A Grey-Wolf based Optimized Clustering approach to improve QoS in wireless sensor networks for IoT applications

Abstract

Wireless sensor networks (WSNs) have gained much attention in public and research communities due to their incredible capabilities and ever-growing range of applications. The WSN is equipped with a specialized transducer that adds the sensing services to IoT. This equipment is limited to battery and resource capacity, which introduces many challenges to academia and industry. Hence WSN has to be utilized in an energy-efficient manner to maximize the network’s lifetime while providing a precise QoS guarantee. QoS is an essential issue in many IoT applications such as environmental monitoring, smart cities, weather monitoring, animal tracking, disaster management, bio-medical applications. An optimal clustering technique for WSN, which includes the formation of clusters, and cluster head (CH) selection, can significantly improve the QoS to increase the lifespan of a WSN. This paper proposes a Grey wolf optimization-based cluster head selection technique for WSN considering distinct factors like energy level of the node, node degree, sink distance, intracluster distance, and priority factor. This paper also addresses the routing through QoS aware relay node selection for effective and reliable inter-cluster routing from CHs to Base station (BS). The proposed technique is simulated and evaluated based on the Quality of Service (QoS) parameters viz. residual energy, stability period, throughput, network lifetime, and delay. The proposed techniques improve the overall network performance by 10.00%, 23.75%, and 54.54% corresponding to ESO, GECR, and LEACH. Hence, the study infers that the protocol is well suited to design WSNs in IoT applications.