Hybrid Election-Based Ladybug Beetle Optimization for Power Consumption Minimization in Energy Efficient Resource Allocation for Software-Defined WSN

Software-Defined Wireless Sensor Networking (SDWSN) is termed a raising network structure that became more important to the Internet of Things (IoT). In this network structure, the control planes effectively manage the sensor plane. Due to these kinds of separation, the management of networks became easier and also their efficacies are enhanced in the self-motivated atmosphere. The common issue presented in the sensor atmosphere is minimal life in the network devices inspired by the maximal level of energy consumption rate. The current work proposes a system design that aims to improve efficiency in a SDWSN by combining optimization concepts. A new hybrid optimization algorithm is integrated by combining the Election-Based Optimization Algorithm (EBOA), and Ladybug Beetle Optimization Algorithm (LBOA) and the fused combination is termed as Hybrid Election-based Ladybug Beetle Optimization (HELBO). This paper analyzes an energy-efficient resource allocation algorithm in SDWSNs with computation capacity and powerful storage. These algorithms are optimizing the bandwidth and power allocation in the SDWSN to attain a considerable Signal to Interference plus Noise Ratio (SINR) under the individual constraint of quality of service. Finally, the simulation results reveal that the proposed HELBO algorithm performs better than the other existing algorithms.