Energy Efficiency Techniques in Wireless Sensor Network for Sensor Nodes

Wireless Sensor Networks (WSNs) are an emerging field of technology with a wide range of applications, namely, smart cities and environmental monitoring. The network’s long-term sustainability and the uninterrupted functionality of sensor nodes suffer tremendously due to their energy constrained nature. In recent years, new developments on ways of using energy efficiently have played a great role in solving these problems by optimizing resources, increasing network lifespan and improving data transfer techniques. Notable improvements are the sophisticated techniques of scavenging for energy that draw upon solar, thermal, vibration and different environmental energy sources that were used to supply sensor nodes with energy. Machine learning techniques, most notably reinforcement learning and predictive analytics, have become common in the management of energy in a dynamic fashion as well as optimizing the network's duty cycle. To these, it is noteworthy the progress made in energy efficient routing protocols such as multi-path and energy balanced clustering which helps reduce energy loss during data transmission. Innovative sleep scheduling schemes and data aggregation methods also help to conserve energy by shortening the on time of sensor nodes and lessening the number of active data transmissions. In addition, new developments in hardware design such as ultra-micro low-power microcontrollers and small power transceivers have resulted in lower power consumption of sensor nodes. These combined efforts directed to energy efficiency are key in increasing the operating time of WSNs and improving their efficiency by facilitating better distribution in different areas of use and application.