublin, Ireland, 7–11 June 2020; pp. 1–6.
33.
Challa, S.; Das, A.K.; Gope, P.; Kumar, N.; Wu, F.; Vasilakos, A.V. Design and analysis of authenticated
key agreement scheme in cloud-assisted cyber–physical systems. Future Gener. Comput. Syst.
2020,108, 1267–1286. [CrossRef]
34.
Zhu, L.; Li, M.; Zhang, Z.; Qin, Z. ASAP: An Anonymous Smart-Parking and Payment Scheme in
Vehicular Networks. IEEE Trans. Depend. Secur. Comput. 2020,17, 703–715. [CrossRef]
35.
Lin, J.; Chen, S.Y.; Chang, C.Y.; Chen, G. SPA: Smart Parking Algorithm Based on Driver Behavior and
Parking Traffic Predictions. IEEE Access 2019,7, 34275–34288. [CrossRef]
36.
EPA. European Parking Association. Available online: https://www.europeanparking.eu
(accessed on 9 December 2019).
Sensors 2020,20, 4669 22 of 22
37.
ParkiFi. Dynamic Pricing for Parking. Available online: https://www.parkifi.com/blog/dynamic-pricing-
for-parking (accessed on 5 November 2019).
38.
Tang, C.; Wei, X.; Zhu, C.; Chen, W.; Rodrigues, J.J.P.C. Towards Smart Parking Based on Fog Computing.
IEEE Access 2018,6, 70172–70185. [CrossRef]
39.
Cormen, T.H.; Leiserson, C.E.; Rivest, R.L.; Stein, C. Introduction to Algorithms, 3rd ed.; The MIT Press:
Cambridge, MA, USA, 2009.
40.
Salomon, D.; Motta, G. Handbook of Data Compression; Springer: London, UK, 2010; pp. 443–730. [CrossRef]
41.
Adelantado, F.; Vilajosana, X.; Tuset-Peiro, P.; Martinez, B.; Melia-Segui, J.; Watteyne, T. Understanding the
Limits of LoRaWAN. IEEE Commun. Mag. 2017,55, 34–40. [CrossRef]
42.
Invensense. MPU9250. Available online: https://www.invensense.com/products/motion-tracking/9-axis/
mpu-9250 (accessed on 20 February 2020).
43.
Sensortec, B. BME280. Available online: https://www.bosch-sensortec.com/bst/products/all_products/
bme280 (accessed on 20 February 2020).
44.
National Institute of Standards and Technology. Specification for the Advanced Encryption
Standard (AES). Available online: https://nvlpubs.nist.gov/nistpubs/FIPS/NIST.FIPS.197.pdf
(accessed on 4 February 2020).
45.
Internet Research Task Force (IRTF). ChaCha20 and Poly1305 for IETF Protocols. Available online:
https://tools.ietf.org/html/rfc7539 (accessed on 4 February 2020).
46.
Weatherley, R. Arduino Cryptography Library. Available online: https://rweather.github.io/arduinolibs/
crypto.html (accessed on 10 December 2019).
47. Raspberry Pi 3 Model B+. Available online: http://www.dragino.com/products/lora/item/106-lora-gps-
hat.html (accessed on 22 November 2019).
48.
Dragino. LoRa GPS HAT for Raspberry Pi. Available online: http://www.dragino.com/products/lora/
item/106-lora-gps-hat.html (accessed on 22 November 2019).
49.
LZW Compression. Available online: https://rosettacode.org/wiki/LZW_compression
(accessed on 30 March 2020).
50.
Linodians. Cloud Hosting & Linux Servers|Linode. Available online: https://www.linode.com
(accessed on 10 April 2020).
51.
Sarker, V.K.; Jiang, M.; Gia, T.N.; Anzanpour, A.; Rahmani, A.M.; Liljeberg, P. Portable Multipurpose
Bio-signal Acquisition and Wireless Streaming Device for Wearables. In Proceedings of the 2017 IEEE
Sensors Applications Symposium (SAS), Glassboro, NJ, USA, 13–15 March 2017; pp. 1–6. [CrossRef]
52.
OpenBSD Project. OpenSSH Features. Available online: https://www.openssh.com/features.html
(accessed on 7 July 2020).
53.
Farahani, S. Chapter 6—Battery Life Analysis. In ZigBee Wireless Networks and Transceivers; Farahani, S., Ed.;
Newnes: Burlington, MA, USA, 2008; pp. 207–224. [CrossRef]
54.
