An optimum energy efficient approach for sensor clustering to improve network lifetime in WSN

Wireless sensor network are being utilized in several applications fields such as health care facility, agriculture, military applications, etc. However, the battery incorporated in the system is small in size and has a low power rating which lead to reduced network lifespan. Therefore it is a challenging task for the current wireless networks to utilize the sensor’s energy for longer period. In order to overcome these limitations, a number of approaches were proposed in recent years, but the issue that was analyzed in the current approaches were that they were focused on selection of cluster head on basis of convention probability basis approaches. In order to upgrade the resource utilization an energy-efficient control technique that focuses on lowering complexity and increasing network longevity is proposed in this paper. The proposed technique employs a K-Mean clustering approach to form clusters based on network density. In addition to this, a Whale Optimization Algorithm (WOA) is utilized, which considers various QoS parameters such as node residual energy, communication distances and delay and node density. The suggested scheme is tested using the MATLAB software, and performance factors are compared to traditional techniques. Finally, the proposed strategy shows promising results in terms of increasing network lifetime with approx 300 communication rounds, along with less energy consumption, and higher network stability.