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Studies in Systems, Decision and Control 242
Mohamed Elhoseny
Aboul Ella Hassanien Editors
Emerging Technologies
for Connected
Internet of Vehicles
and Intelligent
Transportation System
Networks
Emerging Technologies for Connected
and Smart Vehicles
Studies in Systems, Decision and Control
Volume 242
Series Editor
Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences,
Warsaw, Poland
The series “Studies in Systems, Decision and Control” (SSDC) covers both new
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on the state of the art and future developments relevant to systems, decision
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control spanning the areas of Cyber-Physical Systems, Autonomous Systems,
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Zentralblatt Math: MetaPress and Springerlink.
Editors
Emerging Technologies
for Connected Internet
of Vehicles and Intelligent
Transportation System
Networks
Emerging Technologies for Connected
and Smart Vehicles
123
Editors
Mohamed Elhoseny Aboul Ella Hassanien
Faculty of Computers and Information Department of Information Technology
Sciences Faculty of Computers and Information
Mansoura University Cairo University
Mansoura, Egypt Giza, Egypt
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Over the past decade, advances in vehicular communications and intelligent trans-
portation systems (ITS) have been aimed at trimming down the fuel consumption by
avoiding traffic congestion and enhancement of traffic safety while initiating a new
application, i.e., mobile infotainment. To address the individual requirements of both
safety and non-safety applications in the connected and smart vehicles field, there is a
need to build up a new communication technology for the integrated solutions of
vehicular communication and smart communications. The connected vehicles
infrastructure can be of various models such as vehicle-to-vehicle (V2V), vehicle-to-
infrastructure (V2I), and vehicle-to-everything (V2E). Due to the rapid growth in the
connected vehicles, many research constraints need to be addressed, e.g., reliability
and latency, appropriate scalable design of MAC and routing protocols, performance
and adaptability to the changes in environment (node density and oscillation in net-
work topology), and evaluation and validation of connected vehicles’ protocols under
the umbrella of coherent assumptions using simulation methodologies. In addition,
the information shared among connected vehicles is of great importance and it is not
yet clear what kind of privacy policies will be defined for the ITS networks. This book
aims to emphasize the latest achievements to identify those aspects of connected
vehicles and ITS networks that are identical to a traditional communication network in
the broader spectrum. In this book, we present the concepts associated with vehicular
communication systems and their applications in 11 chapters.
v
Contents
vii
viii Contents
M. Elhoseny (B)
Faculty of Computers and Information, Mansoura University, Mansoura, Egypt
e-mail: Mohamed_elhoseny@mans.edu.eg
K. Shankar
School of Computing, Kalasalingam Academy of Research and Education, Anand Nagar,
Krishnankoil 626126, Tamil Nadu, India
e-mail: shankarcrypto@gmail.com
1 Introduction
With the expansion in demand for information download among the vehicular clients,
control utilization, both at vehicle end and the roadside unit is additionally expanding
relatively by Shrivastava et al. [10]. An improved multicast based energy efficient
artful information scheduling algorithm. We gauge optimum data rate and an opti-
mum number of clients having great channel conditions, in this way deterring the
need to know the channel state data at the transmitter. The most testing features in
VANETs are their dynamic topology and versatility, where vehicles are moving at
variable high speeds and in various directions. Conversely, the test in the WSN is in
dealing with the constrained energy assets of the nodes, since the execution of WSNs
emphatically relies upon their lifetime by Mohaisen et al. [11]. To conquer these dif-
ficulties, this exploration researches the impacts of various Quality of Service (QoS)
Energy Efficient Optimal Routing for Communication … 3
The increase in traffic once a day is a major test for the general population of devel-
oping nations. Accordingly, the experts capable should concentrate on road security
to make the road traffic as efficient as could reasonably be expected [17–20]. Because
of IT advancement, the communication among vehicles over expansive spaces has
coordinated the consideration of scientists towards efficient road traffic the execu-
tives [21]. Information broadcasting from such a large number of sources with the
limitation of opportune and conveyance of message causes blockage issue in vehicu-
lar communication, in this way guaranteeing packet dropping, low energy efficiency
and broadened delay [22–24]. The proposed research work has been committed to
the optimal routing structure in an energy-efficient way by optimizing the energy
consumption parameter.