Eiter, T.; Mannila, H. Computing Discrete Fréchet Distance; Technical Report (CD-TR 94/64); Christian Doppler
Laboratory for Expert Systems: Vienna, Austria, 1994.
55.
Pavel, S. Dynamic Time Warping Algorithm Review; Technical report series; Information and Computer Science
Department University of Hawaii: Honolulu, HI, USA, 2009.
©
2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... The system accumulates all messages in the messaging server for further processing if the main server for information collection fails. Another smart parking solution with a pricing algorithm for revenue maximization was proposed in [35]. The price is dynamically set to balance the available and requested parking spots and ensure the minimum parking fee is charged. ...
... where Rate m is the maximum rate that the mth driver will pay. The sorting algorithms Merge Sort or Heapsort [35] can be used to search for the optimal maximum value, due to their low complexity. ...
Article
Full-text available
Current parking systems employ a single gateway-centered solution (i.e., cloud) for data processing which leads to the possibility of a single point of failure, data loss, and high delays. Moreover, the parking-spot selection process considers criteria that do not maximize parking utilization and revenue. The pricing strategy does not achieve high revenue because a fixed pricing rate is utilized. To address these issues, this paper proposes a smart parking system based on the Internet of Things (IoT) that provides useful information to drivers and parking administrators about available parking spots and related services such as parking navigation, reservation, and availability estimation. A multi-layer architecture is developed that consists of multiple sensor nodes, and fog and cloud computing layers. The acquired parking data are processed through fog computing nodes to facilitate obtaining the required real-time parking data. A novel algorithm to obtain the optimal parking spot with the minimum arrival time is also presented. Proof-of-concept implementation and simulation evaluations are conducted to validate the system performance. The findings show that the system reduces the parking arrival time by 16%–46% compared to current parking systems. In addition, the revenue is increased for the parking authority by 10%–15%.
... In [9] price variations are based on peak hours. Different from these approaches is seen in [10]. They allowed bidding among competitors to determine who takes on the available parking space. ...
... In addition, this type of architectures is not suitable for real-time data processing due to communication latency. More recently, fog computing [14,37,56] and edge computing architectures [13,33,47] were proposed to address the limitation of the architectures based on cloud computing. The main idea behind the proposal of these new architectures is to process data as close to the location where the data were obtained as possible to reduce the time and amount of data transmission. ...
Chapter
Full-text available
Integration of artificial intelligence (AI), Internet of Things (IoT) and Cloud computing technologies in a unified system to address a real-world problem is challenging and of high demand. This chapter discusses the processes, challenges and solutions concerning designing an airport smart parking system. IoT parking sensors, Open Automatic License Plate Recognition (OpenALPR) library, and the IBM cloud-based IoT platform are integrated to tackle technical challenges, including the automatic identification of plate numbers, models, and colours of vehicles in parking spaces, in both indoor and outdoor parking environments. The chapter also addresses several issues related to the system, i.e., the system architecture design, the selection of sensing technologies and hardware and software platforms, while taking into account specific characteristics of IoT and AI technologies. Although the proposed system is developed for the airport smart parking problem, the discussion is relevant to problems in other domains from the system design and integration perspectives.
... This problem is severe and affects several aspects of human life. The lack of parking lots causes traffic congestion because people searching or waiting for an available parking lot move slowly, causing a long queue to form on streets and blocking other cars [3]. Moreover, cars moving slowly when searching for parking increases carbon emissions [4]. ...
Article
Full-text available
The size of cities has been continuously increasing because of urbanization. The number of public and private transportation vehicles is rapidly increasing, thus resulting in traffic congestion, traffic accidents, and environmental pollution. Although major cities have undergone considerable development in terms of transportation infrastructure, problems caused by a high number of moving vehicles cannot be completely resolved through the expansion of streets and facilities. This paper proposes a solution for the parking problem in cities that entails a shared parking system. The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage. Open-to-specific-groups parking lots consist of parking spaces provided for particular people, such as parking buildings at universities for teachers, staff, and students. The proposed shared parking system comprises four primary steps: collecting and preprocessing data by using an Internet of Things system, predicting internal demand by using a recurrent neural network algorithm, releasing several unoccupied parking lots based on prediction results, and continuously updating the real-time data to improve future internal usage prediction. Data collection and data forecasting are performed to ensure that the system does not override personal usage. This study applied several forecasting algorithms, including seasonal ARIMA, support vector regression, multilayer perceptron, convolutional neural network, long short-term memory recurrent neural network with a many-to-one structure, and long short-term memory recurrent neural network with a many-to-many structure. The proposed system was evaluated using artificial and real datasets. Results show that the recurrent neural network with the many-to-many structure generates the most accurate prediction. Furthermore, the proposed shared parking system was evaluated for some scenarios in which different numbers of parking spaces were released. Simulation results show that the proposed shared parking system can provide parking spaces for public usage without overriding personal usage. Moreover, this system can generate new income for parking management and/or parking lot owners.
