Energy aware metaheuristic optimization based clustering with multihop routing techniques for wireless sensor networks

Abstract

Advancements in sensing and communication technologies resulted in the newlinedesign of wireless sensor networks (WSNs) for low-cost distributed monitoring newlinesystems. WSN comprises a massive number of tiny and low power sensors, which newlineundergo deployment either randomly or manually over the target region. WSN finds newlinesuitability in standard applications in environmental observation, disaster management newlinesystems, healthcare, climatic forecasting, military service, healthcare, smart home, newlinetarget observing, traffic observation, free management, farming verification, industrial newlinedamage prediction, and power management. Since the nodes in WSN are limited to newlineenergy and deployed in harsh environments, it is hard to recharge or replace the inbuilt newlinebatteries in the sensor nodes. Therefore, effective utilization of available energy is a newlineproficient way to accomplish enhanced energy efficiency and network lifetime (NLT). newlineClustering and routing are the popular energy-efficient techniques offering several newlinemerits such as energy efficiency, network longevity, scalability, and less latency. newlineDuring the clustering process, the sensor nodes are organized into groups termed newlineclusters. The regular nodes in the cluster are called cluster members (CM) and a Cluster newlineHead (CH) is chosen among them. newlineThe benefits of clustering are minimum energy consumption with improved newlinebandwidth utilization, reduced overhead, increased connectivity, stabilized network newlinetopology, decreased delay, effective load balancing (LB), and reduction in the size of newlinethe routing table. At the same time, improper selection of CHs and possible routes to newlinebase station (BS) results in high energy utilization and reduced lifetime of WSN. newlineAppropriate CHs and optimal route selection processes can be assumed as the nondeterministic newlinepolynomial-time hardness (NP hard) optimization problem. NP-hard is newlinethe defining property of a class of problems that are informally quotat least as hard as the newlinehardest problems in NPquot, which can be resolved by the use of metaheuristic algorithms newlinesuch as ant colony op