An integrated model for energy conservation in IoT-enabled WSN using adaptive regional clustering and monkey inspired optimization

Abstract

The advancement of the Internet of Things (IoT) technologies will play a significant role in the evolution of the smart city, smart healthcare, and smart grid applications. The key objective of IoT is to allow the autonomous exchange of valuable data between invisibly embedded devices with the help of some prominent technologies. Wireless Sensor Network (WSN) is one of the emerged technologies used for sensing and data exchange processes in IoT-based applications. Network sustainability and energy stability are the most significant multi-objectives to attain an energy-efficient IoT-based WSN (IWSN). Consequently, in order to handle these multi-objectives, a novel Adaptive Regional Clustering (ARC) scheme has been proposed in this paper by exploiting two appropriate methodologies. Primarily, location-based modelling is employed to gather the location information from each sensor node in the IWSN environment. Thereafter, an effective hierarchical clustering can be carried out with the assist of the ARC algorithm. The cluster head will be chosen based on node capacity and node trust value by implementing the Enhanced Monkey Inspired Optimization (EMIO) algorithm. Finally, the optimal cluster head node acts as an energy-efficient local director for conducting inter-cluster connectivity, data transmission, and other duties. The effectiveness of the proposed ARC-EMIO scheme has been assessed using the NS-3 simulator and the results evident that the proposed scheme guarantees better performance with an improved network lifetime of 35% and energy efficiency of 22% when compared with the existing state-of-the-art clustering techniques.