... A multilayer design for an intelligent parking system has been presented that incorporates edge-cloud computing, LPWAN technology, and multiparametric parking space sensor nodes by Sarker et al. 81 The suggested mechanism enables the dynamic management of parking on a large scale and provides beneficial information to drivers regarding vacant parking places. Besides, a dynamic pricing algorithm has been proposed to maximize parking space availability for drivers while generating the most future income for parking authority. ...
Article
Full-text available
With the quick progress of wireless technologies, the Internet of Things (IoT) has been recognized as the main part of daily people's lives that makes their life more convenient by utilizing a diverse range of smart devices. Over the last decade, various useful and forthcoming application fields have been developed by taking advantage of the IoT concept. In this regard, LPWAN technologies such as LoRa, Sigfox, and NB-IoT play an important role in advancing. These technologies are suitable wireless communication protocols for battery-powered IoT objects that enable long-distance communication in low-power devices at a low operation cost. Some review papers have investigated the LPWAN technology in the IoT from different perspectives. However, the lack of detailed and systematic study outlining the role of these technologies in different IoT applications is very clear. Consequently, this gap inspired us to write the current study. The current works are classified into three main classes, including smart city, home automation, and smart healthcare. Besides, the involved models in the smart city group are categorized into five subgroups, including smart monitoring, smart metering, air pollution, smart agriculture, and smart parking. The selected works are reviewed, and their main features, including main idea, coverage range, sensor types, processing board, frequency band, communication protocols, power consumption analysis, and achievement, are specified. Generally, our main aim is to describe the challenging problems of applying LPWAN to various IoT applications, specify the efficient models, and recommend some hints for upcoming studies.
... Active research areas in TIERS include multi-robot coordination [1], [2], [3], [4], [5], swarm design [6], [7], [8], [9], UWB-based localization [10], [11], [12], [13], [14], [15], localization and navigation in unstructured environments [16], [17], [18], lightweight AI at the edge [19], [20], [21], [22], [23], distributed ledger technologies at the edge [24], [25], [26], [27], [28], [29], edge architectures [30], [31], [32], [33], [34], [35], offloading for mobile robots [36], [37], [38], [39], [40], [41], [42], LPWAN networks [43], [44], [45], [46], sensor fusion algorithms [47], [48], [49], and reinforcement and federated learning for multi-robot systems [50], [51], [52], [53]. ...
... Moreover, a vehicle smart routing system was a useful tool for assessing how air pollution caused by vehicles could be minimized by taking ecosystem services (green areas) into account [22]. Even if the impact on the environment in a real-time location based shared smart parking system was not assessed, it is found to be useful time saving solution for air pollution and efficient traffic management [25,26]. ...
Article
Full-text available
The transport sector is one of the largest contributors of CO2 emissions and other greenhouse gases. In order to achieve the Paris goal of decreasing the global average temperature by 2 ºC, urgent and transformative actions in urban mobility are required. As a sub-domain of the smart-city concept, smart-mobility-solutions integration at the municipal level is thought to have environmental, economic and social benefits, e.g., reducing air pollution in cities, providing new markets for alternative mobility and ensuring universal access to public transportation. Therefore, this article aims to analyze the relevance of smart mobility in creating a cleaner environment and provide strategic and practical examples of smart-mobility services in four European cities: Berlin (Germany), Kaunas (Lithuania), Riga (Latvia) and Tartu (Estonia). The paper presents a systematized literature review about the potential of smart-mobility services in reducing the negative environmental impact to urban environments in various cities. The authors highlight broad opportunities from the European Union and municipal documents for smart-mobility initiatives. The theoretical part is supplemented by socioeconomic and environmental descriptions, as well as experience, related to smart-mobility services in the four cities selected.
