A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

Abstract

Summary Vehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches.

NLOS node localization for improving warning message distribution in VANETS

“…Many NLOS positioning systems have been contributed previously, among these some nature‐inspired metaheuristic algorithms including Hybrid Crow Search and Gray Wolf Optimization Algorithm, 16 Improved Rank Criterion‐based NLOS node Detection Mechanism (IRC‐NLOS‐DM), 17 geographic and spatio‐temporal information based distributed cooperative positioning (GSTICP) technique, 18 collaborative network coverage enhancement scheme (CONEC) 19 and Rank Criteria Improved Confidence‐based Centroid Scheme (RCICCS) 20 were identified to be leading in locating the unknown NLOS nodes in the network. The current approaches might still be improved when it comes to regulating the agreement between exploration and exploitation for reducing localization errors.…”