Localizing non-line-of-sight nodes in Vehicluar Adhoc Networks using gray wolf methodology

摘要

Vehicular ad hoc networks (VANETs) evolved by adopting the principles of mobile ad hoc networks. This network has been designed to deploy safety related application in vehicular node in the less chaotic environment in road scenarios. Vehicles exchange emergency messages through direct communication. In a practical situation, a direct communication between the vehicles is not possible, and it is prohibited by either static or dynamic obstacles. These obstacles prevent the direct communication between the vehicles and can craft a situation like non-line of sight (NLOS). This NLOS becomes a perennial problem to the researchers as it creates localization and integrity issues which are considered to be important for road safety applications. Handling the moving obstacles is found to be a challenging one in the VANET environment as obstacles like truck are found to have similar characteristics of the vehicular nodes. This paper utilizes the merits of the meta-heuristic approach and makes use of the improved gray wolf optimization algorithm for improving the localization and integrity services of the VANET by overcoming the NLOS conditions. The proposed methodology is found to have improved neighborhood awareness, reduced latency, improved emergency message delivery rate, and reduced mean square error rate.