Article
With the advent of modern technologies, including the IoT and blockchain, smart-parking (SP) systems are becoming smarter and smarter. Similar to other automated systems, and particularly those that require automation or minimal interaction with humans, the SP system is heuristic in delivering performances, such as throughput in terms of latency, efficiency, privacy, and security, and it is considered a long-term cost-effective solution. This study looks ahead to future trends and developments in SP systems and presents an inclusive, long-term, effective, and well-performing smart autonomous vehicle parking (SAVP) system that explores and employs the emerging fog-computing and blockchain technologies as robust solutions to strengthen the existing collaborative IoT–cloud platform to build and manage SP systems for autonomous vehicles (AVs). In other words, the proposed SAVP system offers a smart-parking solution, both indoors and outdoors, and mainly for AVs looking for vacant parking, wherein the fog nodes act as a middleware layer that provides various parking operations closer to IoT-enabled edge devices. To address the challenges of privacy and security, a lightweight integrated blockchain and cryptography (LIBC) module is deployed, which is functional at each fog node, to authorize and grant access to the AVs in every phase of parking (e.g., from the parking entrance to the parking slot to the parking exit). A proof-of-concept implementation was conducted, wherein the overall computed results, such as the average response time, efficiency, privacy, and security, were examined as highly efficient to enable a proven SAVP system. This study also examined an innovative pace, with careful considerations to combatting the existing SP-system challenges and, therefore, to building and managing future scalable SP systems.
Article
Full-text available
The LoRa radio technology is one of the most prominent choices in the Internet of Things Low-Power Wide Area Networks (LPWANs) industry due to its versatile and robust technical characteristics along with its ability to achieve long communication ranges combined with low energy consumption and reduced cost. One of the main issues in LoRa networks is how many end-devices can be reporting efficiently while meeting the requirements set by the application they support. This is known as the capacity metric and it is affected by many network parameters and various factors. A literature overview is presented in this work, studying works on LoRa-based networks, outlining their behavior and categorizing them based on their technological breakthroughs. Throughout this survey, a number of performance determinants that stand out are highlighted. These factors span five main categories that encompass physical layer characteristics, deployment and hardware features, end device transmission settings, LoRa MAC protocols, and application requirements. Discussion follows the presentation of each of the factors pinpointing the relevant research, and describing the impact of each one of them on the achieved network efficiency focusing especially on the capacity metric. Open issues and research directions are also highlighted for each of the five identified categories.
Conference Paper
Full-text available
With the constant improvement of electronics and development by research community, professionals and enthusiasts around the world, Internet of Things (IoT) based devices have seen a massive increase. These devices are now connected to our daily life in multiple ways and facilitate smooth operation of large, autonomous and semi-autonomous systems in different sectors. The communication among these systems needs to be done in a secure manner. However, as most of the IoT devices have very limited processing capability and energy source, all cryptography algorithms are not able to run on all devices. In addition, depending on the required data performance, it can be desirable to use one specific type of algorithm over others. In this paper, we analyze popularly used lightweight algorithms in terms of operational latency by running them on multiple widely used embedded modules. In addition, we measure power consumption while running an algorithm to realize its impact on battery life as an example. Finally, we discuss design-time considerations to help designers to select an appropriate cryptography algorithm for different applications.
Article
Full-text available
Humanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented.
Conference Paper
Full-text available
Increased automation and intelligence in computer systems have revealed limitations of Cloud-based computing such as unpredicted latency in safety-critical and performance-sensitive applications. The amount of data generated from ubiquitous sensors has reached a degree where it becomes impractical to always store and process in the Cloud. Edge computing brings computation and storage to the Edge of the network near to where the data originates yielding reduced network load and better performance of services. In parallel, new wireless communication technologies have appeared to facilitate the expansion of Internet of Things (IoT). Instead of seeking higher data rates, low-power wide-area network aims at battery-powered sensor nodes and devices which require reliable communication for a prolonged period of time. Recently, Long Range (LoRa) has become a popular choice for IoT-based solutions. In this paper, we explore and analyze different application fields and related works which use LoRa and investigate potential improvement opportunities and considerations. Furthermore, we propose a generic architecture to integrate Edge computation capability in IoT-based applications for enhanced performance.
