To use role-based access control (RBAC) in wireless network is difficult than that in wired network. RBAC needs to search relative tables to get the user's permissions. We present an access control judgment algorithm which bases on artificial neural network (ANN). The algorithm reduces the data transmission using bit string to express roles and permissions. The algorithm employs set theory to represent roles and their inheritance hierarchy, as well as conflicted permissions. It uses selected roles as input vectors and the matching permissions which contain no conflict as the output vectors to train the ANN. Then it uses the trained ANN to compute directly users' permissions when the system is under running condition, instead of searching tables. That improves the efficiency of access control. The algorithm is simple and efficient, which makes it easy to be realized in wireless networks.
The unmanned system based on mobile-edge computing (MEC) and blockchain can conveniently share computing resources and realize multi-devices collaboration. However, frequent data communication in the framework of object detection brings a heavy computational burden. In this paper, a novel blockchain-based multi-view unmanned equipment fusion architecture using multiple object tracking (MOT) technique is designed. Inspired by the idea of person re-identification and hash representation for image retrieval, a novel MOT technique using HashNet to extract deep hash appearance of objects is proposed. In addition, based on the blockchain and MEC technology, we make some improvements in feature fusion and tracking interrupt recovery. We combine deep hash appearance features with motion features and design a tracking interruption recovery mechanism to solve the problem of object occlusion. The experiment results on the MOT challenge dataset demonstrate that the proposed algorithm can handle object occlusion problem effectively and successfully reduce the number of identity switches. In real application scenes our algorithm performs particularly well, showing that our algorithm is more practical.
Information literacy assessment is extremely important for the evaluation of the information literacy skills of college students. Intelligent optimization technique is an effective strategy to optimize the weight parameters of the information literacy assessment index system (ILAIS). In this paper, a new version of differential evolution algorithm (DE), named hybrid differential evolution with model-based reinitialization (HDEMR), is proposed to accurately fit the weight parameters of ILAIS. The main contributions of this paper are as follows: firstly, an improved contraction criterion which is based on the population entropy in objective space and the maximum distance in decision space is employed to decide when the local search starts. Secondly, a modified model-based population reinitialization strategy is designed to enhance the global search ability of HDEMR to handle complex problems. Two types of experiments are designed to assess the performance of HDEMR. In the first type of experiments, HDEMR is tested and compared with seven well-known DE variants on CEC2005 and CEC2014 benchmark functions. In the second type of experiments, HDEMR is compared with the well-known and widely used deterministic algorithm DIRECT on GKLS test classes. The experimental results demonstrate the effectiveness of HDEMR for global numerical optimization and show better performance. Furthermore, HDEMR is applied to optimize the weight parameters of ILAIS at China University of Geosciences (CUG), and satisfactory results are obtained.
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