Collaborative Optimization Strategy of Edge Sensor Cloud Based on Security and Low Energy Consumption

There are two problems to be solved in multi-sensors wireless sensor networks: low data collection efficiency and the risk of data leakage when a large amount of data is processed in sensor cloud. Owing to these reasons, we devise a safe, energy-saving, and efficient distributed edge collaborative sensor network resource selection architecture firstly. Secondly, to address first problem, an edge analysis node selection (edge collaborative analysis node selection, ECANS) algorithm is proposed. Through the analysis of user requests, the best strategy of sensor network nodes is obtained to reduce the node's delay and energy consumption of data collection. Aiming at the second problem, an edge collaborative sensor network privacy protection data offloading model is constructed to maximize privacy entropy, and the edge resource selection strategy with the largest privacy entropy is gained through intelligent heuristic algorithm. Al last, experimental results show that ECANS algorithm can reduce node delay and energy consumption by 56.71% and 57.66% compared with effective node sensing (ENS) data collection methods. In the edge resource selection stage, the maximum privacy entropy model makes the system privacy entropy increased by 32.07% and 15.36%, compared with genetic algorithm (GA) resource selection scheme and particle swarm optimization (PSO) resource selection scheme. The latency and energy consumption of the sensor network were reduced by 46.92% and 11.26% compared with no-EC. © 2023 Univ. of Electronic Science and Technology of China. All rights reserved.