Environment Monitoring based on the Integration of Edge Computing and Multimodal Sensors

Environmental monitoring plays a crucial role in modern urban development, yet it faces challenges related to the accuracy of data collection and the battery life of sensor nodes. This study investigates the design and implementation of an environmental monitoring system that integrates edge computing and multimodal sensors. The paper proposes the combination of edge computings low latency and distributed processing capabilities with the diversity and data redundancy of multimodal sensors to achieve efficient and precise environmental monitoring. The system employs the LSTM (Long Short-Term Memory) time series prediction model to dynamically adjust the sampling frequency of sensors, thereby optimizing energy consumption. Experimental results indicate that this method significantly improves the real-time responsiveness, accuracy, and battery life of the environmental monitoring system. This study makes significant contributions to the field of environmental monitoring by addressing technical challenges, economic feasibility, and social impacts, thus providing strong support for the sustainable development of smart cities.