Article
Full-text available
An experience of finding a vacant parking slot can be very stressful in densely populated areas, especially in peak hours. Such parking process takes a long time, wastes significant gasoline and emits extra vehicle exhaust that harms the environment. Smart parking, aimingto assist drivers in finding desirable parking slots more efficiently through Information and Communication Technologies (ICTs) such as Vehicle Ad Hoc Networks (VANETs), has received extensive attention recently. Current VANETsbased parking slot allocations can not provide a fully satisfactory solution, because vehicle communication devices – on-board units (OBS), and roadside units (RSUs) lack computational capabilities to perform humanized and accurate service provisioning, such as real-time parking slots information and probabilistic prediction on future parking slots. Therefore, we in this paper propose a fog computing based smart parking architecture to improve smart parking in real time. Fog nodes deployed at parking lots, cooperating with each other, enable real-time parking slot information provisioning as well as parking requests processing. The cloud center can further enhance smart parking capability by enforcing global optimization on parking requests allocation. The experimental results of our approaches show higher efficiency compared to other parking strategies. The proposed fog computing based smart parking can lower the average parking cost and minimize gasoline wastes and vehicle exhaust emission.
Article
Smart parking problems have received much attention in recent years. In literature, many smart parking allocation algorithms that considered the parking grid reservation and recommendation have been proposed. However, the parking policies for maximizing parking rate and benefits still can be improved. This paper proposes a smart parking allocation algorithm (SPA), which aims to maximize the benefits created by a given parking lot while guaranteeing the quality of parking services. The proposed SPA algorithm predicts the driver behavior and estimated parking traffic in the near future based on the historical parking records. These predictions help SPA better match the parking demands and the resource of available parking grids and hence improve the utilization and the created benefit of each parking grid. The proposed SPA applies three policies, namely worst-fit (WF-SPA), best-fit (BF-SPA), and parking behavior forecast (PBF-SPA), to allocate the available grids to the vehicles. Performance evaluations reveal that the proposed SPA outperforms exiting work in terms of accumulated parking rate and service quality and hence improves the benefits of a given parking lot.
Technical Report
The City Centre area is the rapidly expanding economic, social and cultural heartland of Auckland. Unfortunately, it is also where Auckland’s highest air pollution levels are observed. Narrow roads flanked by high buildings create deep street canyons that restrict ventilation of air pollutants such as nitrogen dioxide (NO2) and fine particulate matter 2.5 micrometres and smaller (PM2.5) resulting in levels which sometimes exceed national and international regulatory standards for air quality. This is despite an otherwise favourable geographical location that encourages a reliable airflow and has little long-range transportation of pollutants from neighbours. An awareness of the impacts of air pollution along with a recognition of Auckland’s climate change commitments has helped focus attention on the impacts of policy implementation on air quality. Key stakeholders require evidence to help guide strategy development that is consistent with the local climate, urban design and environmental goals. Policy decisions that promote safer streets, climate action, active and public transportation modes as well as congestion mitigation strategies have multiple and interdependent benefits that includes increased economic activity, vibrant social spaces and a cleaner, more sustainable environment, including cleaner air. All fossil fuelled vehicles degrade air quality to some extent. However, vehicles that run on diesel tend to release higher concentrations of air pollutants than those powered by petrol. Multiple studies have drawn attention to the relationship between the volume of bus traffic and elevated concentrations of NO2 and black carbon. Black carbon is a component of fine and ultra-fine particulate matter produced during diesel fuel combustion. These very small airborne particulates have been connected to chronic and acute health impacts worldwide and are a concern in an area of Auckland that has over 10 million pedestrians a year. Studies indicate that a key method of reducing air pollution within Auckland City Centre is to reduce emissions from buses and other large heavy goods and construction vehicles. Evidence provided in this report demonstrates multiple benefits to traffic calming, bus electrification and pedestrianisation of key streets across downtown Auckland that enhance proposed policy and planned changes while significantly reducing hazardous air pollutants in Central Auckland.
Article
In last two decades, various monitoring systems have been designed and deployed in urban environments, toward the realization of the so called smart cities. Such systems are based on both dedicated sensor nodes, and ubiquitous but not dedicated devices such as smart phones and vehicles’ sensors. When we design sensor network monitoring systems for smart cities, we have two essential problems: node deployment and sensing management. These design problems are challenging, due to large urban areas to monitor, constrained locations for deployments, and heterogeneous type of sensing devices. There is a vast body of literature from different disciplines that have addressed these challenges. However, we do not have yet a comprehensive understanding and sound design guidelines. This article addresses such a research gap and provides an overview of the theoretical problems we face, and what possible approaches we may use to solve these problems. Specifically, this paper focuses on the problems on both the deployment of the devices (which is the system design/configuration part) and the sensing management of the devices (which is the system running part). We also discuss how to choose the existing algorithms in different type of monitoring applications in smart cities, such as structural health monitoring, water pipeline networks, traffic monitoring. We finally discuss future research opportunities and open challenges for smart city monitoring.