3 System Model
This model we will analyze the Vehicular communication framework by energy effi-
cient routing process, it’s solved by the help of optimization. Essentially, build up the
vehicle network topology for V1R communication or V2V communication model.
Before finding the energy efficient routing, clustering and cluster head selection is
performed, the detail examination of our inventive model intricately talked about in
the underneath area.
4 M. Elhoseny and K. Shankar
4 Network Topology
The network topology of networks can be viewed as a subset of the city map and the
developments of vehicles are confined along the streets and by the traffic conditions.
Here we have utilized 200 vehicle nodes, the ordinary nodes portrayed in gray color
trade data among one another without GPS. Be that as it may, so as to have a decent
knowledge of position data of the entire network, a couple of reference nodes are
outfitted with GPS reliable and low-control communications are simultaneously con-
sidered and examined in light of the impedance of the jammers. The road separates
the plane into two sections. The source node and destination node are on either side
of the road, separately; the systematical model is exhibited in Fig. 1.
A few clustering strategies for VANET have been proposed in the literature. The
cluster individuals and utilize the VANET clustering systems to frame the clusters,
Energy Efficient Optimal Routing for Communication … 5
none of them took speed contrast into thought for clusters arrangement in VANET.
Here we are utilized K-medoid clustering model to clusters the 200 vehicle nodes,
from the group with fewer individuals are rejected and its cluster head joins the
neighboring group, while different individuals begin group development process in
the event that they can’t join any close-by groups.
This clustering model chooses “k” information things as beginning medoids to rep-
resent the k clusters. The various residual things are incorporated into a cluster which
has its medoid nearest to them. From that point, another medoid is resolved which can
represent the cluster better. K clusters are framed which are focused on the medoids
and every one of the information individuals is put in the proper cluster dependent
on closest medoid [15].
Initialization: Chooses K value of the n data points as the medoids
Medoids Selection: The medoid value is chosen by evaluating the distance
between every two data points of all considered objects. Here, the distance mea-
sure is calculated by
n
Dist = (Fi − Si )2 (1)
i=1
The determination criteria for the formation of cluster head mostly rely on the porta-
bility measurements (speed, position, and acceleration) of vehicle nodes.
6 M. Elhoseny and K. Shankar
• A vehicle with the smallest average distance among a cluster is picked as the
cluster head.
• Closest Position to Average: A vehicle endeavors to pick as its cluster head arranged
by the absolute difference of candidate’s position to the normal position of every
single proximal vehicle.
• Closest Velocity to Average: A vehicle endeavors to pick as its cluster head arranged
by the absolute difference of candidate’s velocity to the average velocity of every
single proximal vehicle.
Energy Efficient Optimal Routing for Communication … 7
We explore the optimal routing path configuration in traffic areas by considering the
per-node most extreme energy effectiveness. Here, the energy efficiency between
vehicular nodes is improved by the AODV protocol. The optimal energy efficient
routing way is chosen by the parameter as minimum energy utilization.
velocity of k-th individual. In food source and enemy, V − indicates the position of
the enemy, V + indicates the position of food source.
(iii) Updation process: To update the position of artificial dragonflies in an inquiry
space and reproduce their developments, two vectors are considered: step (D) and
position (V). The progression vector closely resembles the speed vector in Parti-
cle Swarm Optimization (PSO), and the DA algorithm is created dependent on the
framework of the PSO algorithm. The progression vector demonstrates the course
of the development of the dragonflies and characterized as pursues:
where s signifies the separation weight, a signifies the alignment weight, c is the
cohesion weight, f signifies the food factor, e signifies enemy factor, w signifies
inertia weight, t shows iteration count.
