The Creation of Solar-Powered, Energy-Efficient Wireless Sensor Network Topologies That Use Advanced Algorithms, Artificial Intelligence for Energy Forecasting, And Machine Learning Techniques

Microgrids are miniature, decentralized power systems connected to the larger electrical grid, employing various components to monitor energy usage and environmental variables. Their adoption in residential areas has been limited due to installation and maintenance challenges. Integrating ambient-powered, wireless sensors could enhance their functionality, reducing energy waste by harvesting power from environmental sources. A combination of energy harvesting technologies and battery power is suggested for optimal performance, potentially utilizing everyday components for cost efficiency. Additionally, the thesis explores advancements in "smart agriculture," which incorporates renewable energy, automated systems, and innovative sensors to improve farming efficiency and sustainability. Key areas include soil quality monitoring, pest detection, and the application of AI and big data analytics, aiming to provide insights into agricultural innovation for academics and professionals.