Energy-Efficient Routing Protocol Based on Multi-Threshold Segmentation in Wireless Sensors Networks for Precision Agriculture

TL;DR

This paper introduces an energy-efficient routing protocol for wireless sensor networks in precision agriculture, using multi-threshold segmentation for better cluster formation and cluster head selection to extend network lifetime.

Contribution

It proposes a novel clustering algorithm inspired by image segmentation and an optimal cluster head selection method based on residual energy and location.

Findings

Improves cluster head distribution uniformity

Prolongs network lifetime significantly

Reduces energy consumption by up to 64.50%

Abstract

Wireless sensor networks (WSNs), one of the fundamental technologies of the Internet of Things (IoT), can provide sensing and communication services efficiently for IoT-based applications, especially energy-limited applications. Clustering routing protocol plays an important role in reducing energy consumption and prolonging network lifetime. The cluster formation and cluster head selection are the key to improving the performance of the clustering routing protocol. An energy-efficient routing protocol based on multi-threshold segmentation (EERPMS) was proposed in this paper to improve the rationality of cluster formation and cluster head selection. In the stage of cluster formation, inspired by multi-threshold image segmentation, an innovative node clustering algorithm was developed. In the stage of cluster head selection, aiming at minimizing the network energy consumption, a calculation theory of the optimal number and location of cluster heads was established. Furthermore, a novel cluster head selection algorithm was constructed based on the residual energy and optimal location of cluster heads. Simulation results show that EERPMS can improve the distribution uniformity of cluster heads, prolong the network lifetime and save up to 64.50%, 58.60%, and 56.15% network energy as compared to RLEACH, CRPFCM, and FIGWO protocols respectively.