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Localizing non‐line‐of‐sight nodes in Vehicluar Adhoc Networks using gray wolf methodology
International Journal of Communication Systems ( IF 2.1 ) Pub Date : 2020-10-22 , DOI: 10.1002/dac.4642
Ramu Kaviarasan, Pillutla Harikrishna

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.

中文翻译:

使用灰太狼方法对Vehicluar Adhoc网络中的非视距节点进行本地化

车载自组织网络(VANET)通过采用移动自组织网络的原理而发展。该网络旨在将安全相关的应用程序部署在道路场景中不太混乱的环境中的车辆节点中。车辆通过直接通信交换紧急消息。在实际情况下,车辆之间不可能直接通信,并且无论是静态还是动态障碍物都禁止这种通信。这些障碍物阻碍了车辆之间的直接通信,并可能造成非视线(NLOS)等情况。对于研究人员来说,这种NLOS一直是一个长期的问题,因为它会产生本地化和完整性问题,这些问题对于道路安全应用至关重要。在VANET环境中,发现移动障碍物是一项具有挑战性的挑战,因为发现卡车等障碍物具有与车辆节点相似的特征。本文利用了元启发式方法的优点,并利用改进的灰狼优化算法克服了NLOS条件,从而改善了VANET的定位和完整性服务。发现所提出的方法具有改善的邻域意识,减少的等待时间,改善的紧急消息传递率以及降低的均方误差率。本文利用了元启发式方法的优点,并利用改进的灰狼优化算法克服了NLOS条件,从而改善了VANET的定位和完整性服务。发现所提出的方法具有改善的邻域意识,减少的等待时间,改善的紧急消息传递率以及降低的均方误差率。本文利用了元启发式方法的优点,并利用改进的灰狼优化算法克服了NLOS条件,从而改善了VANET的定位和完整性服务。发现所提出的方法具有改善的邻域意识,减少的等待时间,改善的紧急消息传递率以及降低的均方误差率。
更新日期:2020-12-03
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