Optimised Tail-based Routing for VANETs using Multi-Objective Particle Swarm Optimisation with Angle Searching

Routing protocols for vehicular ad hoc networks (VANETs) are highly important, as they are essential for operating the concept of intelligent transportation system and several other applications. VANET Routing entails awareness about the nature of the road and various other parameters that affect the performance of the protocol. Optimising the VANET routing guarantees optimal metrics, such as low E2E delay, high packet delivery ratio (PDR) and low overhead. Since its performance is of multi-objective nature, it needs multi-objective optimisation as well. Most researchers have focused on a single objective or weighted average for multi-objective optimisation. Only a few of the studies have tackled the actual multi-objective optimisation of VANET routing. In this article, we propose a novel reactive routing protocol named tail-based routing, based on the concept of location-aided routing (LAR). We first re-defined the request zone to reduce the lateral width with respect to the lateral distance between the source and destination and named it tail. Next, we incorporated angle searching with crowding distance inside the multi-objective optimisation MO-PSO and called it MO-PSO-angle. Then, we conducted optimisation of tail-based routing using MO-PSO-angle and compared it with optimised LAR, which exhibited the superiority of the latter. The best improvement was at the optimisation point with a 96% improvement of PDR and a 313% improvement in E2E delay.

@article{AL-Shammari2020,
  title     = {Optimised Tail-based Routing for VANETs using Multi-Objective Particle Swarm Optimisation with Angle Searching},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {6},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Mustafa Qasim AL-Shammari and Ravie Chandren Muniyandi},
  doi       = {10.14569/IJACSA.2020.0110629},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110629}
}