Abstract

In many sectors like healthcare, military, automotive sector, and manufacturing, the wireless sensor networks (WSN) have been widely used. Regardless of its widespread applications, WSN also have some limitations. Those limitations include processing power, storage capacity and energy supply (ES). Here, the ES is one of the major challenges in WSN. To address this issue, the WSN aims to enhance the energy efficiency (EE). Then, the data aggregation (DA)-based clustering technique is suggested for resolving those challenges, as it balances energy consumption (EC) across sensor nodes (SN). This will facilitate the suggested method in improving EE. For the purpose of selecting cluster heads (CH) effectively, a robust search algorithm and faster convergence are crucial. An adaptive metaheuristic (MH) algorithm (AMHA) based on Tunicate Swarm Optimisation Algorithm (TSOA) is suggested, and it may support in optimizing deep foundation design and global optimization. In every iteration, 2 crucial phases are included in the suggested Adaptive TSOA (ATSOA). Those steps include a local refinement based on the top-performing tunicate (TC) and a global search (GS) directed by randomly chosen TC. Thus, premature convergence is prevented by these changes, and it also supports in enhancing the exploration capabilities of the model. To enhance the convergence speed and optimise search accuracy (ACC), a new hybrid method (CSBHC) was suggested. The Cuckoo Search (CS) and (BHC) ß-Hill Climbing are integrated in this CSBHC method. The benefits of the CS algorithm (CSA) with the BHC method are integrated in the CSBHC method, as similar to probability mechanism in (SA) Simulated Annealing. On the basis of an exponentially decreasing probability, it becomes active at every repetition. The search efficiency is greatly enhanced by the suggested method, and it was demonstrated by the comparative tests with different node density (ND). Thus, the routing performance and effective CH selection (CHS) are improved by the suggested method.

Authors

N.S. Kavitha¹, R. Rathiya², M. Sakthivel³
Dr. N.G.P. Institute of Technology, India^1,2, Erode Sengunthar Engineering College, India³

Keywords

Wireless Sensor Networks (WSNs), Tunicate Swarm Optimization (TSA), Adaptive Tunicate Swarm Optimization (ATSA), Cuckoo Search with ß-Hill Climbing (CSBHC)