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The widely deployed WiFi APs and ZigBee devices make the coexistence for these two networks more pervasive than ever. Previous solutions focus on the contention resolution in time domain, but would lead to the underutilized frequency resources. However, for frequency domain schemes, to enable efficient coexistence for WiFi and ZigBee networks, the WiFi nodes or ZigBee nodes have to select the appropriate channel for transmission. Such scheme will need strict cooperations among these two independent systems, and could not work with the conventional legacy systems seamlessly. In this paper, we propose a frequency overlay approach named COFFEE (COexist wiFi For zigbEE networks), where subcarriers interfering with ZigBee transmissions are nullified by WiFi. According to our basic evaluations, due to the relatively robust feature in WiFi/OFDM design, nullifying small portion of subcarriers would affect the WiFi performance slightly, but could improve the capability for coexistence significantly. Thus by using COFFEE, WiFi and ZigBee nodes can transmit their packets concurrently without any coordination. Also, COFFEE and conventional legacy WiFi systems could work together seamlessly. Furthermore, we implement COFFEE with USRP software radio platform and evaluate the performance under real wireless network scenarios. The extensive evaluations show that compared to the coexistence schemes in time domain, COFFEE is able to increase the network throughput by more than 300%. Even when the ZigBee node never knows the transmission time of WiFi packet, it still can provide concurrent lossless transmission by using COFFEE with only 10% to 15% WiFi throughput reduction.

In this paper, we develop a wireless data gathering model for a multi-drone system in traditional wireless sensor networks (WSNs), where each drone serves as a data collector in the extremely large and densely deployed area. Previous solutions usually consider how to schedule the traveling route among nodes, but fail to optimize the data transmission time. We focus on extending the data collection issue over Shannon model which involves the factors such as the transmission bandwidth and the SNR between a drone and a sensor node. A novel and typical system model is formulated and we investigate the corresponding Route Selection and Communication Association (RSCA) problem, that is, given a set of candidate flight routes and a fixed number of deployed nodes, we determine which routes should be selected for traveling and which nodes should be associated such that the overall energy consumption for data gathering could be minimized. We prove the RSCA problem is NP-hard by reduction from the Vertex Cover problem and then devise an efficient and accessible O(log log n) approximation algorithm within the time complexity bound by O(p2 log n log log n), where n is the number of sensor nodes and p is the number of routes in WSNs. Extensive simulations are carried out to investigate the performance of our designed algorithm by comparing with the brute-force and random methods. The proposed algorithm achieves 54% more energy consumption at most and 45% more on average comparing with the optimal solution. Furthermore, real-world trace-driven evaluations have been conducted to show that our obtained solution would hold 34% more at most and 25% more on average comparing with the optimal energy consumption and eventually validate our algorithm.

Wireless energy transfer technologies have played an important role in the development of Internet of Things. Most of the previous studies focus on scheduling mobile chargers efficiently for rechargeable sensor nodes. In this paper, we investigate the deployment problem for wireless charging stations (WCSs) in urban areas with respect to the users detouring cost when they move to the candidate WCSs. With pre-known user's trajectories and given number of WCSs, we deploy the WCSs to maximize the number of recharged users with guaranteed probability. We convert our deployment problem into an NP-hard weighted maximum coverage problem, and prove the objective function is a maximum submodular set function. To this end, a simple but efficient greedy algorithm with approximation factor of (1 - 1/ε) is proposed for the threshold detouring mode. In addition, an improved algorithm with an approximation factor of (1 - 1/√e) is presented for the linear/nonlinear detouring mode. Finally, we evaluate the performance of our algorithms by comparing them with two typical heuristic algorithms (flow-centric and random-based), and the impacts of different detouring thresholds on our algorithms by synthetic traces. Moreover, real trace-driven evaluations validate that our algorithm improves the coverage quality by 75% when compared to the two aforementioned algorithms.

Wireless energy transfer technologies have attracted increasing attention on empowering the wireless sensor nodes in recent years. In this paper, we consider a typical wireless rechargeable network, where a mobile charging vehicle is scheduled to charge a wireless sensor network with practical nodes' deployment restrictions that may result in low charging efficiency for sensor nodes by charging vehicle. In our model, we take the effects of charging distance and angle on charging efficiency into consideration. Intuitively, there is an inevitable tradeoff between the charging distance and the angle. First of all, the scheduling traveling path of charging vehicle in previous studies has been proved to be NP-hard. Even worse, the nonlinear property between the charging distance and angle makes the problem even harder. For these concerns, we aim at minimizing the recharging cycle time, which contains the traveling time and recharging time. To this end, we prove that the charging vehicle would travel along the shortest Hamiltonian cycle. And we show the optimal charging location for each wireless charging incident. Experimental results demonstrate that our proposed solution could improve the charging efficiency around two times compared with the baseline scheme without optimization for angle and distance.

Unfortunately, we had mixed up the institution and address of author “Qiongzheng Lin” with others. The correct institution and address of author “Qiongzheng Lin” are given here.

In this paper, we propose a verifiable quantum cloud computation scheme based on blind quantum computation which effectively solves the privacy and verifiability problem for clients when interfacing with quantum computer in cloud computation. Different from most of present works (e.g. using brickwork state as resource state), we use cluster state with grid distributed structure to realize the universal and cryptographic calculation which is more suitable for the distributed architecture of cloud computation. Throughout the process, the random key is always held by the client that effectively promote the confidentiality. Moreover, we propose a new method to make the client Alice verifies the correctness of the results through only classical calculation. Security analysis shows that cloud server Bob can only obtain the dimension of input state and cluster state from the calculation, but cannot obtain any information of the input, the output and the algorithm. Dishonest Bob will be detected during the verification.

According to the perishable characteristics of refrigerated food and the objective of minimizing the total cost, the mathematical optimization model of cold chain logistics distribution center location problem is established by introducing such constraints as the freshness and time window. In order to solve the problems of slow convergence and easy to fall into local optimal solution in the process of the traditional wolf colony optimization, an immune wolf colony hybrid algorithm is proposed to solve the location problem of distribution center. In this hybrid algorithm, the idea of vaccination of immune algorithm is introduced into the wolf colony algorithm. By adjusting the antibody concentration and selecting immune operator, the diversity of the wolf colony algorithm is improved, and then the search space of the solution is expanded; the convergence speed and solution accuracy of the wolf colony algorithm are improved by using immune memory cells and immune vaccine. The simulation results show that the immune wolf colony algorithm can quickly converge to the global optimal solution and optimize the location model of logistics distribution center. The algorithm has good feasibility and robustness.