Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and Solutions

Abstract

The Vehicular Ad-hoc Network (VANET) represents a new future for dynamic information dissemination between societies. VANET has a wide range of applications in a variety of aspects, including Intelligent Transportation Systems (ITS). VANET has some characteristics, like highly dynamic topology and intermittent connections. These characteristics lead to untrustworthy information transmission in VANET. Vehicle clustering is an efficient approach to improve the scalability of the network and connection reliability. The performance of the clustering is also affected by VANET characteristics. This article provides a comprehensive description of VANET clustering algorithms. The most notable clustering algorithms introduced between 2010 and 2021 are reviewed. A complete survey on clustering in VANETs is provided based upon the clustering process. The clustering process in most algorithms is explored in the aspects of CH selection metrics, cluster formation, and cluster maintenance. The clustering techniques based on some parameters like stability, convergence, overhead, and latency are compared. Some of the most common problems, as well as the approaches employed to solve them, are also discussed. Also, the performance parameters which evaluate the clustering approaches are summarized.

Full text

Paper—Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and…
Clustering Review in Vehicular Ad hoc Networks:
Algorithms, Comparisons, Challenges and Solutions
https://doi.org/10.3991/ijim.v16i10.29973
Mays Kareem Jabbar1,2(), Hafedh Trabelsi1
1CES_Lab, ENIS, Sfax University, Sfax, Tunisia
2Faculty of Engineering, University of Misan, Misan Province, Iraq
m_mays85@uomisan.edu.iq
Abstract—The Vehicular Ad-hoc Network (VANET) represents a new future
for dynamic information dissemination between societies. VANET has a wide
range of applications in a variety of aspects, including Intelligent Transportation
Systems (ITS). VANET has some characteristics, like highly dynamic topology
and intermittent connections. These characteristics lead to untrustworthy infor-
mation transmission in VANET. Vehicle clustering is an efcient approach to
improve the scalability of the network and connection reliability. The perfor-
mance of the clustering is also affected by VANET characteristics. This article
provides a comprehensive description of VANET clustering algorithms. The most
notable clustering algorithms introduced between 2010 and 2021 are reviewed.
A complete survey on clustering in VANETs is provided based upon the cluster-
ing process. The clustering process in most algorithms is explored in the aspects
of CH selection metrics, cluster formation, and cluster maintenance. The cluster-
ing techniques based on some parameters like stability, convergence, overhead,
and latency are compared. Some of the most common problems, as well as the
approaches employed to solve them, are also discussed. Also, the performance
parameters which evaluate the clustering approaches are summarized.
Keywords—VANET, clustering algorithms, performance parameters
1 Introduction
An important next-generation transportation technology is the Intelligent Transpor-
tation System (ITS), which includes all types of vehicle communications. ITS provides
a variety of services to passengers including driving assistance, safety applications,
emergency warnings, congestion control, and so on [1]. A VANET is a self-organizing
network made up of moving vehicles. Because of the expanding number of applica-
tions aimed at passenger safety, VANET is garnering a lot of interest from wireless
network manufacturers and academics. VANET is a subset of Mobile Ad Hoc Network
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Paper—Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and…
(MANET). MANET is a network of mobile nodes connected via wireless communi-
cation that does not have a xed infrastructure and is self-conguring. The network
becomes a VANET when the mobile nodes in MANETs are exchanged by cars and then
begin to follow xed routes, like roads. The major advantage of VANET is to provide
congestion control and road safety to the vehicles by making communication among
them to share the information. The average speed and mobility of nodes in VANET are
quite high, resulting in a rapid change in the network structure; these are its distinguish-
ing features [2]. In the early 1990s, people began to pay greater attention to VANET
technologies, and it has grown in importance in subsequent years.
VANET’s components are; Road side units (RSUs) and On-Board Units (OBUs).
The RSUs are put alongside the road, and saved all information of the vehicle then
forwarded to other OBUs. RSUs have complete control over the transmission of infor-
mation jobs in OBUs or vehicles. Moreover, OBUs are devices that are installed in
dynamic vehicles to facilitate information sharing between the cars and RSUs.
Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication are the
two types of VANET communications. In V2V communications, Vehicles equipped
with OBUs can communicate directly with each other inside their radio ranges, whereas
V2I communication, as well as the deployment of infrastructure along roadsides and
the various applications that can increase the quality of service provided by infrastruc-
tures to vehicles. V2V and V2I can be included as Vehicle to X (V2X) communications.
It is the communications between vehicles and communications between vehicles and
other terminals, such as RSUs [3], [4].
A Dedicated Short-Range Communication (DSRC) is a communications system
designed specically for use in automobiles. It is proposed for transmission informa-
tion and communication among vehicles with a transition range of 100 to 1000m [3].
The DSRC system works in a similar way to how Wi-Fi works. The Federal Commu-
nication Commission (FCC) of the United States has assigned a higher spectrum band
with a range of 75 MHz [5], [6]. Both V2V and V2I communications are supported
by DSRC.
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Paper—Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and…
Fig. 1. Vehicular communications types [6]
Vehicle safety, trafc management, and accurate vehicle information communica-
tion are the main functions of VANET. The topology of VANET is dynamic and can
be anticipated using GPS because of the high speed of vehicles. In order to supply ad
hoc connectivity in VANET, a wireless communication facility equips in the vehicles.
