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

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.

I. Introduction

With the rapid development of mobile devices (e.g., smartphone, smartwatch, tablet, etc.), a tremendous amount of data are generated everyday in Internet of Things (IoT) [1]. These data are aggregated by workers [i.e., mobile data collectors (MDCs)] and further analyzed for industrial applications. In the data aggregation process, there are two main concerns that worth our attention. The first one is how to aggregate data without privacy disclosure. For example, task releasers, who post data aggregation tasks, cannot tolerate the leakage of sensitive information contained in the task. Therefore, they prefer to choose trustworthy workers to fulfill the task. Note that as integration of distributed ledger, smart contract, peer-to-peer network, and consensus mechanism, the blockchain [2] can provide reliable access control, secure storage, and distributed computation. That suggests the privacy concern should be addressed by applying blockchain to data aggregation task design, i.e., a deliberately modified blockchain can restrict workers to the tasks of certain security levels (SL) and completion requirements (CRs). The second concern is how to be energy efficient due to data aggregation may cost workers a significant amount of energy. That suggests the data aggregation route should be designed with less energy cost. As a distributed open platform, the edge computing [3], which has been widely used in smart grids, healthcare, smart home, etc, integrates computing, storage, and applications to provide edge intelligence services. Therefore, edge computing can offer high-performance calculation for energy-efficient route design. Although plenty of works have been proposed to achieve secure data aggregation, only a few of which consider both SL-based task classification and energy-efficient task fulfillment. In this article, we propose a blockchain-based secure data aggregation strategy (BSDA) for edge computing empowered IoT. The details of our contributions are listed as follows. 1)

To prevent privacy disclosure, the security label, which consists of task SL and task CR, is integrated with the block header design such that task receivers are limited to search and accept tasks of the corresponding SLs and CRs. Furthermore, both of sensitive task decomposition and task receivers partition are developed against collusion attack.

2)

To improve system performance, we introduce new block generation rules that allow the block to be generated without waiting for a fixed period of time such that transaction processing is greatly improved.

3)

To achieve energy efficiency in MDC route design, a deep reinforcement learning (DRL) method, the improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG), is developed under the constrain that the SL of each MDC should not be inferior to that of the task meanwhile the task completion condition of each MDC should be higher than the CR of the task. Compared with traditional DRL methods, i.e., deep -network (DQN) and DDPG, IDDPG enables deep exploration, enhanced stability, and convergence acceleration such that the highly energy-efficient MDC route is discovered.

4)

The simulation results indicate that a) BSDA can effectively resist the impact of collusion attack; b) BSDA obtains a high throughput and a low transaction latency under various scenarios; and c) the aggregation ratio of BSDA is higher than contemporary data aggregation strategies with the requirement of a lower energy cost.