A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks. (2nd December 2020)

• Record Type: Journal Article
• Authors: A., Christy Jeba Malar; M., Deva Priya; Janakiraman, Sengathir
• Is Part Of: International journal of communication systems. Volume 34:Number 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-02
• Subjects: Crow Search Optimization Algorithm (CSOA) -- Gray Wolf Optimization Algorithm (GWOA) -- NLOS positioning -- Non‐Line‐of‐Sight (NLOS) nodes -- Vehicular Ad hoc NETworks (VANETs); Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4697
• Languages: English
• ISSNs: 1074-5351

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. Abstract : Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed with balanced exploitation and exploration for accurate localization of NLOS nodes. HCSGWOA uses cooperative positioning by recovering the location of vehicles based on vehicular nodes' relative range measurement and Global Navigation Satellite System (GNSS) information. HCSGWOA also uses modified position update of Crow Search Algorithm (CSA) that aids in better balance in searching process using nonlinear control parameter and adaptive balance probabilistic strategy for accelerating the process of positioning NLOS nodes.