Cost‐Efficient Network Design in Multichannel WSNs With Power Control: A Grey Wolf Optimization Approach to Routing and Clustering

Abstract

Wireless sensor networks (WSNs) require optimized energy consumption and an improved packet delivery ratio (PDR) for optimal performance. Clustering and routing strategies are frequently used to reduce energy usage, while multiradio (MR) multichannel (MC) systems improve PDR levels. Transmission power control (TPC) features in both energy consumption reduction and PDR improvement. While the mentioned techniques are used in previous studies, they were investigated separately and without a holistic approach. This study discusses the challenge of obtaining high‐PDR, energy‐efficient, and cost‐effective clustering and routing in WSNs. As a means of reducing deployment costs, a heterogeneous setting with energy‐constraint normal sensors for environmental monitoring as well as some high‐energy, TPC‐enabled, MR super nodes is assumed. The super nodes are considered as CHs and are responsible for collecting the sensed data from the normal sensors. Installing additional radios on super nodes enables static channel assignment (CA), which yields high PDR with low imposed overhead on the network. The mentioned TPC‐based MC heterogeneous setting, which yields cost‐effectiveness, high PDR, and energy efficiency, was not investigated in previous studies. The considered problem is decoupled into two phases of configuring the super nodes and normal sensors, which are solved using the grey wolf optimization (GWO) algorithm. The performed simulations show an average improvement of our proposed algorithm in PDR, energy consumption, consumed energy per delivered bit, and network lifetime by 9%, 30%, 65%, and 44%, respectively.