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  • 标题:QOS BASED PATH SELECTION FOR MODIFIED SMART OPTIMIZATION METHODS
  • 本地全文:下载
  • 作者:RADHWAN JAWAD KADHIM ; Dr MUAYAD SADIK CROOCK
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2018
  • 卷号:96
  • 期号:23
  • 出版社:Journal of Theoretical and Applied
  • 摘要:The routing technology is a major challenge in determining the performance of the Ad Hoc Wireless Sensor Network (AHWSN). In this paper, we propose a routing methods to reinforce network performance and overcome network problems such as path destroying within a specified time. The proposed method considers the all possible paths between source node and destination node are discovered. Each path has three metrics of Quality of Service (QoS): bit rate, packet loss rate and delay to be considered in path selection methods instead of single metric. Therefore, this problem is considered as an NP-complete problem. The proposed methods aims to finding the robust path between source and destination node in the face of conflicting design objectives. This is done by minimize packet loss rate, maximize the bit rate and minimize delay using three methods in path selection decision: Weighted Sum optimization method, Weighted Sum Based Genetic Algorithm optimization method and the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) with two types of Crossover. The obtained results of the three methods are compared with Bellman-ford algorithm based Ad Hoc on Demand Distance Vector (AODV) protocol and compared with NSGA-II priority based encoding. Simulation results, performed in Matlab (2017a), prove that the effectiveness of the proposed methods in terms of finding best path to solve routing problem and QoS requirements in static and dynamic environment. There is important decrease in packet loss rate, delay and increase in bit rate by the proposed method under the varying sensor nodes. In addition, the proposed methods save energy because they do not need to reroute again when route failure occurs.
  • 关键词:AHWSN; Weighted Sum; Genetic Algorithms; NSGA-II; AODV
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