The position vector can be calculated as
Vt+1 = Vt + M p Vt (6)
The neighborhood area is expanded and at last, at the conclusive period of the opti-
mization process, the swarm becomes just a single gathering. Food source and the
enemy are chosen from the best and the most exceedingly bad arrangements got
in the entire swarm at any moment. This leads the assembly towards the promis-
ing locales of pursuit space and in the meantime, it leads dissimilarity outward the
non-promising territories in inquiry space. The flow diagram of the EDA is shown
in Fig. 3.
Energy Efficient Optimal Routing for Communication … 9
In the result analysis part, the performance of the K-medoid clustering and optimiza-
tion model is investigated by examining the network parameters in terms of energy
consumption, packet delivery ratio, network lifetime and throughput. The execution
10 M. Elhoseny and K. Shankar
of the proposed study is analyzed with the help of Network Simulation Tool-Version
2 (NS2). The simulation results of the proposed work are described in this section.
(a) (c)
(b) (d)
compared. At long last, the diagram finishes up the proposed methodology achieves
the most extreme lifetime in the V2V communication.
At whatever point the node transmission control is more, at that point, the num-
ber of bounces in between source to destination will be generally less. This thusly
results in diminished control overhead and expanded feasible throughput. In any case,
expanded node control utilization may lead to impedance with part nodes which
results in packet loss because of crashes. So as to lessen the energy utilization of
V2V communication between vehicle nodes, we proposed k-medoid clustering with
EDA optimization. Figure 4d demonstrates the energy utilization rate of 50, 100, 150
and 200 vehicle nodes based communication. Contrasted with existing advancement,
K-medoid clustering with EDA expends the least energy.
The percentage analysis of EC for a number of vehicle nodes is illustrated in
Fig. 5. For 25 numbers of vehicles, k-medoid + DA consume 83% energy in data
transmission and k-medoid + EDA consumes 49%. Similarly, 50, 75, 100, 125,
150, 175 and 200 numbers of vehicles are illustrated in the figure. The bar graph
concludes that the proposed approach (k-medoid with EDA) achieves minimum
energy consumption compared to k-medoid + DA approach.
12 M. Elhoseny and K. Shankar
6 Conclusion
This paper introduced the efficient node identification of VANETs considering with
energy utilization factor at least. This goal was accomplished by the clustering algo-
rithm, for example, K-medoid algorithm along with optimization. It groups the vehi-
cle nodes in various adjusts and choosing any nodes as cluster heads in certain rounds,
it has the capacity to diminish the number of transmitted messages from every node
to various nodes and to the base station, saving more energy in the network. At that
point, the energy efficient route for V2V communication was obtained among the
clustered nodes in VANETs by the advancement of network parameter (EC) utiliz-
ing the EDA algorithm. From the simulation examination, the proposed algorithm
k-medoid + EDA give the minimum energy consumption, contrasted with the k-
medoid + DA method. Later on, we can also broaden this work by improving energy
efficiency regarding limited expense and maximized QoS by proposing progressively
efficient clustering and optimization strategies.
