AN ENERGY-EFFICIENT HYBRID CLUSTERING TECHNIQUE (EEHCT) FOR IOT-BASED MULTILEVEL HETEROGENEOUS WIRELESS SENSOR NETWORKS

Heterogeneous Wireless Sensor Networks (HWSN), which are based on the Internet of Things (IoT), have become a popular technology that are important for creating a range of applications that are focused on people. Similar to a wireless sensor network (WSN), the most important resource in an Internet of Things-based HWSN is energy. In order to minimize energy consumption and achieve energy-efficient network operations, numerous proposals have been made by the researchers. A significant amount of these efforts focus on applying the clustering strategy, which has shown to be highly beneficial. But the majority of schemes necessitate the recurring cluster construction, which consumes a large quantity of nodes' energy throughout the clustering process. Such schemes also have different protocol designs according to the varying degrees of heterogeneity. Using a mixed clustering approach, this work for IoT-based HWSN, an Energy-Efficient Hybrid Clustering Technique (EEHCT) has been presented that reduces energy consumption during cluster formation and equally distributes the network load regardless of the degree of heterogeneity to increase network lifetime. The network's load-balanced clusters are created by suitably combining static and dynamic clustering techniques. Through a comprehensive collection of simulations and experiments, EEHCT proves its superiority over state-of-the-art schemes in terms of several network performance measures, including stability, throughput, and network longevity. In terms of network longevity, for example, it outperforms its counterparts by up to 90.27% both under typical operating settings and with different network setups. To illustrate, statistical analysis has been supplied in addition to quantitative data.

Keywords: Wireless Sensor Network, Node Coverage, Grey Wolf Algorithm, Routing Protocol, Monitoring Area.