Research on sensor networks optimization based on edge computing

A sensor network is a novel type of distributed network comprising sensor nodes that perform data collection, information processing, and communication functions. The network is employed in a multitude of fields, including earthquake monitoring, agricultural production, smart homes, and battlefield reconnaissance. In this work, we propose a novel optimization model that employs edge computing nodes for data preprocessing and distribution in sensor networks. This approach markedly diminishes the computational load on the central server by conducting data processing and filtering at the network's edge, consequently reducing the volume of data that must be transmitted to the central server. In particular, we have developed a collaborative computing algorithm that allows multiple edge nodes to share computing tasks, thereby enhancing the speed and accuracy of data processing. Furthermore, the dynamic resource allocation algorithm is employed to adaptively adjust the resource allocation of edge nodes in response to real-time network conditions and task requirements, thereby enhancing the overall efficiency and reliability of the network. Our innovative approach to data preprocessing, including advanced filtering and compression at the edge nodes, and our dynamic resource allocation strategy, which adaptively adjusts resources based on real-time network conditions and mission needs, is significantly better than existing technologies. The experimental analysis indicates that the proposed method exhibits superior performance in terms of data processing speed and network latency compared to the traditional sensor network architecture.