References
1. Al-Mayouf, Y.R.B., Abdullah, N.F., Ismail, M., Al-Qaraawi, S.M., Mahdi, O.A., Khan, S.:
Evaluation of efficient vehicular ad hoc networks based on a maximum distance routing algo-
rithm. EURASIP Journal on Wireless Communications and Netw. 2016(1), 265 (2016)
2. Rehman, O., Ould-Khaoua, M.: A hybrid relay node selection scheme for message dissemina-
tion in VANETs. Future Gener. Comput. Systems 93, 1–17 (2019)
3. Huang, M., Yang, B., Ge, X., Xiang, W., Li, Q.: Reliable energy-efficient routing algorithm for
vehicle-assisted wireless ad-hoc networks. In: 2018 14th International Wireless Communica-
tions & Mobile Computing Conference (IWCMC), pp. 1219–1224. IEEE (2018)
4. Lee, J.H., Moon, I.: Modeling and optimization of energy efficient routing in wireless sensor
networks. Appl. Math. Model. 38(7–8), 2280–2289 (2014)
5. Kakhandki, A.L., Hublikar, S.: Energy efficient selective hop selection optimization to maxi-
mize the lifetime of the wireless sensor network. Alex. Eng. J. 57(2), 711–718 (2018)
Energy Efficient Optimal Routing for Communication … 13
26. Elhoseny, M., Farouk, A., Zhou, N., Wang, M.-M., Abdalla, S., Batle, J.: Dynamic multi-hop
clustering in a wireless sensor network: performance improvement. Wirel. Pers. Commun.
95(4), 3733–3753
27. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H., Riad, A.: Balancing energy consumption
in heterogeneous wireless sensor networks using genetic algorithm. IEEE Commun. Lett. IEEE
19(12), 2194–2197 (2015)
Mobility and QoS Analysis in VANET
Using NMP with Salp Optimization
Models
Abstract Wireless Sensor Networks (WSN), the essential thing is to beat path selec-
tion and duration of VANET framework by most extreme mobility as well as trans-
mission rate. In light of this mobility and connectivity charts changes in all respects
as often as possible and it influences the execution of VANETs. Because of the
attributes of VANET, for example, self-association, dynamic nature,and quick mov-
ing vehicles, routing in this network is an impressive test. This chapter discussed the
mobility, just as Quality of Service (QoS) is VANET communication process, Here
we are utilized Network Mobility Protocol (NMP) for routing reason, Moreover,
the extensive optimization procedure is Salp Swarm Optimization (SA). It’s able to
improve the initial random solutions viably and merge towards the optimum, Based
on optimization procedure to get an optimal path with a minimum delay from source
to destination. From this NMP-SA model improve the high mobility of nodes. The
simulation results demonstrate better QOS parameters contrasted with other similar
optimization Models.
1 Introduction
In the ad hoc network, nodes are conveying straightforwardly to one another without
utilizing any passage. The variable node thickness, high node mobility, and unusual
and cruel communication condition [1]. Several routing protocols were presented
to use network assets and improve routing effectiveness in VANETs [2]. Be that
as it may, the vast majority of these protocols still have deficiencies when meeting
the QoS [3] prerequisites and in ensuring the stability of network topology at the
time of routing procedure [4]. In these circumstances, the most critical necessity
is the transmission of data from source to objective with a specific dimension of
QoS. The QoS implies the transmission of information from source to objective with
minimum delay and minimum overhead [5]. Since portable vehicles are allowed to
move arbitrarily, vehicle mobility is a standout amongst the most essential issues in
protocol structure [6]. The impacts of vehicle mobility on traffic flow control, routing
way determination, portable channel appointing, control overhead estimation. The
routing is subject to the protocols being utilized for routing in the network [7, 18]. If
there should be an occurrence of VANET, execution of these routing protocols relies
on different situations like urban and the highway. The routing protocols which are
position based are normally viewed as all around coordinated if there should be an
occurrence of vehicular setting [8, 19–21]. VANETs present a difficult nature for
protocol and request plan because of their low dormancy and raised information rate
necessities in a raised mobility condition [9, 22].
For upgrading the mobility model the optimization will be utilized, optimiza-
tions methods like Salp Swam Optimization (SSO) evolutionary computation [8]
and Particle Swarm Optimization (PSO) [9], and so on. Some of them are nearby
inquiry-based and others (PSO, GA, ACO) are global search based heuristics [10].
Our proposed slap swarm model to get the best way with maximum number data
transmission process over VANET Network [11].
2 Related Works
In 2015 Abdul Halim et al. [12], has been proposed the network optimization is one
approach to keep up the current protocols and other network parameter contrast with
structure and actualizing new improved protocols in which it excessively expensive.
