Wireless rechargeable sensor networks have become a hot research issue as it can overcome the limited energy bottleneck of wireless sensor networks owing to the recent breakthrough of wireless power transfer technology. Though network lifetime is prolonged and sensor nodes can sustain immortally, the issue of network robustness is overlooked, yielding most theoretical work unsuitable for practical applications when confronting with unpredictable packet loss. In this paper, we address the network robustness issue by maximizing the charging utility in a risk-averse view. First, we build a risk-averse model based on the concept of CVaR (Conditional Value at Risk), which trades-off charging utility and risk aversion for quantifying robustness. Then, we propose a spatial discretization scheme to construct a charging route for mobile charger, which can reduce computational overhead. Afterwards, a path optimization scheme is designed to further improve the charging utility. We convert the original problem into the submodular function maximization problem and propose a method with a performance guarantee while maximizing the system robustness. Finally, testbed experiments and simulations are conducted, and the results demonstrate that our schemes outperform comparison algorithms by at least 22.4% in effective energy in the presence of risks to guarantee system robustness.

The Internet of Things is an emerging technology that integrates the Internet and physical smart objects. This technology currently is used in many areas of human life, including education, agriculture, medicine, military and industrial processes, and trade. Integrating real-world objects with the Internet can pose security threats to many of our day-to-day activities. Intrusion detection systems (IDS) can be used in this technology as one of the security methods. In intrusion detection systems, early and correct detection (with high accuracy) of intrusions is considered very important. In this research, game theory is used to develop the performance of intrusion detection systems. In the proposed method, the attacker infiltration mode and the behavior of the intrusion detection system as a two-player and nonparticipatory dynamic game are completely analyzed and Nash equilibrium solution is used to create specific subgames. During the simulation performed using MATLAB software, various parameters were examined using the definitions of game theory and Nash equilibrium to extract the parameters that had the most accurate detection results. The results obtained from the simulation of the proposed method showed that the use of intrusion detection systems in the Internet of Things based on cloud-fog can be very effective in identifying attacks with the least amount of errors in this network.

Urban tracking plays a vital role for people's urban life in s, e.g., public safety, case investigation, finding missing items, etc. However, the current tracking methods consume a large amount of communication and computing resources since they mainly offload all related sensing data, i.e., videos, generated by widely deployed cameras to the cloud where data are stored, processed, and analyzed. In this paper, we propose a graph optimized data offloading algorithm leveraging a crowd-AI hybrid method to minimize the data offloading cost and ensure the reliable urban tracking result. To be specific, we first formulate a crowd-AI hybrid urban tracking scenario, and prove the proposed data offloading problem in this scenario is NP-hard. Then, we solve it by decomposing the problem into two parts, i.e., trajectory prediction and task allocation. The trajectory prediction algorithm, leveraging the state graph, computes possible tracking areas of the target object, and the task allocation algorithm, using the dependency graph, chooses the optimal set of crowds and cameras to cover the tracking area while minimizing the data offloading cost separately. Finally, the extensive simulations with large real world data set are conducted showing that the proposed algorithm outperforms benchmarks in reducing data offloading cost while ensuring the tracking success rate in s.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

A high degree of energy efficiency and real-time for data transmission is required in cyber-physical systems (CPSs). Data aggregation is an efficient technique to conserve energy by reducing the amount of transmission data. To optimize real-time communication under constraints of power consumption and data aggregation performance of each node in CPS, this paper presents a learning automata (LA)-based degree-bounded bottleneck data aggregation tree (DBBDAT) construction framework to minimize the maximum delay on data aggregation trees with bounded degree, which is an NP-hard problem. We model the network of CPS as a connected weighted and directed graph to form a network of LA. Degree-bounded data aggregation trees are constructed first by the action selection of each automaton. Then, the action vector of each automaton is updated by linear reward-inaction learning algorithm, and at last DBBDAT is constructed based on a threshold. Simulation results show that our approach significantly outperforms integer linear programming (ILP)-based method in terms of time complexity. Compared with ILP-based method, it can obtain an optimal solution or a suboptimal solution with guaranteed approximation ratios, and can control the tradeoff between accuracy and cost by choosing appropriate learning rate and threshold. Its distributed implementation is simple and it can efficiently solve the problem for the sparse graph in practice.

Since the quantity and quality of DNA sequence directly affect the accuracy and efficiency of computation, the design of DNA sequence is essential for DNA computing. In order to improve the efficiency and reliability of DNA computing, there is a rich literature targeting at generating DNA sequences with lower similarity that can hybridize at a lower melting temperature. However, it is not trivial to improve both melting temperature and similarity for the DNA sequence, since DNA sequence design problem under the constraints of Hamming distance, secondary structure and molecular thermodynamic is known to be NP-hard. For the sake of achieving the lower melting temperature and similarity for the generated DNA sequence, we proposed an improved method for the bacterial foraging algorithm based on activity of bacteria (BFA-A). In particular, the effect of bacterial vitality on foraging ability is considered, and a competitive exclusion mechanism is introduced to improve the quality of the generated DNA sequences. In BFA-A, high-quality DNA strands are replicated to avoid the participation of inferior strands in the operation, and the active regulation mechanism and the competitive rejection mechanism are used to improve and accelerate the chemotaxis process. Experiments show that our proposed approach significantly outperforms existing methods in terms of melting temperature and similarity. In addition, the experimental results also show that our method can reduce the number of iterations, and has guiding significance to generate high-quality DNA sequences more efficiency.