Optimizing Wireless Sensor Networks: A Survey of Clustering Strategies and Algorithms

Wireless Sensor Networks (WSNs) are essential for real-time data collection and monitoring in various fields, such as environmental sensing, healthcare, industrial automation, and military surveillance. Energy management is very important to the WSNs lifetime and performance since the sensor nodes use batteries and often deployed in areas that are difficult to access. Clustering has become a vital technique in the control of energy demands with the various sensor nodes being grouped in several clusters under the supervision of a cluster head. Clustering helps in the distribution of energy evenly in the network minimizing the number of unnecessary transmissions. This study highlights clustering techniques and methods that have been developed for WSNs, together with their objectives, concepts, and consequences on the performance of networks. The clustering strategies are categorized by employing several methods; these are hierarchical, distributed, centralized, and hybrid approaches. Every clustering technique has its benefits and drawbacks. The choice of the best-fit approach determines on the actual needs of the WSNs. The discussion explains the strategies of different algorithms, advantages, and disadvantages. Moreover, the issues discussed in the study address the present-day concerns and the future research trends of enhancing clustering algorithms of WSNs. The presented work contributes to the understanding of how to choose and enhance clustering approaches to enhance WSNs effectiveness and longevity. This study serves as a helpful source of knowledge that can encourage the further development of the enhancement of clustering algorithms for WSNs in response to modern technology needs.