A COMPREHENSIVE SURVEY AND ANALYSIS FOR ENERGY CLUSTERING ALGORITHMS USING DATA TRANSFER FOR WIRELESSSENSOR NETWORKS

Abstract

In recent years, Wireless Sensor Networks (WSNs) have become increasingly integral to various applications, from environmental monitoring to industrial automation. The challenge of energy efficiency remains paramount, as sensor nodes are often deployed in environments where battery replacement is impractical. This work presents a comprehensive survey and analysis of energy clustering algorithms, focusing on data transfer methodologies within WSNs. The primary objective of these algorithms is to optimize energy consumption, thereby prolonging the network's operational lifespan. This survey categorizes energy clustering algorithms into several key types, including hierarchical, location-based, and hybrid approaches. Hierarchical clustering algorithms, such as Low-Energy Adaptive Clustering Hierarchy (LEACH), Improved low-energy adaptive clustering hierarchy (ILEACH), and Hybrid-Partial Swarm Optimization (PSO)-ILEACH algorithms, are explored in detail. These algorithms emphasize the formation of clusters and the selection of cluster heads to minimize intra-cluster communication costs and reduce the overall energy expenditure. Location-based clustering algorithms leverage geographic information to form clusters, thereby optimizing data routing paths and minimizing energy usage. Additionally, hybrid algorithms combine elements of hierarchical and location-based approaches to further enhance energy efficiency. The survey also examines the role of data transfer mechanisms in energy-efficient clustering. Efficient data aggregation and compression techniques, along with multi-hop communication protocols, are crucial for reducing redundant data transmission and conserving energy. The works discuss various data aggregation strategies, including tree-based, cluster-based, and hybrid aggregation models. Moreover, the impact of communication protocols, such as Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), on energy efficiency is thoroughly analyzed. Furthermore, the survey identifies emerging trends and challenges in the field of energy-efficient clustering for WSNs. Also, this survey identifies emerging trends and challenges in the field of energy-efficient clustering for WSNs. The integration of machine learning and artificial intelligence techniques for adaptive clustering and predictive maintenance is discussed as a promising direction for future research. This Work proposes a framework for the systematic evaluation and benchmarking of clustering algorithms, which could serve as a valuable tool for researchers and practitioners in the field.