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Due to the sharing and open-access characteristics of the wireless medium, wireless sensor networks (WSNs) can be easily attacked by jammers. To mitigate the effects of a jamming attack, one reliable solution is to locate and remove the jammer from the deployed area within the WSN. To realize the jammer’s localization in the WSN, many range-free methods have been proposed. However, most of these methods are sensitive to the distribution of nodes and the parameters of the jammer. For this reason, a jammer location-aware method based on Fibonacci branch search (FBS) is proposed in this article. First, the interference region is estimated by using the interference region mapping service of sensors in wireless sensor networks. Then, the search point is selected in the jamming area and the fitness function is designed according to the average distance from the search point to the boundary sensor. According to the basic branch structure and interactive search rules, the global optimal solution is obtained in the jamming area. Finally, the position of the search point with the best fitness value is used as the estimation of the jammer position. Compared with the existing typical range-free methods, rich simulation experiments demonstrate that the FBS algorithm is superior in the location-aware method for jammers with a higher precision and a lower sensitivity to the distribution of nodes and the parameters of the jammer, respectively.

With the widespread application of Internet of things (IoT), the interference problem becomes more and more serious, which results in not only the poor network performance but also the increased energy consumption of IoT nodes. Therefore, in this paper, we investigate the problem of how to locate the interference sources and infer their communication relationships in the cognitive radio IoT (CR-IoT) deployment scenario. First, we utilize MDS-MAP(P) algorithm with dynamic power control to realize cooperative self-localization of the CR-IoT nodes, which is more energy-efficient than all the IoT nodes equipped with global positioning system (GPS) receivers. Then, we propose a non-cooperative localization method to determine the inference sources with the angle of arrivals (AoAs) measured by the CR-IoT nodes. Finally, the communication relationship between interference sources can be inferred based on the association rule of signals. The network simulation results validate that the proposed methods can locate the interference sources accurately with low energy consumption and correctly infer their communication relationships, which is helpful for the interrupted CR-IoT nodes to take a specific opportunistic transmission policy to reduce their energy consumption.

MIFARE CLASSIC CARD (hereinafter referred to as Mifare Classic) produced by NXP is the most common contactless smartcard on the market. Mifare Classics uses NXP's licensed patent stream cipher algorithm, CRYPTO-1, whose internal structure and algorithm parameters are commercial secrets and unknown to the outside world. At present, there is much research on the security of Mifare Classics. We analyze the security of the CRYPTO-1 algorithm in the Mifare Classics from the point of generating function of the linear feedback shift register (Linear feedback shift register, hereinafter referred to as LFSR) of the card and propose an optimization method for the vulnerability of generating function to improve the security of the Mifare Classics. Furthermore, the optimization method is generally applicable to the encryption algorithm based on the LFSR generating of pseudo-random sequences.

With the development of China high speed railways, many people can enjoy their journey with much less time and more comfort, convenience, safety and punctuality. In order to take full advantage of high-speed railway, we need to understand the structure of the high-speed railway network and assess the centrality of high-speed railway stations. In this paper, we collect the high speed railway schedules and analyze the topology of China high speed railway network. The research based on complex network theory shows that China high-speed railway network is a scale-free and small-world network. The conclusion of the research can be used in transportation task assignment and the high-speed railway construction planning in future.

In this paper, we propose a secret sharing scheme based on Hadamard matrix and prove its security properties. This secret sharing scheme enjoys both the lower computation complexity and the lower space complexity. This secret sharing scheme can be widely used in many light-weight computation and storage devices, such as smart phones, sensors in Internet of things.