Abstract
Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network’s overall performance and consequently reducing the routing cost of the vehicular network.
Highlights
Accidents are one of the major reasons behind sudden deaths worldwide
The results of our anticipated algorithm are comparatively analyzed with three renowned algorithms, Multi Objective Particle Swarm Optimization (MOPSO) [43], CACONET [44] and Comprehensive Learning Particle Swarm Optimization (CLPSO) [45]
The experimentation proves that our algorithm generates smaller number of clusters in comparison to other techniques mentioned above
Summary
Accidents are one of the major reasons behind sudden deaths worldwide. The Global status report of 2018 on safety of roads [1], providing statistics of 180 countries, shows that the number of deaths due to road accidents globally, has soared to 1.35 million annually. National economies are massively affected by road accidents and crashes. These comprises of medical costs, damage to the property and cost on emergency/police services. The present traffic system has VOLUME XX, 2021 many problems. It has accident risks, problem of congestion. Within the past few years VANETs are reasonably a vibrant area amongst researchers. Numerous research papers have been published having recommendations, plug-ins, theoretical evaluation of the concerns/problems involved, simulation outcomes and enhancements in order to improve current techniques and algorithms [3]
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