With the widespread application of real-time embedded systems (ESs), the contradiction between the energy consumption requirements of modern processors and the limited battery capacity becomes more obvious. Dynamic voltage scaling (DVS) has been proven to be one of the most effective technologies for energy management. However, recent studies have shown that the use of DVS leads to a significant increase in the transient fault rate of processors as the characteristic size of logic gates (or transistors) gets smaller and smaller. In this article, we consider the problem of assigning processing frequencies to a group of periodic real-time tasks so as to minimize the overall energy consumption under the constraints of time and reliability. First, under the DVS, we take the reliability of the ESs into consideration through the regularization terms and present the energy consumption optimization model based on the metaheuristic algorithms. Second, a novel algorithm for adaptive differential whale swarm optimization (ADWOA) is proposed according to the optimization requirements. Finally, the optimized data are saved on the chain through the storable feature of the blockchain for the necessary queries. It is worth noting that the on-chain data contains the intrinsic characteristics of the ES, which may give rise to the disclosure of processor privacy. Therefore, we come up with the differential privacy on-chain creating algorithm (DPCA) to protect the privacy of data on the chain. Experimental results show that ADWOA can minimize the energy consumption in real-time ES on the premise of ensuring system reliability and privacy.

Currently, the conventional mode of power data transaction is mediated by Web pages. Nevertheless, there are challenging issues such as privacy protection, transaction security and data reliability in power data trading. In this paper, we present a novel secure power data trading scheme (SPDTS). Firstly, the zero-knowledge proof is employed to achieve data availability and consistency without revealing the data. Then, SPDTS takes full advantage of the dispersibility and immutability of blockchain to ensure the reliability of data transactions. To keep the transaction process efficient, the processing tasks for power data are performed under smart contract. Meanwhile, a trusted execution environment (TEE) is adopted to guarantee the security of power data. Finally, we present a differential privacy scheme to safeguard the privacy information in the power data. Our study indicates that the proposed scheme can achieve privacy protection, transaction security and data reliability. Also, we conduct security analysis and verify the privacy protection property of the scheme in real cases.