Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Sasikumar Gurumoorthy; Parimella Subhash; Rocio Pérez de Prado; Marcin Wozniak

doi:10.3390/s22249921

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Sensors (Basel). 2022 Dec 16;22(24):9921. doi: 10.3390/s22249921.

Authors

Sasikumar Gurumoorthy¹, Parimella Subhash², Rocio Pérez de Prado³, Marcin Wozniak⁴

Affiliations

¹ Department of Computer Science and Engineering, Jerusalem College of Engineering, Chennai 600100, India.
² Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology (VNRVJIET), Hyderabad 500090, India.
³ Telecommunication Engineering Department, University of Jaén, 23700 Linares, Spain.
⁴ Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland.

Abstract

Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

Keywords: RSSI; WSN; improved DCNN; security; trust evaluation.

MeSH terms

Algorithms
Cluster Analysis
Computer Communication Networks*
Neural Networks, Computer
Wireless Technology*

Grants and funding

The authors acknowledge contributions to this project from the Rector of the Silesian University of Technology under proquality grant no. 09/010/RGJ22/0068. In addition, this research is supported by Spanish Research Projects P18-RT-4040 and PID2020-119082RB-C21.