Abstract
Clustering is an efficient method for creating routing algorithmsin Wireless Sensor Networks (WSNs), which increases the network's lifetime and scalability. Considering the limited capabilities of sensor nodes, such as energy, processing power and communication range, clustering-based routing protocols accommodate the network’s operation with these constraints. Recent related works have proven that the energy consumption of sensor nodes can be minimized if efficient clustering methods are incorporated. In the clustering method, Cluster Head (CH) selection and cluster formation play a vital role in data transmission. This paper proposes a novel hybrid Improved Version of Binary Dragonfly Algorithm (IVBDA) and Mamdani fuzzy inference system for clustering protocols in WSNs. In this approach, first, IVBDAis used to choose CHsand then the Mamdani fuzzy inference system is used to structure clusters. Finally, a multi-hop routing process is used to transmit data packets. The proposed clustering protocol has been simulated on WSNs with different topologies. The performance of the clustering protocol is evaluated using the average energy consumption, the number of live nodes, the network lifetime and the number of packets received by the Base Station (BS). The evaluation results show that the proposed clustering protocol has better performance than the previous protocols.