A Secure Data Aggregation Strategy in Edge Computing and Blockchain-Empowered Internet of Things

【Author】 Wang, Xiaoding; Garg, Sahil; Lin, Hui; Kaddoum, Georges; Hu, Jia; Hossain, M. Shamim

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a blockchain-based secure data aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers [i.e., mobile data collectors (MDCs)] to search and accept tasks, the block header is intergraded with a security label including task security level (SL) and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed to design energy-efficient MDC routes under the constrains that the SLs of MDCs should be higher than the SLs of data aggregation tasks. Simulation results indicate that 1) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency and 2) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

【Keywords】Task analysis; Data aggregation; Receivers; Edge computing; Internet of Things; Blockchain; data aggregation; deep reinforcement learning (DRL); edge computing; Internet of Things (IoT)

【发表时间】2022 AUG 15

【收录时间】2022-08-28

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-边缘计算