The prevalence of Internet of Things (IoTs) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird or Smart Cities, IoTs also incur more concerns on privacy and security. Among the top security challenges that IoTs face, access authorization is critical in resource sharing and information protection. One of the weaknesses in today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain-enabled decentralized capability-based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identitybased capability token management strategy is proposed, which takes advantage of smart contract for registering, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, our experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine-grained AC solution to IoT systems.

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Existing pursuer-evader (PE) game algorithms do not provide good real-time solutions for situations with the following complexities: (1) multi-pursuer multi-evader, (2) multiple evaders with superior control resources such as higher speeds, and (3) jamming confrontation between pursuers and evaders. This paper introduces a real-time decentralized approach, in which decentralization strategy reduces computational complexity in multi-pursuer multievader situations, cooperative chasing strategy guarantees capture of some superior evaders, and min-max double-sided jamming confrontation provides optimal jamming-estimation strategies under adversarial noisy environments. Extensive simulations confirm the efficiency of this approach.