The Wireless Sensor Networks (WSNs) have become the most cost-effective monitoring solution due to their low cost, despite their major drawback of limited power due to dependence on batteries. Clustering is one of the successful solutions for extending the life of the WSN. Where optimal CH selection results in an increase in the lifetime of WSN. The paper discusses a strategy for field deployment of nodes in the form of clusters and a method for Cluster Head (CH) selection using Particle Swarm Optimization (PSO). The method employs a fitness function to select the optimum CHs for cluster nodes. This paper simulates a case study for the early detection of forest fires and demonstrates how to extend the lifetime of a WSN by utilizing the proposed PSO algorithm.

In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.