Scalability issues are caused by the dynamic nature of the VANET. In VANET architec-
ture, data distribution requires an effective and efcient routing strategy. The VANET
environment is evolving to provide vehicle safety and security [7], [8]. Cluster stabil-
ity is important for the VANET’s reliability and scalability, as it guarantees minimal
intra- and inter-cluster communication, lowering the overhead associated with these
issues [6]. To achieve the best information of communication in VANET, the most
recent clustering algorithms are described. We also concentrate on VANET’s intelli-
gent clustering algorithms. This leads us to explore different clustering strategies. This
study is an extended survey for the previous study which has introduced in [6]. The
main contributions of this work; rstly, we provide an overview of the development
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of clustering algorithms in VANETs from 2010 to 2022, which have been observed
and studied. Also, most of these algorithms have never been summarized in previous
research. Secondly, the existing clustering techniques are summarized and classied in
terms of clustering procedure: CH selection, cluster formation, and cluster maintenance,
and then we compare these algorithms using different key parameters. Thirdly, different
challenges are introduced and the techniques used to solve them. Finally, a compre-
hensive analysis of the most common parameters used for evaluating the performance
of clustering algorithms is introduced. The performance parameters are cluster perfor-
mance parameters and network performance parameters. Also, simulation tools of each
clustering algorithm are presented. The following is our study’s structure: Section 2
focuses on VANET clustering, including its algorithms, history, and process, followed
by a comparison of clustering algorithms based on some key parameters. Section 3
discusses various problems and the clustering approaches utilized to resolve them, as
well as each cluster technique performance. In Section 4, we describe the performance
evaluation parameters for some clustering techniques. The survey’s conclusion and our
future work are reported in Section 5.
2 Clustering in VANET
Clustering is a common VANET technology that offers an appealing approach for
simplifying and optimizing network functions and services. When compared to the
traditional at structure, it has dramatically improved performance in a variety of appli-
cations. Clustering is a technique for organizing network nodes into small groupings
called clusters. Typically, vehicles in close proximity are grouped together in a cluster
based on various key parameters and metrics. The vehicles present in the cluster are
known as [9]:
1. Cluster Head (CH) – This is the node that is the coordinator or head of the cluster.
The CH is selected according to different criteria and its main task is allowing cluster
members to communicate and share information with other members and CHs.
2. Cluster Member (CM) – The remaining nodes in the cluster are the CMs. These
nodes exchange information by broadcasting messages to each other.
3. Gateway Node (GW) – This node helps to communicate with RSU, it does not need
to present it to every cluster.
Figure 2 illustrates the VANET’s cluster-based communication structure. Internal
cluster communication is handled entirely by the CH. There are two specic rout-
ings that divide a cluster internal communication; intra-cluster communication and
inter-cluster communication. The cluster’s stability is increased during cluster mainte-
nance by forecasting node-to-node failure links [6].
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Paper—Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and…
Fig. 2. The cluster architecture [10]
2.1 VANETs clustering algorithms history
In the early 1990s, the clustering techniques for VANETs began to be developed and
have expanded in recent years.
Researchers discovered that prior clustering algorithms in MANETs were no longer
appropriate for VANETs due to their predictable mobility and specied route topology.
Additional control overheads may be imposed due to the time it takes to complete
the clustering phases. As a result, a good clustering method should construct a small
number of clusters and dynamically maintain the cluster structure without creating sig-
nicant network overhead. Furthermore, to avoid wasteful cluster re-formations, an
effective cluster maintenance plan is required. Some clustering algorithms of MANETs
were introduced to t the specic characteristics of vehicular communications such as
Mobility Based Clustering (MOBIC) in [11], Weighted Clustering Algorithm (WCA)
in [12], and Distributed and Mobility Adaptive Clustering (DMAC) in [13]. Also, most
of the VANET clustering algorithms were derived from the previous MANETs. Several
clustering techniques for VANETs have been proposed, particularly after 2010 as a
result of the expansion and development of the VANET. In Table 1, several VANET
clustering algorithms and the number of citations for each algorithm are highlighted,
which have been presented from 2010 to 2022. We can note that the Passive Multi-hop
Clustering (PMC) in [14] has the highest mean citations.
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Table 1. Clustering algorithms citations
Reference Year Algorithm Abbreviations Citation Mean
[15] 2010 Aggregate Local Mobility ALM 127 10.6
[16] 2010 Cluster-Based Directional Routing Protocol CBDRP 68 5.7
[17] 2010 Proposed in [17] – 104 8.6
[18] 2011 Vehicular clustering based on the Weighted
Clustering Algorithm
VWCA 141 12.8
[19] 2012 Distributed Medium Access Control DMMAC 7 0.7
[20] 2012 Fuzzy Logic Based clustering Algorithm FLBA 78 7.8
[21] 2012 Trust dependent Ant Colony Routing TACR 49 4.9
[22] 2012 Threshold Based algorithm TB 119 11.9
[23] 2012 Mobility-Aware Clustering Algorithm based
on Destination positions
AMACAD 74 7.4
[24] 2012 Spring-Clustering SP-Cl 53 5.3
[25] 2012 Stability-Based Clustering Algorithm SBCA 57 5.7
[26] 2013 Agent Learning–based Algorithm ALCA 77 8.6
[27] 2014 Adaptive K-Harmonic Means AKHM 21 2.6
[28] 2015 Aggregate Relative Velocity ARV 31 4.4
[29] 2015 Distributed Multi-hop Clustering based on
Neighborhood Follow
DMCNF 94 13.4
[30] 2015 Adaptive Weighted Clustering Protocol AWCP 54 7.7
[31] 2015 Neighbor Mobility-Based Clustering
Scheme
NMCS 11 1.6
[32] 2015 Direction based clustering and multi-
channel medium access control
DA-CMAC 11 1.6
[33] 2016 Vehicular Multi-hop algorithm for Stable
Clustering-LTE
VMaSC-LTE 255 42.5
[34] 2016 Neighbor stability-based VANET clustering
algorithm
NSVC 24 4
[35] 2016 MObility-aware and SIngle-hop Clustering
scheme
MOSIC 10 1.7
[36] 2016 New Clustering Algorithm Based on Agent
Technology
NCABAT 9 1.5
[37] 2016 Clustering-Based VANET Routing
algorithm Protocol
CBVRP 28 4.7
[38] 2018 Proposed [38] – 12 3
[39] 2018 Deep Reinforcement Learning DRL 17 4.25
[40] 2018 Unied Framework of Clustering approach UFC 62 15.5
[14] 2018 Passive Multi-hop Clustering PMC 180 45
[41] 2018 Link Reliability-based Clustering Algorithm LRCA 29 7.25
[42] 2018 Proposed in [42] – 33 8.25
[43] 2018 Proposed in [43] – 26 6.5
(Continued)
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Reference Year Algorithm Abbreviations Citation Mean
[44] 2019 Enhanced Weight-based Clustering
Algorithm
EWCA 16 5.3
[45] 2019 Center-Based Clustering algorithm CBSC 35 11.6
[46] 2019 Hybrid Clustering Algorithm based on
Roadside
HCAR 16 5.3
[47] 2019 Double-Head Clustering DHC 36 12
[48] 2019 Proposed in [48] – 1 0.33
[49] 2019 Enhanced Distributed Channel Access EDCA 6 2
[50] 2019 Probabilistic-Direction-Aware Cooperative
Collision Avoidance
P-DACCA 13 4.3
[51] 2019 Fuzzy-based Cluster Management Scheme FCMS 23 7.7
[52] 2019 Mobility Based Clustering Algorithm MBCA 2 0.7
[53] 2020 Naive Bayes Prediction Scheme NBP 1 0.5
[54] 2021 Junction-based Clustering for VANET JCV 0 0
[55] 2022 Proposed in [55] – 3 3
[56] 2022 Region-based Collaborative Management
Scheme
RCMS 0 0
2.2 Clustering process in VANETs
The cluster establishment in the VANET communication process is the most import-
ant part. There are two phases to complete this process:
• First phase- (Cluster Generation): cluster formation process and CH selection
process; during this phase, nodes send advertisement messages to pick the primary
CH and CM, and subsequently regular data packets are transmitted between them.
