Elk Skill Optimizer Enabled Joint Channel Access and Energy Management Algorithm for Dynamic Channel Allocation in WSN

ABSTRACT Wireless sensor networks (WSNs) are widely used for data collection in environments with limited infrastructure and resource‐constrained sensor nodes (SNs). To meet these demands, utilizing multiple wireless channels has emerged as a promising solution to increase network capacity and reduce interference. However, exploiting multiple channels in energy‐constrained WSNs introduces significant challenges. Thus, this research introduces an elk skill optimizer (ESO) for dynamic channel allocation (DCA) in WSN. Initially, the CA system model is simulated, and channel assignment is accomplished. The channel assignment with joint power allocation, sampling rate, and transmission rate is performed employing ESO. Finally, channel assignment is conducted utilizing a deep Kronecker network (DKN) to determine the allocated channel at the next time interval. In addition, ESO obtained high performance results compared to existing schemes with maximum values of achievable rate, energy efficiency, network utility, and sum rate about 6.013 Mbits/s, 0.242 Mbits/J, 282.172, and 121.022 Mbits/s as well as minimum value of bit error rate (BER) about 0.776. Moreover, the performance gained by the proposed scheme when considering the metrics achievable rate is 30.191%, 17.354%, 28.032%, 16.520%, 14.215%, and 1.317% higher than the existing schemes used for comparison.