Energy optimization in multiple sensors based WSN

Abstract

WSNs are the cornerstone of continuous environmental observation, which introduces sensory tunnels and necessitates constant adjustability in order to gather and transmit meteorological information to the base station. Interest in low-power (WSN) wireless networks has increased as a result of the development of the Internet of Things (IoT). These networks are utilized for tasks including data gathering, process monitoring, and independent work management in a number of industries, such as the military, transportation, and health care. When wireless nerves in wireless neural networks are powered by batteries, their health and performance deteriorate. By using energy from the vicinity of the sensor to power the device, it is feasible to increase the sensor's lifespan while still achieving environmental performance. The expense of replacing batteries is drastically reduced by the use of energy harvesters in environmental field structures that power sensory hubs less frequently. Familiarity with the data collected and setting the start and end power thus charging the battery automatically when power reaches the limit value.