The need for optimization of the vehicular network is to achieve throughput, end-to-
end delay and packet delivery ratio utilizing the Taguchi technique.
Grey wolf optimization based clustering calculation for VANETs is proposed, that
imitates the social conduct and hunting system of grey wolfs for making effective
clusters by Fahad et al. in 2018 [5]. The proposed technique is contrasted with well-
known meta-heuristics from existing research papers and results demonstrate that it
gives ideal results that lead to a vigorous routing protocol for grouping of VANETs,
Mobility and QoS Analysis in VANET … 17
which is suitable for highways and can achieve quality communication, affirming
dependable conveyance of data to every vehicle.
QoS-Aware Routing Protocol for VANETs called QoS-Aware Routing in VANETs
(QARV) in which packets achieve the goal while satisfying the QoS (Kaur et al. 2018)
[13]. This protocol works in the interstate situation. The new idea called Terminal
Intersection idea is utilized in these two protocols so as to decrease the congestion and
reduce the ideal opportunity for investigating the route. Two bio-roused calculations,
Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO), are utilized
to accomplish the outcomes.
In 2013 Raw et al. [14], path duration can be utilized to anticipate the behavior of
the versatile nodes in the network. Estimation of the path duration in VANETs can
be a key factor to improve the execution of the routing protocol. Estimation of way
length is a provoking assignment to execute as it relies upon numerous parameters
including node density, transmission range, quantities of hops, and speed of nodes.
In the road, since each vehicle is moving a fixed way with high moving pace,
the vehicle embracing our protocol can gain IP address from the VANET through
the vehicle to vehicle communication by Chen et al. in 2009 [15]. The vehicle can
depend on the help of the front vehicle to execute the pre-handoff system or it might
gain its new IP address through multi-hop transfers from the vehicle on the paths of
the equivalent or inverse course and in this manner lessens the handoff delay.
3 Mobility Analysis
Mobility models usually focus on the individual moving behavior between mobility
ages. Here, an epoch is considered as brief timeframe, in which both moving speed
and moving bearing of vehicles are roughly considered as consistent [23]. Mobility
management is basic for high-speed and consistent administrations for vehicular
networks since vehicle nodes change their purposes of connection much of the time
and net-work topology can change suddenly. Mobility in VANET communication
having a few necessities [17]:
Fast Vertical handover: This prerequisite is required for delay-sensitive appli-
cations, similar to wellbeing, to stay away from the accidents. In a heterogeneous
wireless condition, vertical handover of the portable client’s associations among var-
ious wireless innovations must be upheld to accomplish consistent administration
[24].
Communication support: Data transmission range of the mobile nodes is to
achieve the destination. Mobility the management plans for vehicular networks need
to consider the multilevel communications necessities [25].
The efficiency of VANET: High frequency of change of the purpose of con-
nection, the mobility management plot must be versatile and productive to help
distinctive sorts of traffic [26].
Location Management: With novel mobility attributes of VANETs, fundamental
routing protocols can’t be legitimately connected to VANETs because of the large
18 K. Shankar et al.
latency and overhead. The upgrade from geological clustering was utilized for the
two areas refreshing and questioning to improve scalability. For territory awareness,
neighborhood look was utilized to find the goal node [27].
The mobile model examined with the accompanying suspicion that is the relative
moving rate and the relative moving direction between two versatile vehicles are
considered as two free irregular factors. At that point, the vehicle moving speed
possibly haphazardly changes when inter-vehicle link changes. For this mobility, the
termination time determined by:
ET
a∈N(b)
ET(a, b) = (1)
|N(b)|
As it were, mobility models depict the example of the development of vehicles, and
how their area, speed and acceleration change after some time. The execution of
any protocol is predominantly controlled by the mobility patterns of vehicle, it is
attractive to recreate the vehicle development of focused genuine applications in a
sensible manner.
