Energy Management Techniques of Wireless Sensor Networks for Internet of Things Applications

The development and use of new technologies such as machine learning, the Internet of Things, and machine-to-machine (M2M) networks are driving the use of large wireless sensor network (WSN) deployment. However, the main constraint in the WSN is the finite energy supply of node batteries. So, ensuring a network remains operational for the longest possible period, it relies on the effective utilization of sensor node energy in data sensing, processing, and communication tasks. To achieve this initiative, various energy management techniques for WSNs are discussed which include sleep-wake scheduling, MIMO techniques, multihop technique, energy harvesting, clustering and routing, distributed source coding, and machine learning-based WSN. The simulation results of some techniques depict that it reduces energy consumption and therefore, it helps to enhance the lifetime of WSN in the IoTs application. Maintaining the quality of service and increasing the lifetime of WSNs in various applications in a dynamic network condition is a challenging task. ML can make the computing process more reliable, energy-efficient, and cost-effective in dynamic conditions.