Energy-Efficient Techniques in IoT-based Software-Defined Wireless Sensor Networks: A Systematic Review

In recent years, the number of Internet of Things (IoT) devices are increasing rapidly due to their vast applications and the convenience they offer. This growth has prompted a significant interest in developing energy-efficient techniques to manage the power consumption of these devices, ensuring sustainability and reducing the ecological footprint associated with their widespread use. The integration of IoT with Software-Defined Wireless Sensor Networks (SDWSNs) enhances network programmability and simplifies the management of large-scale sensor networks. This survey paper presents a systematic review of energy-efficient clustering techniques in IoT-enabled SDWSNs. The review categorizes existing approaches into classical and meta-heuristic-based methods and also highlights their strengths and limitations. Classical approaches like LEACH paved the way for energy-efficient clustering, while meta-heuristic techniques such as PSO, GWO, and ACO optimize cluster head selection and routing performance. This paper focuses on how dynamic clustering techniques can improve network performance by addressing challenges such as high energy use, uneven load distribution, and unreliable communication. Finally, the paper highlights future research possibilities, such as integrating edge computing, advanced deep learning models, and sustainable networking practices to meet the growing needs of next-generation IoT networks.