A Bio-Inspired Cluster Optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

Ghassan Husnain, Shahzad Anwar, Gulbadan Sikander, Armughan Ali and Sangsoon Lim ()
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Ghassan Husnain: Department of Computer Science, Iqra National University, Peshawar 25100, Pakistan
Shahzad Anwar: Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25100, Pakistan
Gulbadan Sikander: Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar 25100, Pakistan
Armughan Ali: Attock Campus, COMSATS University Islamabad, Islamabad 43600, Pakistan
Sangsoon Lim: Department of Computer Engineering, Sungkyul University, Anyang 14097, Republic of Korea

Energies, 2023, vol. 16, issue 3, 1-20

Abstract: Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf Optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

Keywords: bio-inspired algorithms; clustering; vehicular networks; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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