Energy efficient hotspot problem mitigation techniques using multiple mobile sink in heterogeneous wireless sensor network

Abstract

Summary Wireless sensor networks (WSNs) are application‐oriented and mainly used in the following fields, such as battleground surveillance, border security, and disaster management. The sensor nodes should be extra reliable, and also, high consistency is needed to perform these tasks. Therefore, heterogeneous wireless sensor networks (HWSNs) are introduced to enhance the network lifetime and stability period by minimizing the node's communicative distance from mobile sinks (MSs). The maximum amount of energy is consumed while communicating the data from nodes to a single sink. In this work, multiple mobile sinks (MMSs) are used to transfer the data from nodes to sink with less energy consumption and a reduced number of hops. Also, higher connectivity is ensured as the sinks are placed close to the nodes, and data can be successfully delivered to sinks. This work reveals the importance of multiple MSs in HWSNs. The use of MMSs and cluster‐based routing approaches improves the efficiency of data collection in HWSNs. K‐medoid with adaptive sunflower optimization algorithm is used for the selection of cluster head (CH). To achieve energy efficiency (EE), detecting an optimal route for MS is a critical task. Then, the hybrid Harris Hawk and ant colony optimization (HANT) algorithm‐based sink moving strategy is used for finding an optimal traversal path. The proposed heterogeneous network is implemented in the network simulator (version 2) (NS2) platform. Simulation results show enhancement in EE in terms of network lifetime, stability period, several dead and alive nodes, and network's remaining energy.

Machine learning‐based clustering protocols for Internet of Things networks: An overview

“…‐ Most of K‐medoids‐based solutions consider energy efficiency 74–77,79 . Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem. The dynamic nature of the network is well managed by previous studies 76,77,79 .…”

“…‐ Most of K‐medoids‐based solutions consider energy efficiency 74–77,79 . Meanwhile, the work in Gupta et al 75 effectively mitigates the hotspot problem.…”

“…The K‐medoids clustering algorithm was used for cluster formation and CH selection, where the medoid node was selected as a CH. Gupta et al 75 offered an energy‐efficient hot‐spot problem mitigation technique based on multiple mobile sinks in heterogeneous IoS. The K‐medoids and the adaptive sunflower optimization algorithm were adopted to select effective CHs.…”