In order to create a stable cluster, there may be a few techniques added between
the advertisement message transmission and CH selection.
• Second phase- (Cluster Maintenance): Stable cluster merging, selection of second-
ary CH, re-clustering, and cluster splitting occur at this phase.
Some researchers in the literature had discussed these phases separately. This section
provides an overview of the algorithms and criteria used in each clustering step, includ-
ing CH selection, cluster formation according to the hop count, and cluster maintenance.
Cluster generation phase. This phase goes through two processes to complete the
generated of clusters; the cluster formation process and the CH selection process. Some
clustering algorithms elect the CHs rst, on the basis of which the clusters are formed
to complete the clustering process and others vice versa.
Cluster head selection. The network’s robustness and scalability are strongly
inuenced by CH stability. The stable CH guarantees that intra- and inter-cluster
communications are kept. To improve VANET stability, a reliable vehicle only can
be a CH. The researchers considered various parameters for selecting the CH, such as
received signal strength, relative speed, position, direction, and link lifetime. Many
clustering approaches are relying on a combination of multiple metrics rather than a
Table 1. Clustering algorithms citations (Continued)
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single metric for selecting the CH, like DHC in [47], CBSC in [45], TCAR in [21],
AWCP in [30], and EWCA in [44]. Some of these algorithms and their metrics used for
CH selection are tabulated in Table 2:
Table 2. CH selection metrics
Reference Algorithm CH Selection Metric
[15] ALM Priorities associated with each vehicle
[16] CBDRP Moving direction
[18] VWCA Distrust Level, Degree, Velocity, Direction
[19] DMMAC Speed and direction based
[20] FLBA Relative velocity
[21] TACR Relative velocity, packet forwarding reputation
[22] TB Distance, Relative velocity
[23] AMACAD Destination, Distance, Relative velocity
[24] SP-Cl Relative velocity, distance
[25] SBCA Relative Speed, RSS
[26] ALCA Velocity
[27] AKHM Transmission bandwidth
[28] ARV Relative velocity
[29] DMCNF The propagation delay ratio, Number of the following car
[30] AWCP Highway ID, direction, position, speed
[31] NMCS Change in degree
[33] VMaSC-LTE Lowest average speed
[34] NSVC Rate of change of the number of neighbors
[35] MOSIC Relative speed, Relative distance, and Relative mobility
[36] NCABAT Lowest ID
[38] Proposed in [38] Speed, Position
[39] DRL Q-learning based routing
[40] UFC UFC relative position, relative velocity, and link lifetime
[14] PMC Speed, Neighbors, Link lifetime, Position
[41] LRCA link reliability
[42] Proposed in [42] Mobility, direction, degree, and reputation
[44] EWCA Speed, Position
[45] CBSC Position, Relative Speed
[46] HCAR Lowest ID
[47] DHC Signal Strength, Relative Speed, Link Lifetime
[48] Proposed in [48] Trust, relative speed, and position
[54] JCV Relative position, movement at the junction, degree of a node, and
time.
[55] Proposed in [55] Using PSO mechanism.
[56] RCMS Using SRP model and feature relevance between vehicles
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Cluster formation. Cluster formation in VANET has various types and categories:
center-based vs. distributed-based, single-hop vs. multi-hop, location service-based vs.
user information-based, etc. This section discusses cluster formation on the basis of
topology. This means a cluster structure in VANETs can be modeled according to a hop
distance that separates the CH and its members, transmission range, and cluster radius.
Accordingly, only two main categories of algorithms are distinguished: single-hop and
multi-hop algorithms as in Figure 3.
Fig. 3. Clustering model [10]
1. Single-hop Clustering Algorithm
It is the algorithm that creates clusters with a single-hop distance between each node
and its CH. That means every node connects directly with the CH [55]. Many clustering
algorithms form directly single-hop clusters based on the transmission range of the CH
or the limited cluster radius. Some of the single-hop clustering algorithms are:
In [18], the VWCA was proposed. It is a single-hop clustering algorithm, and it
improves the security, connectivity, and stability performance. The connectivity can be
increased using the adaptive transmission range algorithm (AART) which is based on
detected short-range communication standards. The AART helps to extend the trans-
mission range dynamically from 100m to 1000m based on the vehicles’ density.
Another single-hop clustering algorithm for VANETs was proposed in [23],
it is called an AMACAD. Speed distance and position are the parameters used to elect
the CH.
