Performance Improvement of the Wireless Sensor Network with Proficient Power Management with Supervised Multimodel Data Regression Algorithm In Precision Agriculture

Wireless sensor network (WSN) technology has helped industrial automation, agriculture, smart cities, environmental monitoring, target tracking, structural health monitoring, healthcare, and military applications. Due to energy limits, WSNs powered by batteries have a limited lifetime. By harvesting energy from the environment, energy harvesting technology promises to reduce the load of replacing or recharging depleted batteries for sensor nodes. Ultra-low power solutions aim to extend the entire sensor network lifetime by reducing energy consumption in the WSN. The development of Dynamic Power Management strategies can improve the performance and lifetime of Energy Harvesting Wireless Sensor Networks (EHWSNs). Energy conservation and management are key concerns in EHWSNs, necessitating the development of energy harvesting-aware protocols and algorithms that allow for continuous network operation. The proposed Supervised Multi-Model Data Regression Algorithm (SMDR) technique is based on the earlier works. The proposed approach is utilized both acoustic sensor, it is an insect pest detection sensor which works by monitoring the noise level of the insect pests. These sensors help detect an infestation at a very early stage, thus greatly reducing crop damage.