An In-depth Analysis of Uneven Clustering Techniques in Wireless Sensor Networks

Abstract

The low-cost and convenient feature of Wireless Sensor Networks (WSNs) has made them popular in many sectors over the last decade. The WSNs are now widely used as a result of recent advancements in low-power communication and being energy-efficient. The WSNs typically use batteries to power sensor nodes. The finite stored energy in batteries and the hassle of battery replacement have led to a critical focus on energy efficiency for WSNs. Clustering and data aggregation are the most efficient methods to address the energy concerns of WSNs. This paper comprehensively reviews several uneven clustering methods and compares the various uneven clustering algorithms. The methods are described in terms of their goals, attributes, categories, advantages and disadvantages. Probabilistic clustering is used when there is a need of simplicity and speed. As a result, this study compared all these types of protocols based on their clustering properties, CHs properties, and on the type of clustering process; and current research gap effective techniques are also addressed.

Optimal Cluster Head Selection in Wireless Sensor Network via Combined Osprey-Chimp Optimization Algorithm: CIOO

“…In recent years, a wide range of application domains, including military operations, medical care, smart cities, renewable electric power and energy, with monitoring the environment, have benefited significantly from the technological innovation and research carried out on WSNs. WSN is made up of BS and many sensor nodes [1,2]. These are tiny independent devices with many limitations, including battery life, low processing power, and short communication distance [3].…”