An Energy Efficient Naïve Bayes-based Clustering Protocol for Wireless Sensor Network

Wireless sensor networks (WSN) play a major role in the development of a variety of smart systems, including health, medical, and military applications. A WSN is made up of small sensor nodes (SNs), which sense data for these applications. It is critical for these sensitive applications that sensor networks maintain information transmission. The SNs sense the information, process it, and then transmit it to the base station (BS). While performing these tasks, a lot of power from the SNs gets consumed. However, these SNs are equipped with a limited number of batteries. As a result, after a time, these nodes become dead, due to which the whole network gets compromised. These SN batteries are also non-rechargeable and non-replaceable. As a result, preserving the energy of these SNs becomes more difficult. One of the major issues is determining the optimal cluster head (CH). However, in this paper, the solution of the optimal CH node is given by using a Naïve Bayes classifier algorithm. As a result, we have developed an energy-efficient Naïve Bayes-based clustering protocol (EENBCP) for WSN. Extensive simulations have revealed that the suggested approach conserves energy more efficiently and increases the lifespan of the network.