The stability of the CH in VANET was enhanced by designing a SBCA [25]. This
algorithm is provided a more stable structure according to vehicle mobility and a num-
ber of neighbors. The cluster formation procedure does not take the vehicle’s direction
into account, which has an impact on the VANET system’s performance.
In [31], the authors proposed a novel single-hop clustering algorithm for VANET
named NMCS. The vehicle which has the lowest “neighbor vehicles mobility” value is
selected as a CH. This algorithm provides reliable network topology.
MOSIC was proposed in [35]. Gauss Markov mobility (GMM) model is used by this
proposed for mobility prediction that makes vehicle able to prognosticate its mobility
relative to its neighbors.
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NCABAT was introduced in [36]. The objective of this algorithm is to describe agent
properties to vehicles with the purpose of improving traditional schemes in terms of
performance.
Creating a stable cluster is the main goal of a distributed system-based passive data
dissemination technique [38] that can overcome the VANET’s communication delay.
The message is efciently transmitted using the passive cluster formation approach.
LRCA was proposed in [41] to grant reliable and efcient data transmission in urban
VANET. LLT-based neighbor sampling scheme is used to select a group of vehicles
with stable neighbor vehicles. In this proposed, the routing approach is prepared to sup-
port infotainment services in VANET which are not strict in delay constraints.
In [42], a new single-hop clustering scheme was presented that elects trustworthy
CHs based on a hybrid approach combining trust factors and stability. Also, this
research proposed a new adaptive trust function to assess the data trust between nodes
according to the reported event’s requirement in terms of trust severity, unlike other
schemes which use a static trust function. The proposed scheme increases the reliability
of sharing data compared with other recently proposed.
In [44], the authors proposed EWCA which is a single-hop clustering algorithm.
In an emergency message transmission case, this technique reduces the formation of
unstable clusters and enhances the clustering stability.
CBSC was proposed in [45] to help self-organized VANETs form stable clusters and
decrease the status change frequency of vehicles on highways and two metrics. Also,
a new CH selection algorithm was presented to minimize the impact of vehicle motion
differences. In this proposed, two metrics are introduced to enhance VANETs security.
In [46], HCAR is a Hybrid Clustering Algorithm. It was designed on the basis of
the RSUs for the Internet of Vehicles (IoV), and it is a single-hop clustering algorithm.
The distributed RSUs are where the HCAR algorithm is centralized. After the RSUs
have been controlled, a graph theory-based approach is used to form the clusters. The
selection of secondary CH resolves the unavailability problem of CH. This proposed
enhanced the stability of CH.
DHC algorithm for VANET was proposed in [47] with a focus on rising clusters
stability and reducing the number of clusters in the network under different conditions
and scenarios. The proposed scheme performed better than other algorithms in terms
of efciency and cluster stability, under different channel models, vehicles density, and
trafc scenarios, especially in dynamic mobility environments.
In [54], the authors suggested a robust and dynamic mobility-based clustering
approach JCV. It takes into account the moving direction at the next junction in the
cluster formation process. This technique provided high stability, preventing clusters
from breaking at the junction frequently.
Single-hop clustering algorithms provide more reliable intra-cluster communication
and highly effective coordination to CHs. The coverage area of this type of cluster is
small, which leads to high maintenance overhead and a large number of clusters.
Also, in single-hop clustering algorithms, when the density of vehicles is very high,
collisions can occur and can lead to a low packets delivery ratio. Also, when the density
of vehicles is low, the vehicle may not nd any member and stay single, so it cannot
form a cluster. These two situations should be avoided because the cluster performance
will be decreased. We can summarize that the single-hop algorithms provide good
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cluster stability and low latency, but clustering coverage requires further improvement.
Also, to solve the problem of high and low density, the maximum and the minimum
number of vehicles can be limited in a cluster.
2. Multi-hop Clustering Algorithm
Clusters are generated with multi-hop distance, where every node is at a distance of
at most multi-hop from its CH. Some multi-hop clustering algorithms are presented in
this section.
In [29], DMCNF was proposed; it allows vehicles to periodically select their tar-
gets from one-hop neighbors in a distributed manner. The CH selected depends on the
relationship of neighborhood among vehicles. This algorithm improves the cluster’s
stability.
A hybrid backbone-based clustering algorithm was proposed in [15], also, it is a
multi-hop clustering algorithm. The concept of a number of links and vehicular mobil-
ity are used for cluster formation and CH selection. During cluster formation, nodes
with a relatively higher degree of connectivity initially form a backbone that is desig-
nated as leadership. The leadership then participates in CH election and efcient cluster
re-organization using an aggregate relative velocity of vehicles in the leadership.
A multi-hop clustering approach was also presented in [22] called TB with the goal
of maximizing the stability of the network topology and decreasing network dynamics.
The speed difference among vehicles as well as the position and the direction were
taken into account in this proposed during the cluster formation process. This algorithm
increases CH lifetime and minimizes vehicle transition between clusters.
The multi-hop clustering algorithm was introduced in [33]. It is called VMaSC-
LTE, and it is based on the amalgamation of a 4G cellular system with IEEE 802.11p
to improve the VANET communication performance. The multi-hop technique ensures
that CH selection and clustering are both stable. With the decrease in CH, the stability
improves.
An AWCP was introduced by taking into account the speed information, direction,
position, and highway ID to select the most stable vehicles among current vehicles
to operate as CHs [30]. To maximize cluster structure stability, highway ID informa-
tion is used. This technique improves cluster lifetime and minimizes communication
overhead.
PMC algorithm in VANET was proposed in [14] to solve the lack in clustering algo-
rithms performance in terms of stability and reliability. In this algorithm, the clustering
is introduced depending on the priority neighbor following strategy, and the CH select-
ing technique is adopted to select the optimal CH.
In [48], the author introduced a heuristic algorithm for electing a vehicle as a CH in a
cluster. In this method, weighted tness values are used for electing a CH vehicle based
on three parameters; trust value, absolute relative average speed, and position from the
cluster boundary.
