A Study on AI-Enabled Energy-Efficient Wireless Sensor Networks with Edge Computing for Smart Environmental Monitoring
Keywords:
Artificial Intelligence; Edge Computing; Wireless Sensor Networks; Energy Efficiency; Smart Environmental MonitoringAbstract
The rapid growth of Internet of Things (IoT), Wireless Sensor Networks (WSNs), Artificial Intelligence (AI), and Edge Computing has enabled the development of intelligent and sustainable environmental monitoring systems. Traditional WSN-based monitoring approaches face challenges related to limited energy resources, high communication overhead, latency, and restricted processing capabilities. This conceptual study explores the integration of AI-enabled energy-efficient WSNs with Edge Computing for smart environmental monitoring applications. The study examines how AI techniques enhance sensor data analysis, prediction, anomaly detection, and decision-making, while Edge Computing enables real-time processing by reducing dependence on centralized cloud platforms. Energy optimization approaches such as efficient routing, clustering, adaptive sensing, and resource management are discussed to improve network lifetime and operational efficiency. The study proposes a conceptual framework combining AI-driven analytics, edge intelligence, and energy-efficient WSN architectures to achieve reliable, scalable, and real-time environmental monitoring. The integration of these technologies provides significant potential for applications including smart cities, pollution monitoring, climate observation, and sustainable environmental management.
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