Sensitive data are exchanged between the optimal route nodes. These nodes can
communicate with one another through different ways utilizing direct connections
within the transmission range or utilizing middle nodes to exchange data packet
from source to goal, the mobility and thickness of nodes changes with time and area.
For this routing model imaginative protocol will be utilized, the explanation of this
protocol is discussed in underneath segment.
Our epic work, Network Mobility Protocol (NMP) utilized, it’s an idea and the
property of multi-hop VANETs. Every vehicle is outfitted with a mobile router,
which is furnished with two communication interfaces, including the Wi-MAX and
Wi-Fi interfaces. When it scopes to goal, it stays static for the predefined stop time
and moving again as indicated by the same principle. The mobility behavior of nodes
especially relies upon the stops time and most extreme speed of nodes. This mobility
protocol the procedure determined by
Mobility model, the acceleration is input, to ascertain nodes next speed the accel-
eration of Current node. δ It implies that the node is moving with de-acceleration
(negative increasing speed) Otherwise, it moves with positive acceleration.
Pros of NMP
• Router Free Connection to the web with no wireless switch is the primary preferred
standpoint of utilizing a mobile ad hoc network.
• The control of the network is disseminated among the mobile nodes of the network
as there is no foundation network for the principle control of the network activity.
• It lessens inordinate memory prerequisites and the NMP decreases the route rep-
etition.
• This protocol is the constraining the number of encapsulation and passages two
along these lines diminishing the packet delivery delay. The number of encapsu-
lation and passages in intra-NMP routing is zero.
A new route or path must be set up for further communication once a path failure
occurs. Expanding the proficiency of execution of VANETs learning of the path dura-
tion can help extraordinarily. For the optimal route determination swarm based opti-
mization used, the purpose behind choosing the optimal path is to improve mobility
and QoS factors. Notwithstanding the distinction between developmental and swarm
knowledge systems, the basic is the improvement of one or a lot of solutions at the
process of optimization. The speed and the direction of the node determined by the
Current speed and direction of the mobile node are dependent on the past speed
and direction values. In view of the speed vehicle mobility in a simulation considers
the probability appropriation capacities covering both moving direction and speed
consistently as the recreation advances. The estimations of speed and direction for
development in the period; β is the consistent incentive in the range [0, 1].
Salps have a place with the group of Salpidae and have a straightforward barrel-
formed body. Their tissues are exceedingly like jelly fishes. They likewise move
fundamentally the same as jellyfish, in which the water is pumped through the body
as the drive to push ahead [16]. To scientifically show the salp chains, the populace
is first isolated to two gatherings: leader and followers. The leader is the salp at the
Mobility and QoS Analysis in VANET … 21
front of the chain, while the remainders of salps are considered as followers and this
flowchart appears in Fig. 2. The Updating state of Salp Algorithm (SA) is
gj + W1 Hj − Lj W2 + Lj W3 ≥ 0
Oj =
1
(5)
gj − W1 Hj − Lj W2 + Lj W3 < 0
Here,O1j signifies the position of the first salp (leader) in jth dimension, the posi-
tion of the food source in jth dimension is symbolized as f i ; the upper bound and
lower bound is indicate as Hj and Lj ; W1 , W2 , and W3 symbolizes random number
randomly generated in the interval of [0,1].
To update the position of the supporters, Newton’s law of movement is used. The
inconsistency between iterations is equivalent to 1, and considering the underlying
speed as 0, this condition can be communicated as pursues:
Salp chain is very liable to find a superior solution by exploring and exploiting the
space around it. In this manner, the salp chain can possibly move towards the global
optimum that changes through the course of iterations.
Optimal Path Selection: From the above-Updated method the better path will
be chosen, to get the most extreme Transmission rate, so the target characterized as
In light of the optimization model, we are ascertaining the mobility by NMP, optimal
route for information dispersal in inadequate just as a dense network of vehicles.
For this, we chose to pick a course that minimized the probability of the event of
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