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基于 ASFO 的节能分簇和路由以及一种基于跨层的便捷路由协议的无线传感器网络。

Energy-Efficient Clustering and Routing Using ASFO and a Cross-Layer-Based Expedient Routing Protocol for Wireless Sensor Networks.

机构信息

Department of Electronics and Communication Engineering, HKBK College of Engineering, Bangalore 560045, Karnataka, India.

Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore 641202, Tamilnadu, India.

出版信息

Sensors (Basel). 2023 Mar 3;23(5):2788. doi: 10.3390/s23052788.

Abstract

Today's critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters.

摘要

当今传感器网络研究的关键目标是延长无线传感器网络 (WSN) 的寿命并降低功耗。WSN 需要使用节能的通信网络。聚类、存储、通信容量、高配置复杂性、低通信速度和有限的计算能力也是 WSN 的一些能量限制。此外,对于 WSN 能量最小化,簇头选择仍然是一个问题。在这项工作中,使用基于自适应旗鱼优化 (ASFO) 算法和 K-中心点的方法对传感器节点 (SN) 进行聚类。研究的主要目的是通过能量稳定、节点间距离减少和延迟最小化来优化簇头的选择。由于这些限制,实现最佳的能源资源利用是 WSN 中的一个基本问题。一种基于节能跨层的权宜路由协议 (E-CERP) 用于确定最短路径,动态最小化网络开销。所提出的方法用于评估数据包投递率 (PDR)、数据包延迟、吞吐量、功耗、网络寿命、数据包丢失率和误差估计,结果优于现有方法。对于 100 个节点,服务质量参数的性能结果为 PDR(100%)、数据包延迟(0.05s)、吞吐量(0.99Mbps)、功耗(1.97mJ)、网络寿命(5908 轮)和 PLR(0.5%)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a9f/10007087/99a3678e158d/sensors-23-02788-g001.jpg

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