Multi-hop clustering algorithms can reduce the number of clusters; expand clus-
ter coverage area, and enhance cluster stability. We can summarize that the multi-hop
algorithms provide high clustering coverage, and good cluster stability, especially with
regard to the number of CM re-afliation, CH changes, and cluster lifetime. However,
multi-hop cluster formation is more complex, which will take a long cluster formation
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time, and this may cause a delay in transmitting the information. Also, the cluster over-
head requires more improvement.
Also, according to some simulation results, the cluster performance degrades when
the number of hops is more than three. This means when the hop count increases the
cluster performance will decrease.
A comparison between the clustering algorithms is shown in Table 3 in terms of
transmission range, vehicle density, vehicle velocity, hop count, and trafc scenario.
Table 3. Clustering algorithms comparison
Algorithm Transmission Range Vehicle
Density
Vehicle
Velocity
Hop
Count
Trafc
Scenario
EWCA 300m 50–150 30m/s Single Highway
Proposed in [38] 300m 80–510 5.5–33.3m/s Single Highway
VWCA Dynamic 100–1000m 10–350 19–33.3m/s Single Highway
UFC 300m 200 10–35m/s Single Highway
FLBA 200m 0.05–0.4/m 22–33.3m/s Single Highway
NMCS – – – Single –
AMACAD 100–200m 50 11–31 m/s Single Urban
SBCA 300m 50–150 25–35m/s Single Highway
CBSC – – 55.55m/s Single Highway
HCAR 100m–300m 100 10–40m/s Single Highway
MOSIC 200m 100 10–35m/s Single Highway
LRCA 200, 500m 1500 10–30m/s Single Urban
DHC 300m 50–200 13.830m/s Single Highway,
urban
NCABAT 150m 60 – Single Random
Proposed in [42] – 10–60 10–120m/s Single Highway
JCV 200m 100 10–35m/s Single Highway
DMAC – 30–200 2, 5, 10m/s Multi Random
ALM – 50–1000 10–30m/s Multi Highway
DMCNF 100–300m 100 10–35m/s Multi Highway
DMMAC 200m 100–800 22–33.3m/s Multi Highway
VMaSC-LTE 200m 100 10–35m/s Multi Highway
Sp-Cl 80m, 125m 20–150 22–44m/s Multi Highway
TB 150–300m, 800, 1000m 400 19, 25, 30m/s Multi Highway
AWCP 1000m 25–200 33.3–41.6m/s Multi Highway
PMC 100–300m 100 10–35m/s Multi Random
CBDRP – 60 25–35m/s Multi Highway
Proposed in [48] 600m 200 11m/s Multi Urban
Proposed in [55] – 100 20–60m/s Multi Urban
RCMS 250m 1200 10–30m/s Multi Urban
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Cluster maintenance. Because of VANETs dynamic topology, severe packet loss
occurs due to frequent vehicle re-connection and disconnection. The cluster maintenance
process ensures strong connectivity by reducing frequent vehicle re-clustering and
also achieves a stable link lifetime through CH. Cluster maintenance involves vehicle
joining, vehicle leaving, cluster merging, and other cluster maintenance methods. There
are a lot of maintenance methods introduced in the literature; we discuss some of them
in this section.
In vehicle joining and vehicle leaving process, the CH sends frequent signals and
if it receives any signal from a vehicle, this new vehicle is assigned to that cluster and
becomes CM of that particular cluster. Then the CH will update its local database.
When the CH loses the connection with a member vehicle, the information for that
member is deleted from the CH’s local database. AWCP in [30], and DMCNF in [29]
are an example of the algorithms which used this method.
The second method is the cluster merging process; it is more complex than the rst
one. Cluster merging takes place when two or more clusters can be represented by a
single merged cluster, which can minimize the clusters’ number and improve the clus-
tering efciency. The conditions of the cluster merging are different for each algorithm.
For example, in the ALM algorithm [15], cluster merging occurs if two CHs are in each
other’s transmission range. The VMaSC-LTE in [33], the averaged relative speed of the
two neighboring CHs, referred to as AVGREL-SPEED, is compared. The CH with the
higher average relative speed relinquishes his CH job and becomes a CM for the CH
with the lower average relative speed. Also, the PMC algorithm in [14] used the cluster
merging method in the cluster maintenance phase, the CH node sends merge request
packets to other neighboring CH to request cluster merging. If one of these two CHs has
smaller following vehicles and high relative speed, the merging process is performed.
Other algorithms addressed the two processes (cluster merging and vehicle leaving
or joining) in the cluster maintenance phase like TB [22], SP-CI in [24], DA-CMAC
in [32], LRCA in [41], UFC in [40], and JCV in [54].
A selected of secondary CH is another approach used in the cluster maintenance
phase. Some algorithms like EWCA proposed in [44], SBCA in [25], CBRDP in [16],
and HCAR in [46] used this method. The secondary CH is selected by the CH accord-
ing to different criteria. It resolves the unavailability of CH to increase the clustering
stability.
Some algorithms used another cluster maintenance method; like Deep Reinforce-
ment Learning (DRL) scheme in [39]. This scheme was designed for VANET and
enhanced the safety and the QoS in the transmission of data. Q-learning tables deter-
mine the best route for data transfer. In the maintenance phase, the performance can be
improved in terms of predicting connection failure and reducing overhead delays by
updating Q-learning tables. While in [20], the maintenance phase in the FLBA algo-
rithm is adjustable to drivers’ behavior on the way and has a learning technique for
predicting the future position and speed of all CMs using fuzzy logic inference system.
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2.3 Clustering algorithms comparison
Various parameters are typically used to compare clustering techniques. These
parameters are used to generate and characterize any clustering algorithm [6]. Cluster
stability, latency, convergence, and overhead are some of these key parameters. The
benchmark algorithms are compared in Table 4 based on these parameters. A good clus-
tering algorithm achieves high stability and low latency, overhead, and convergence.
