With the increasing demands for large-scale computing in Internet of Things (IoT) network, fog computing emerges as a potential solution. However, the time and energy costs are the bottlenecks for developing fog computing. In this paper, we investigate the green fog computing by maximizing the network utility function considering energy efficiency with the constraints of power and interference. The proposed problem is a large-scale mixed integer nonlinear programming. To deal with such kind of problems, we design an algorithm framework to solve the problem in a distributed and parallel manner. The outer loop of the problem is based on the Benders decomposition to divide the integer variables and continuous variables into the master problems and subproblems, respectively. In the subproblem, we use the Dinkelbach algorithm to transform the fractional programming into an equivalent solvable form. In the inner loop, the large-scale problem with only continuous variables is handled by the alternating direction method of multipliers (ADMM) algorithm. For the master problem, we propose a centralized branch-and-bound algorithm to deal with the complexity. We also discuss the properties and performances of our algorithm. Finally, the simulation results indicate that our proposed algorithm is energy-efficient and time-saving.

As an emerging technique, unmanned aerial vehicle (UAV) aided multi-access edge computing (MEC) network has been improving the performance of the communication network. The novel architecture is beneficial for coverage, flexibility, and reliability. However, reducing network latency is a critical issue. In this paper, we design a UAV-aided network with a millimeter wave (mmWave) backhaul to achieve the multi-access edge computing. The routing problem is formulated and solved first to obtain the optimal routes through the ad hoc link for all users. Then, we formulate the joint trajectory design and resource allocation problem, which is a mixed-integer nonconvex programming, to minimize the network latency. Furthermore, we design a novel iterative algorithm framework to handle this challenging problem. In the outer loop, the proposed problem is separated into the primal problems and master problems by adopting generalized benders decomposition (GBD). In the inner loop, we design the algorithm to solve the continuous nonconvex primal problem by combining the alternating direction method of multipliers (ADMM), Dinkelbach algorithm, and successive convex approximation (SCA) algorithm. The simulation results demonstrate that our proposed algorithm framework is effective and feasible.

This paper presents a quad-polarization reconfigurable multi-faceted conformal array of wide coverage range and high gain that is suitable for unmanned aerial vehicle (UAV) communication applications. The design features a sparse array layout comprised of three conformal subarrays, where each subarray includes nine quad-polarization reconfigurable radiating elements. The radiating element is a single-port excited patch radiator loaded by edge slots for size miniaturization and central shorting pin for biasing simplification, which is integrated with a low-complicity reconfigurable feeding network consisting of relatively few PIN-diodes and biasing lines. Using such miniaturized and high-efficiency element, a polarization reconfigurable conformal array protype is designed, fabricated and experimentally characterized. Simulation and measurement results have shown that, the presented conformal array exhibits a 3-dB axial-ratio bandwidth of up to 1.3% at around 1.5 GHz, and -10-dB impedance bandwidth of 7.7% for two circular-polarization modes and 7.3% for two linear-polarization modes, respectively. Besides, the realized gain is over 14 dBi for all reconfigurable polarization modes over a relatively wide beam coverage range of 50° × 180°.

Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably.

Military robot swarm becomes more and more important for contemporary military application. In order to satisfy sophisticated application scenarios, novel communication architecture for military robot swarm has to be conceived. In this paper, we propose a multi-carrier intelligent covert satellite communication (MCICSC) architecture based on electromagnetic environment sensing technology, in where the ability of dynamically adapting the system configuration to the holistic requirements of covertness, performance, and complexity can be considerably improved. Additionally, in order to solve the carrier synchronization problem in very low signal noise ratio (SNR) for each data stream of MCICSC, polar code (PC) is incorporated into the proposed MCICSC system. With the aid of the proposed polar code-aided multi-stream parallel feedback (PAMSPF) carrier synchronization scheme, the MCICSC system is capable of realizing high-precise carrier synchronization even in very low SNR region. Moreover, in order to further decrease the complexity of our MCICSC system, we also propose a polar code-based diversity combining (PBDC) scheme, which is capable of efficiently combing all the data streams without the aid of channel state information (CSI) estimation. Numerical simulations demonstrate that for MCICSC system the performance of the PAMSPF carrier synchronization and PBDC combining schemes can get extremely close to that of an ideally synchronized system. Complexity analysis of the proposed algorithms is given as well.