Improved Grasshopper Optimization With Distance Vector Hop Relay Model for Effective NLOS Localization in VANETs

Abstract

Dissemination of emergency messages in vehicular ad hoc networks (VANETs) assists vehicles in determining approaching vehicle speed dynamics and enables drivers to make sound decisions. Factors in the road scenario, such as static and dynamic obstacles, cause the vehicle to receive emergency messages late or to be in non–line of sight (NLOS). Drivers may make poor decisions because of NLOS conditions, resulting in fatal crashes and loss of life. Hop-based approaches, cooperative volunteer protocol, and hardware-based detection approaches have been proposed to avoid this situation. However, many approaches have flaws in terms of detection and cost. This paper presents a bioinspired improved grasshopper metaheuristic approach to detecting NLOS node positions. This method combines the advantages of the distance vector hop method and bioinspired grasshopper to provide better exploration and exploitation in detecting the position of the nodes. The proposed work is superior in terms of emergency message delivery rate, channel usage rate, neighborhood awareness, and mean square error and delay during broadcast. The localization rate is improved by 12%, 18%, and 23% compared to the chosen benchmark approaches.