Table 4. Clustering algorithms comparison
Algorithm Cluster Stability Latency Overhead Convergence
EWCA High Stability Low Latency High overhead Medium
VWCA High Stability – – Low
DRL – Low Latency Low overhead –
AWCP Low Stability Medium Latency Low overhead Medium
DMMAC High Stability Low Latency – High
VMaSC-LTE High Stability Low latency High overhead Low
ALM Low stability Low latency Low overhead Low
UFC High stability – Low overhead –
ALCA Improves stability High latency High overhead –
FLBA High Stability – – –
TACR Improves stability – Low overhead Low
TB Improves stability – Low overhead High
AMACAD Medium Stability High latency – Low
DMCNF Improves Stability – Low overhead Low
NMCS Improves Stability – – Low
Sp-Cl Improves Stability – Low overhead Low
SBCA Improves stability – Low overhead Low
CBSC High Stability – – –
PMC High stability Low Latency Low overhead Low
CBDRP High stability Low Latency Low overhead High
HCAR Improves stability High latency Low overhead Medium
MOSIC Improves stability Low overhead High
LRCA Improves Stability Low Latency Low overhead –
DHC High stability – Low overhead Low
NCABAT Low Stability Low Delay in High Density – –
Proposed in [42] High Stability – Low overhead Low
Proposed in [48] Improves stability Low latency Low overhead –
JCV High stability Low latency Low overhead –
RCMS High stability Low latency – High
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3 Challenges and techniques used for solution
Many researches with several clustering algorithms are available in order to enhance
the performance of the wireless network. The researchers have examined various issues
and used various clustering algorithms to nd solutions to them; in this section, we
present some of these challenges as well as the approaches utilized to solve them as in
Table 5.
For cluster formation in VANET, a DMCNF algorithm in [29] solved the network
weaknesses that occur as a result of a dynamic topology. For highways, a CBDRP in
[16] solved the problem of fast data transmission and link connectivity.
In order to increase the VANET’s stability in an urban area, a lane-based clustering
algorithm was introduced in [17]. Clustering reduces the overhead and provides a hier-
archical network topology that is efcient. The CH improves the network’s lifetime.
The hybrid backbone-based clustering algorithm in VANET uses the aggregate rel-
ative velocity to select the CH [28]. The nodes carry out an effective CH selection task
with a minimum relative speed and high connection.
In [37], for desert and rugged situations, a VANET-based clustering routing proto-
col was introduced. The source and destination vehicles work to keep the stability of
cluster architecture. The designed algorithm’s tasks are CH selection, cluster structure
formation, and routing protocols.
The overhead delay and cluster stability problems have been solved using the pas-
sive approach in [38], also, the message is transmitted efciently using this approach.
In [32], For VANET, a DA-CMAC algorithm was implemented. The rearrangement
cost for short-period connections is reduced using clustering. The CH manages the
channel access and the time slots are assigned to the CM. Clustering the time slots into
two groups depending on the direction of movement achieves the merging collision and
channel access.
To improve the MAC routing protocol, an EDCA was introduced [49]. A xed con-
trol channel interval (CCHI) and a variable CCHI are two problems of message trans-
mission communication for safety. The EDCA scheme achieves optimal MAC routing
parameters with priority given to the emergency message.
In [43], to address the problem of network connectivity failure, an intelligent
forwarding-based stable and reliable data dissemination approach was proposed. Vehi-
cles choose the next forwarding node based on the mathematical formula that represents
link stability. The data transfer is handled by the greedy approach, and a separate algo-
rithm is used to beat the network connection failure. To recover the information con-
nection’s break links, the edge weights are utilized.
The behavior of driver prediction has an impact on the cluster’s stability in VANET.
In [53], for efcient VANET clustering, machine learning based on a prediction of a
driver behavior technique was proposed. The NBP algorithm estimates the behavior of
the driver based on 2 factors: overtaking decisions and driving speed. The NBP clas-
siers divide a driver’s habit into three categories: vehicle type, relative speed, and a
number of traveled lanes. The VANET’s optimum driving model is intended to obtain
a stable clustering.
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A major issue in VANET is Cooperative collision avoidance (CCA) which has an
impact on cluster stability. In various two directions real trafc scenarios, a probabi-
listic direction aware (PDA) algorithm was proposed to dominance CCA [50]. Cluster
formation is handled using a modied k-medoids method that integrates the Hamming
distance metric for direct knowledge. The distance and speed of nodes are used to
calculate a collision’s probability between the vehicles. The benign factor is used to
determine the vehicles’ optimal safe speed, which is compared to the threshold range
and delivers a collision warning. The communication overhead and collision latency
are decreased.
One of the major issues in the VANET is security because of its impact on the per-
formance of the network. It is done in [26] and [51]. The authors in [51] implemented a
FCMS for detecting a reliable vehicle. For clustering in VANET, FCMS1 and FCMS2
models are compared. Three input factors are for FCMS1. Vehicle trustworthiness (VT)
is the fourth input factors to the FCMS2 model. The FCMS2 model improves cluster
stability over the FCMS1 model.
In [52], the MBCA was used to solve the problem of multimedia broadcasting con-
tent in a hybrid VANET topology. Cluster formation and CH selection are based on
mobility measurements, which are utilized to determine the vehicles’ relative speed.
The cluster’s stability is improved using the handshake process.
Table 5. Problem and solution technique
Reference Problem Techniques Used Performance
[14] Lack in reliability and
stability of clustering
algorithms
PMC approach Improve the performance in terms
of in reliability and stability.
[16] Rapid data
transmission, link
stability and realizing
reliable
CBDRP Reduces latency and increases
the packet delivery ratio and link
stability
[17] Cluster stability A lane-based clustering
algorithm
Improves the stability by
increasing the CH Lifetime
[25] Vehicles frequently
joint and leave the
clusters.
SBCA Improves the stability of the
network by reducing the overhead
and the cluster lifetime
[29] Weakness in the
network because of
high dynamic
Multi-hop clustering
algorithm (DMCNF)
Improves the stability
[33] Delay and delivering
of the safety messages
problems.
