With the sharp increase in the number of intelligent devices, the Internet of Things (IoT) has gained more and more attention and rapid development in recent years. It effectively integrates the physical world with the Internet over existing network infrastructure to facilitate sharing data among intelligent devices. However, its complex and large-scale network structure brings new security risks and challenges to IoT systems. To ensure the security of data, traditional access control technologies are not suitable to be directly used for implementing access control in IoT systems because of their complicated access management and the lack of credibility due to centralization. In this paper, we proposed a novel attributebased access control scheme for IoT systems, which simplifies greatly the access management. We use blockchain technology to record the distribution of attributes in order to avoid single point failure and data tampering. The access control process has also been optimized to meet the need for high efficiency and lightweight calculation for IoT devices. The security and performance analysis show that our scheme could effectively resist multiple attacks and be efficiently implemented in IoT systems. INDEX TERMS Access control, attribute-based access control, blockchain, consortium blockchain, Internet of Things.

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Accurate estimation of battery degradation cost is one of the main barriers for battery participating on the energy arbitrage market. This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model. Firstly, the control problem is formulated as a Markov Decision Process (MDP). Then a noisy network based deep reinforcement learning approach is proposed to learn an optimized control policy for storage charging/discharging strategy. To address the uncertainty of electricity price, a hybrid Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) model is adopted to predict the price for the next day. Finally, the proposed approach is tested on the the historical UK wholesale electricity market prices. The results compared with model based Mixed Integer Linear Programming (MILP) have demonstrated the effectiveness and performance of the proposed framework.

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