An Advanced Energy Efficient Resource Allocation for Software-Defined WSN Using Hybrid Optimization Algorithm

Abstract

Software-Defined Wireless Sensor Networking (SDWSN) is termed a rising network structure that has become 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 study provides a system architecture that intends to increase efficiency in an SDWSN by incorporating optimization techniques. A novel hybrid optimization strategy is created by merging the Election-Based Optimization Algorithm (EBOA) with the Ladybird Beetle Optimization Algorithm (LBOA), and the result is known as Hybrid Election-based Ladybird Beetle Optimization (HELBO). This work examines an energy-efficient resource allocation mechanism in SDWSNs that have substantial computing power and memory. These algorithms optimize the bandwidth along with 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, empirical findings show that the recommended HELBO method outperforms other present algorithms.