Clustering in Wireless Sensor Networks Using Fuzzy Logic for Clusterhead Election: Review

Wireless sensor networks refer to a collection of sensors strategically deployed in specific areas for purposes such as environmental monitoring, surveillance, military applications, and various other uses. These sensor nodes are responsible for detecting information and transmitting it to a central base station. The sensors are generally battery operated and cannot be rechargeable. Since monitoring and data transmission processes consume a significant amount of energy, achieving energy efficiency is a primary concern. Therefore, clustering has become the most popular approach to improving energy efficiency. This approach consists of dividing the network into clusters, each characterized by a cluster head sensor and several member sensors. The cluster head assumes the responsibility of collecting data from member sensors within its cluster, aggregating this data and then transmitting it to the central base station. The central aspect of this clustering approach lies in the selection of the optimal cluster head, as it significantly influences the performance and lifetime of the network. To address this challenge, various protocols have been introduced using fuzzy logic, which has become one of the most widely used methodologies in this context due to its ability to handle uncertain and ambiguous inputs, closely mimicking human reasoning processes. This survey paper aims to provide a comprehensive overview of clustering techniques that use fuzzy logic for selection of cluster heads, using a range of inputs such as residual energy levels, distance to node receiver, network density, and other relevant criteria.