Hybrid architecture called
VMaSC-LTE companies
LTE and IEEE802.11P
Achieves low delay and high
packet delivery ratio
[26] Security ALCA Performing fast clustering,
increases efciency.
[28] Frequent CH changing Hybrid backbone based
clustering algorithm
Improves cluster stability by
forming the cluster leadership
[32] Short communication
period
DA-CMAC Reduces collision and increases
reliability of packets
(Continued)
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Paper—Clustering Review in Vehicular Ad hoc Networks: Algorithms, Comparisons, Challenges and…
Reference Problem Techniques Used Performance
[34] Problem of delivering
data in urban vehicular
environments
NSVC Guarantees the reliability of
delivering emergency messages,
increases clustering stability.
[37] Route selection, stable
clustering suitable for
desert
CBVRP Increases communication
efciency, delivers information
with ensures reliability, and
decreases the routing cost.
[38] Cluster stability and
Overhead delay.
Passive data dissemination
approach
Improves cluster stability, reduces
the communication overhead,
and increases the efciency of
transmission messages.
[41] Cluster stability Reliability-based clustering
algorithm (LRCA)
Provides efcient and reliable data
transmission in VANETs
[43] Failure in data delivery
and communication
Link
Algorithm based on
intelligent forwarding
Minimizes delay, Improves cluster
stability and communication
Safety
[44] CH Stability. EWCA A better cluster stability and
overhead delay reduction
performance
[46] High mobility, big data
clustering
RSU based Multi-Hop
Clustering
Improves cluster stability, and
proves the efciency of the
algorithm in theoretical way.
[49] Transmission message
delay
EDCA for transmitting
emergency message
Reduces average delay and
increases the probability of
successful delivery
[50] Clustering and
cooperative collision
avoidance
P-DACCA with K-medoids
and Bengn factors
Reduces overhead delay, and
collision
Efcient stability
[51] Security and
trustworthiness
detection
FCMS1 and FCMS2 Efcient vehicles’ management in
the cluster
[53] Cluster stability, and
behavior prediction of
driver
NBP clustering Increases cluster stability in real
environments
[52] Stability of Link MBCA Multimedia broadcasting has been
improved.
4 Performance evaluation metrics
Any clustering algorithm’s performance can be assessed and evaluated using a
variety of parameters; Cluster performance and network performance are the most
common metrics used for evaluating the performance of clustering algorithms:
Table 5. Problem and solution technique (Continued)
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4.1 Cluster performance parameters
Cluster performance parameters represent how well clustering techniques perform
and mirror how stable the network’s backbone nodes are. The overall cluster perfor-
mance and stability are described using these parameters. Some of Cluster performance
parameters are:
• Cluster/CH Stability: It is the number of times the same vehicle is elected as a CH
out of all times.
• Cluster number: It refers to the number of clusters that form during network operation.
The clustering algorithm is more efcient when there are few cluster numbers [6].
• Cluster/CH lifetime: It is the maximum time for a vehicle that has played the head’s
task in a cluster. It is computed by dividing the overall lifetime by the time spent in
the head’s role [57].
• CM lifetime: It is the maximum amount of time a node can be CM for. To get its
average, we divide the total lifetime of the CM by the total number of state changes
from CM to another state [57].
• CH change rate: It is the average CH’s number change per time [57].
• Cluster change rate: Average cluster’s number changes for each vehicle in a unit
of time.
• Cluster size: Vehicles’ number in one cluster.
A good and stable clustering algorithm should have a large cluster size, high CH
and CM lifetime, few cluster numbers, and low cluster and CH change rate. However,
these parameters only can’t describe communication links’ details between vehicles in
the network.
4.2 Network performance parameters
The overall network performance is described by these parameters, which include:
• Throughput: It is the number of bits transmitted per second in any network. The
higher value of throughput provides better performance of the network designed [6].
• Packet loss ratio or collision ratio: The rate of packets’ loss during the transmission
process.
• Packet Delivery Ratio (PDR): It is the ratio of the number of packets received by the
destination to the total number of packets [57].
• Overhead: The average number of control messages is received by the vehicle.
• End to End Delay (E2E Delay) or Latency: It is the time taken for transmitting a
packet from a source to a destination.
All these parameters are utilized to estimate the context-based clustering approaches,
like trafc prediction, routing, and information dissemination. A good and efcient
clustering algorithm leads to large throughput, short E2E delay, low packet loss rate,
high PDR, and small overhead. Table 6 presents the evaluated parameters and the sim-
ulator tools used for each algorithm [58], [59], [60].
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Table 6. Clustering algorithms evaluation parameters
Reference Algorithm Simulator Tool Evaluation Parameters
[15] ALM SUMO, SIDE/
SMURPH
CH lifetime, Status changes per Node, CH density.
[16] CBDRP NS2 Latency, PDR, Average Routing Overhead
[18] VWCA MATLAB CH and CM lifetime, PDR.
[19] DMMAC SUMO, MOVE,
NS2
Average cluster size, probability of received CH SMS,
Probability of successful transmission, average travel
time for an emergency SMS
[20] FLBA NS2, MOVE,
SUMO
Average CH time, Average CM’s dwell time, Average
cluster size.
[21] TACR – Routing Overhead, CH Selection Time, Cluster
Creation Time, and Probability of message
Transmission.
[22] TB C++ Average cluster change, Average cluster lifetime
[23] AMACAD Java CH lifetime, Membership lifetime, Re-afliation rate
[24] Sp-Cl – Average cluster change, Number of clusters, and
average cluster lifetime.
[25] SBCA NS2 Average cluster lifetime, overhead, and packet delivery.
[26] ALCA VANET MobiSim Node participation time, Throughput, Efciency, CH
duration, Connectivity ratio
[27] AKHM C/C++ Clustering performance for crossroad scenario,
Clustering performance for rectangle road scenario.
[28] ARV SUMO Average Cluster-Head lifetime, Percentage of CHs.
[29] DMCNF NS2,
VanetMobiSim
Average CH/CM durations, Average number of
clusters, Average CH change number, and average
overhead.
