Caddisfalcon optimization algorithm for on-demand energy transfer in wireless rechargeable sensors based IoT networks

Abstract

Internet of things networks increase the demand for innovative applications due to their interdisciplinary research contributions. However, the internet source for remote applications still depends on the Wireless Sensor Networks (WSNs). Implementing the Wireless Rechargeable Sensor Networks (WRSN) with Wireless Energy Transfer (WET) can eradicate energy limitation issues and network lifetime in IoT applications. In recent years, WRSNs have steadily increased in popularity and have been responsible for significant advances in wireless communication. These accomplishments can be attributed to inexpensive and energy-efficient sensors. However, since WRSN nodes are battery-powered, they lose energy periodically. This energy constraint affects the network’s durability. To increase the network lifetime in WRSN, this research proposes an efficient energy transfer routing protocol based on Caddisfly and Falcon lifespan-based Optimization algorithms for transferring energy from the best node to the needy node through the optimal path. The caddisflies optimization algorithm is used to find the most energy-effective node among all the other nodes in the sensing region based on the residual energies. In contrast, the Falcon optimization algorithm finds the optimal path from the best node to the needy node. Simulated results are proved the improvement and contributions of the proposed algorithm and real-time implementation of the proposed model and its results are also compared with other related models to prove the performance.