Enhanced clustering based routing protocol in vehicular ad‐hoc networks. Issue 1 (13th January 2023)

• Record Type: Journal Article
• Authors: Naeem, Afia; Rizwan, Muhammad; Alsubai, Shtwai; Almadhor, Ahmad; Akhtaruzzaman, Md.; Islam, Shayla; Rahman, Hameedur
• Is Part Of: IET electrical systems in transportation. Volume 13:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-13
• Subjects: electric drives -- fuzzy control -- transportation; Electric vehicles -- Periodicals; Electricity in transportation -- Periodicals; 621.3105
• DOI: 10.1049/els2.12069
• Languages: English
• ISSNs: 2042-9738

Abstract

Abstract: A vehicular ad‐hoc network (VANET) is derived from a mobile ad‐hoc network that is a part of less infrastructure network design. Vehicular communication in VANET can be achieved using vehicle‐to‐infrastructure (V2I) and vehicle‐to‐vehicle (V2V) communication. A vehicle communicates with other vehicles through onboard units while communicating with roadside units in an infrastructure mode. Secure clustering is required for the communication between nodes in the whole network. The fundamental problem with the VANET is the instability of the network that occurs due to vehicles' mobile nature, which decreases the network's efficiency. This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The Sugeno model fuzzy inference system is used for assessing the cluster head (CH) that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters. Our enhanced routing protocol shows that the proper channel model with an efficient routing protocol enhances the link throughput of the VANET for fixed network size. Our results show an efficient selection method of CH through the fuzzy system and a 10% increase in network lifetime. Furthermore, performance evaluation also demonstrates the impact of network sizes and routing protocols on packet delivery ratio and packet loss, average end‐to‐end delay, and overhead transmission. Abstract : This research proposes an enhanced cluster‐based lifetime protocol ECBLTR that focuses on maximising the network's stability of routing and average throughput. The cluster head (CH) is accountable for dictating the next CH based on the routing table and communication. This paper proposes an enhancement using the Sugeno model fuzzy inference system for assessing the CH that takes residual energy, local distance, node degree, concentration, and distance from the base station as input parameters.