[30] AWCP NS2, JOSM,
SUMO, MOVE
Average Cluster Lifetime, PDR, overhead.
[32] DA-CMAC NS3 PDR, CH Changes, Access collision.
[33] VMaSC-
LTE
NS3 & (SUMO) CH/CM Duration, CH Change Rate, Overhead,
Number of Vehicles in SE state.
[34] NSVC – CH lifetime, CH change, throughput.
[35] MOSIC NS3 Average CH/CM Duration, Average Number of
clusters, Average Control Message Overhead, Average
CH Changes Rate.
[36] NCABAT JADE Throughput, E2E Delay, and PDR.
[37] CBVRP – PDR, E2E delay, Number of cluster reconstruction,
Routing cost.
[38] Proposed in
[38]
OMNET++,
SUMO
Overhead.
[39] DRL QualNet7.1,
VanetMobiSim
Average E2E delay, and average PDR.
(Continued)
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Reference Algorithm Simulator Tool Evaluation Parameters
[40] UFC SUMO CH duration, CM duration, Clustering efciency,
Number of initial CHs, CM disconnection rate, CM
re-clustering delay, and CM re-clustering success ratio.
[14] PMC NS2,
VanetMobiSim
Average CH/CM Duration Time, Number of Average
Cluster Head Changes, Clustering Overhead.
[41] LRCA NS2, SUMO CH/CM duration, CH change rate, PDR,E2E delay,
overhead
[42] Proposed in
[42]
OMNET++,
SUMO
CH/CM duration, Overhead, CH selected time, PDR,
Trust/Untrust packets delivery rate.
[43] Proposed in
[43]
NS2, SUMO Latency, throughput, PDR.
[44] EWCA NS2, SUMO Cluster stability, number of clusters, and E2E.
[45] CBSC OMNeT++, SUMO Average CH/CM Lifetime, Average Number of
Re-afliation Times, Packet Loss Rate.
[46] HCAR NS2, VANET
MobiSim
CH lifetime, average overhead, and number of cluster
[47] DHC SUMO CH/CM lifetime, Number of changed states, packet
overhead, Cluster formation rate, CH Alienation.
[48] Proposed in
[48]
OMNET++,
SUMO
Throughput, Delay, No. of packets generated, PDR,
No. of clusters
[49] EDCA MATLAB Control channel interval, service channel interval.
[50] P-DACCA NS2 Cluster stability, overhead, and collision probability.
[51] FCMS1,
FCMS2
– Vehicle speed, vehicle cluster, degree of centrality, and
trustworthiness.
[52] MBCA OMNET++,
SUMO, and
VIENS
Average CH duration, average CM duration, PDR,
network delay, and overhead.
[53] NBP SUMO CH election, CM election, and lifetime of CH.
[54] JCV SUMO,
CVANETSIM,
J AVA
CH duration, CM duration, CH change rate, number of
cluster, cluster participation, number of CM. number of
EN, ratio of CM, EN duration, overhead, delay
[55] Proposed in
[55]
NS2 PDR, Throughput.
[56] RMCS OMNET++,
SUMO
Cluster lifetime, PDR, delay, overlap rate,
reconstruction time
5 Conclusion and future work
In recent years, VANETs have been used in a variety of applications. Vehicle
safety, trafc management, and accurate vehicle information communication are the
main functions of VANET. Due to the high speed of vehicles, the VANET topology is
dynamic. Scalability issues are caused by the dynamic nature of the VANET and the
clustering represents one of the reliable solutions. This work presented an intensive sur-
vey for the most clustering techniques to solve different VANETs issues. We provided
Table 6. Clustering algorithms evaluation parameters (Continued)
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an overview of the clustering technique in VANETs. At rst, a history of 51 cluster-
ing algorithms for two decades with the number of their citations was highlighted.
Then, we have introduced the algorithms and the criteria of each of the clustering steps,
including the metrics used for selecting the CH for each algorithm, cluster formation
according to the hop distance, and cluster maintenance. Also, to see the performance
of these algorithms, we have made comparisons between them based on some key
parameters. Then, we presented some of VANET’s challenges as well as the clustering
approaches utilized to solve them and see the performance of these approaches. Finally,
we introduced some of the most common metrics used for evaluating the performance
of clustering algorithms.
From our survey, we can see most clustering algorithms are designed for highways,
and cluster stability is one of the major issues in VANET. In future work, we will design
a new clustering algorithm for VANET suitable for urban environments using hyper-
graph theory, and our approach will aim to increase the clustering stability.
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7 Authors
Mays Kareem Jabbar received the B.Eng. degree in computer and information
technology engineering from the University of Technology, Baghdad, Iraq, in 2007,
and the master’s degree in wireless communications networks from Computer Engi-
neering Department/Eastern Mediterranean University/Cyprus 2014. She is working
as a lecturer at the College of Engineer, Misan University, Misan, Iraq. She is cur-
rently a Ph.D. student at ENIS/Sfax University/Tunisia. She can be contacted at email:
m_mays85@uomisan.edu.iq.
Hafedh Trabelsi studied in Ecole National d’Ingénieurs de Sfax ENIS from 1983 to
1989, In 1990, M.S. degree received from the Ecole Centrale de Lyon, France, in 1994
the PhD degree at the University of Paris XI, Orsay, France, and in 2008 the Research
Management Ability degree from ENIS, All in electrical engineering. He joined the
Tunisian University in 1995; He is currently a Professor of Electric Power Engineering
at ENIS. Since 2013 he is full professor at University of Sfax, Tunisia, holding the
chair for Smart Electric Vehicle group in Computer Embedded System laboratory at
ENIS. In his research he focuses on the design of new electric machines by using Finite
Element Method and the implementation of advanced control system for electric vehi-
cle. On-going research is focusing on smart city & secure system. He can be contacted
at email: hafedh.trabelsi@enis.tn.
Article submitted 2022-02-04. Resubmitted 2022-03-19. Final acceptance 2022-04-06. Final version
published as submitted by the authors.
48
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