Energy Efficient Clustering Technique for VANET

Abstract

Vehicular ad-hoc networks (VANET) are listed as an extension of mobile ad-hoc networks (MANETs) which can improve road safety and provide the Intelligent Transportation Systems (ITS). In addition to its advantages, VANET faces many obstacles ranging from high-energy consumption to instability induced by high changes in topology. The main goal of designing an optimum route algorithm is to decrease the probability of contact failure and reduce the energy consumption of nodes within the network. Clustering is thus a method to combine nodes and make the network more robust. With no node consciousness, it is often shorter on resources, which causes network execution problems and changes in topology. At that point, a primary energy issue emerges in the AODV routing protocol that aims to improve the energy efficiency of the V2V communication in nodes lifetime and to connection lifetime problems in the network. This article proposed a clustering-based optimization technique called Energy Efficient Clustering Technique (EECT) with the AODV protocol’s K-Medoids clustering algorithm in order to cluster vehicle nodes and find nodes that are convincing to interact in a defined secured and reliable path, which detailed in previous works. Efficient nodes are recognized from each cluster with the goal of energy-efficient communication, to optimize the parameter as minimum energy consumption in VANET.

Machine learning‐based clustering protocols for Internet of Things networks: An overview

“…Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem. The dynamic nature of the network is well managed by previous studies 76,77,79 . However, all k‐medoids‐based solutions are centralized.…”

“…-Most of K-medoids-based solutions consider energy efficiency. [74][75][76][77]79 Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem. The dynamic nature of the network is well managed by previous studies.…”

“…In the proposed CORP, key factors for the CH selection include QoS impact, energy status criteria, distance, and node position. Iswarya and Radha 79 developed a clustering‐based optimization technique for V2 V communications using the ad hoc ondemand distance vector (AODV) protocol and K‐medoids clustering algorithm. The suggested method intends to cluster vehicles and identify the ones that are capable of interacting in a predetermined, secure, and reliable path.…”

“…‐ Most of K‐medoids‐based solutions consider energy efficiency 74–77,79 . Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem.…”

“…The dynamic nature of the network is well managed by previous studies. 76,77,79 However, all k-medoids-based solutions are centralized.…”