Citation: Emmanuel, S.; Isnin, I.F.B.;
Mohamad, M.M.B. A Reliable
Merging Link Scheme Using
Weighted Markov Chain Model in
Vehicular Ad Hoc Networks. Sensors
2022, 22, 4861. https://doi.org/
10.3390/s22134861
Academic Editors: Jaroslaw Pytka,
Andrzej Łukaszewicz, Zbigniew
Kulesza, Wojciech Giernacki and
Andriy Holovatyy
Received: 11 March 2022
Accepted: 20 June 2022
Published: 27 June 2022
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Article
A Reliable Merging Link Scheme Using Weighted Markov
Chain Model in Vehicular Ad Hoc Networks
Siman Emmanuel * , Ismail Fauzi Bin Isnin and Mohd. Murtadha Bin Mohamad
Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia; ismailfauzi@utm.my (I.F.B.I.);
murtadha@utm.my (M.M.B.M.)
* Correspondence: esiman@graduate.utm.my; Tel.: +60-1137030907 or +234-(0)8065641551
Abstract:
The vehicular ad hoc network (VANET) is a potential technology for intelligent transporta-
tion systems (ITS) that aims to improve safety by allowing vehicles to communicate quickly and
reliably. The rates of merging collision and hidden terminal problems, as well as the problems of
picking the best match cluster head (CH) in a merged cluster, may emerge when two or more clusters
are merged in the design of a clustering and cluster management scheme. In this paper, we propose
an enhanced cluster-based multi-access channel protocol (ECMA) for high-throughput and effective
access channel transmissions while minimizing access delay and preventing collisions during cluster
merging. We devised an aperiodic and acceptable merge cluster head selection (MCHS) algorithm for
selecting the optimal merge cluster head (MCH) in centralized clusters where all nodes are one-hop
nodes during the merging window. We also applied a weighted Markov chain mathematical model
to improve accuracy while lowering ECMA channel data access transmission delay during the cluster
merger window. We presented extensive simulation data to demonstrate the superiority of the
suggested approach over existing state-of-the-arts. The implementation of a MCHS algorithm and
a weight chain Markov model reveal that ECMA is distinct and more efficient by 64.20–69.49% in
terms of average network throughput, end-to-end delay, and access transmission probability.
Keywords:
weighted Markov chain; clustering; weight value; merge window; merging link; merge
collision; predicting probability
1. Introduction
In a vehicle ad hoc network, topological changes are frequent as nodes (vehicles)
move in accordance with traffic laws [
1
–
3
]. As automobile density grows, access collisions
occur as a result of poor packet data transmission during slot allocation [
3
]. Therefore, an
effective clustering can lengthen the lifespan of a network. Clustering is a technique for
dissecting a network’s architecture. Topological data are obtained more quickly due to the
network’s smaller size (cluster). Because of the lack of centralized administration, network
topology management and resource allocation become difficult, resulting in inefficient
throughput and increased access latency [
1
]. To overcome hidden terminal problems and
merge collisions, an effective clustering technique is required. When using an allocated
technique to assign a period allocation [
4
], two sorts of conflicts can occur: access collision
and merge collision. Due to mobility, two vehicles that started more than two hops apart try
to join a single period allocation at the same moment [
5
]. Automobiles traveling in reverse
directions with RSUs fastened to the road [6] and two or more clusters merging can cause
merging conflicts. Assuming the nodes in this scenario have already been assigned time
slots in their clusters during the cluster merging process, they must be released from their
current time slot to acquire a new one, which may result in merging collision [
7
], whereas
access collision occurs when more than one node (that has not yet acquired a time slot)
within transmission coverage, or approximately two hops apart, attempts to enter a single
available period allocation. Therefore, increasing traffic density that is not in cluster may
Sensors 2022, 22, 4861. https://doi.org/10.3390/s22134861 https://www.mdpi.com/journal/sensors
