Congestion aware clustered WSN based on an improved ant colony algorithm

Q4 Engineering Measurement Sensors Pub Date : 2024-07-16 DOI:10.1016/j.measen.2024.101280
R. Anto Pravin , X.S. Asha Shiny , V. Baby Vennila , P. Selvaraju , R. Uma Mageswari , S. Satish kumar
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Abstract

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.

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基于改进蚁群算法的拥塞感知集群 WSN
在无线传感器网络(WSN)中开展的传统工作主要集中在以能量为导向的服务上,而对以延迟为导向和拥塞感知服务所采取的措施却很少。因此,所提出的机制特别关注网络结构设计,通过为每个集群设置交会地点以及路由分割来控制拥塞发生和不必要的延迟。这里提出了改进蚁群算法的拥塞感知聚类(CAC_IACA)。该机制包括两个步骤:(i) 按照蚁群优化(ACO)算法确定最佳路径;(ii) 使用会合移动节点进行数据分割。每个集群中都有会合节点,以降低数据传输过程中接收端的拥塞率。所提出的方法主要集中于降低三维环境监测的覆盖成本。对仿真结果进行了分析,证明所提方案的效率比传统方法高出 26.54%。
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来源期刊
Measurement Sensors
Measurement Sensors Engineering-Industrial and Manufacturing Engineering
CiteScore
3.10
自引率
0.00%
发文量
184
审稿时长
56 days
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