ENERGY EFFICIENT MULTI-QoS CENTRIC ROUTING ALGORITHMS IN IoT-BASED SOFTWARE DEFINED WIRELESS SENSOR NETWORKS

Abstract

The rapid expansion of the internet of things has led to the widespread deployment of sensor nodes, creating a need for energy-efficient and scalable communication frameworks. Software-defined wireless sensor networks have emerged as a promis- ing solution by offering centralized control, dynamic reconfiguration, and enhanced flexibility. However, these networks face critical challenges, including limited battery life, routing inefficiencies, and the need to meet multiple quality of service constraints. This thesis addresses these challenges by proposing a set of novel, energy- efficient, multi-QoS-centric routing algorithms for IoT-enabled SDWSNs. A detailed systematic literature review is conducted to classify existing clustering and routing methods into classical, metaheuristic-based, and machine learning-based categories, highlighting research gaps. To overcome existing limitations, three metaheuristic-based protocols are developed: the Energy-Optimized Artificial Hummingbird Algorithm (EOAHA), a Nature-Inspired Multi-Objective Green Routing Protocol (EO-C), and a Multi-Constrained Hybrid Protocol (EQ-AHA). These algorithms are designed to optimize cluster head selection and routing paths by using multi-objective fitness functions based on energy, distance, load, and network stability. Extensive simulations and performance evaluations confirm that the proposed models significantly outperform existing state-of-the-art techniques in terms of network lifetime, energy consumption, data delivery, and robustness. The research offers valuable contributions to the development of intelligent, adaptive, and sustainable routing protocols for future IoT-enabled smart environments.