Dual-Level Sensor Selection with Adaptive Sensor Recovery to Extend WSNs’ Lifetime

Wireless sensor networks (WSNs) have garnered much attention in the last decades. Nowadays, the network contains sensors that have been expanded into a more extensive network than the internet. Cost is one of the issues of WSNs, and this cost may be in the form of bandwidth, computational cost, deployment cost, or sensors’ battery (sensor life). This paper proposes a dual-level sensor selection (DLSS) model used to reduce the number of sensors forming WSNs. The sensor reduction process is performed at two consecutive levels. First, a combination of the Fisher score method and ANOVA test at the filter level weighs all the network sensors and produces only a reduced set of sensors. Additionally, the grey wolf optimizer algorithm produces the optimum sensor subset, while an adaptive sensor recovery solution is proposed to extend the network lifetime even longer using sensors failure management. The proposed model performance is evaluated using four different datasets. In comparison with to other similar methods, the results indicated that the proposed model achieved a more efficient subset of sensors preserving a high accuracy rate. © 2022. Human-centric Computing and Information Sciences.All Rights Reserved