Route Optimization and Optimal Cluster Head Selection in Wireless Sensor Network Using Fractional Tasmanian Hyena Optimization

The advancement of wireless sensor networks (WSNs) has been especially significant in smart computing, where numerous application areas have emerged. These networks comprise self-configured and low-power, smaller sensor nodes. The network lifetime can be affected by the existence of unbalanced nodes due to their increased power consumption. The development of an energy-efficient routing protocol in WSN remains a challenging task. One of the possible ways to address the challenges is to develop clustering techniques with minimal energy consumption. The conventional clustering techniques do not focus on selecting the optimal node as the Cluster Head (CH) for data transmission. Hence, this research developed the Fractional Tasmanian Hyena Optimization (FTHO) for establishing the routing through the selected CH. Here, the FTHO is developed by the combination of Fractional Calculus (FC) and Proposed Tasmanian Hyena Optimization (THO). In order to achieve this, the WSN nodes are simulated, and then clustering is performed by utilizing the Fisher Median Naive Sharding (FMNS)-K-Means algorithm. The optimal CH is selected by the THO with the fitness measures, such as delay, predicted energy, and distance. The experimental results demonstrate that the FTHO achieved the maximum residual energy, Packet Delivery Ratio (PDR), and throughput of 18.213 J, 0.970, and 0.993Mbps, and the minimal delay, distance, and trust of 0.658 ms, 39.30 m, and 84.41.