This paper presents an efficient and secure chaotic S-Box based image encryption algorithm. Firstly, by cryptanalyzing a multiple chaotic S-Boxes based image encryption algorithm, we successfully cracked the cryptosystem by using chosen-plaintext attack (CPA). Secondly, we put forward a new image encryption scheme based on a novel compound chaotic map and single S-Box. In the new scheme, a novel discrete compound chaotic system, Logistic-Sine system (LSS), is proposed, which has wider chaotic range and better chaotic properties. And a new S-Box is constructed by using LSS, which has satisfactory cryptographic performance. Based on the new S-Box and the chaotic key stream, the new image encryption algorithm is designed, which consist of a round of permutation and two rounds of substitution process. The permutation and substitution key sequences are related to the plaintext image content, this strategy enables the cryptosystem to resist CPA. The simulation results and security analysis verified the effectiveness of the proposed image encryption scheme. Especially, the new scheme has obvious efficiency advantages, showing that it has better application potential in real-time image encryption.

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Remote clouds are gradually unable to achieve ultra-low latency to meet the requirements of mobile users because of the intolerable long distance between remote clouds and mobile users and the network congestion caused by the tremendous number of users. Mobile edge computing, a new paradigm, has been proposed to mitigate aforementioned effects. Existing studies mostly assume the edge servers have been deployed properly and they just pay attention to how to minimize the delay between edge servers and mobile users. In this paper, considering the practical environment, we investigate how to deploy edge servers effectively and economically in wireless metropolitan area networks. Thus, we address the problem of minimizing the number of edge servers while ensuring some QoS requirements. Aiming at more consistence with a generalized condition, we extend the definition of the dominating set, and transform the addressed problem into the minimum dominating set problem in graph theory. In addition, two conditions are considered for the capacities of edge servers: one is that the capacities of edge servers can be configured on demand, and the other is that all the edge servers have the same capacities. For the on-demand condition, a greedy based algorithm is proposed to find the solution, and the key idea is to iteratively choose nodes that can connect as many other nodes as possible under the delay, degree and cluster size constraints. Furthermore, a simulated annealing based approach is given for global optimization. For the second condition, a greedy based algorithm is also proposed to satisfy the capacity constraint of edge servers and minimize the number of edge servers simultaneously. The simulation results show that the proposed algorithms are feasible.

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Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context-awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

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A reliable bi-directional communication network is one of the key factors in smart grid (SG) to meet application requirements and improve energy efficiency. As a promising communication infrastructure, wireless mesh network (WMN) can provide high speed and cost-effect communication for SG. However, challenges remain to maintain high reliability and quality of service (QoS) when applying WMNs to SG. In this paper, we first propose a hybrid wireless mesh protocol (HWMP) based neighbor area network (NAN) QoSaware routing scheme, named HWMP-NQ, to meet the QoS requirements by applying an integrated routing metric to route decision with effective link condition probing and queue optimization. To further improve the reliability of the proposed HWMP-NQ, we present a multi-gateway backup routing scheme along with a routing reliability correction factor to mitigate the impact of routing oscillations. Finally, we evaluate the performances of the proposed schemes on NS3 simulator. Extensive simulations demonstrate that HWMP-NQ can distinguish different applications and satisfy the QoS requirements respectively, and also improve the average packet delivery ratio and throughput with a reduced routing overhead, even with a high failure rate of mesh nodes.