基于安全与低能耗的传感云边缘协同优化策略

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

  • 摘要: 多传感器传感网数据采集效率低下,且大量数据在传感云处理存在数据泄露风险。基于此,首先设计了一种安全、节能及高效的分布式边缘协同传感网资源选择架构,提出了一种边缘协同分析节点选择(ECANS)方案。通过对用户请求的分析,获取传感网节点的选择策略,以降低传感节点数据采集的时延和能耗。其次,构建了一种最大化隐私熵的边缘协同传感网隐私保护数据卸载模型,并通过智能启发式算法得到隐私熵最大的边缘资源选择策略。实验结果表明,与ENS数据采集方案相比,ECANS方案使节点时延与能耗分别降低了56.71%和57.66%;在边缘资源选择阶段,与GA资源选择方案和PSO资源选择方案相比,最大化隐私熵模型使系统隐私熵分别提高32.07%及15.36%;与不引入no-EC相比,传感网节点时延和能耗平均降低了46.92%与11.26%。

     

    Abstract: 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.

     

/

返回文章
返回