Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS’s passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Multi-UAV-Enabled Load-Balance Mobile-Edge Computing for IoT Networks

The fifth generation (5G) wireless technologies serve as a key propellent to meet the increasing demands of the future Internet of Things (IoT) networks. For wireless communication security in 5G IoT networks, physical-layer security (PLS) has recently received growing interest. This paper aims to provide a comprehensive survey of the PLS techniques in 5G IoT communication systems. The investigation consists of four hierarchical parts. In the first part, we review the characteristics of 5G IoT under typical application scenarios. We then introduce the security threats from the 5G IoT physical-layer and categorize them according to the different purposes of the attacker. In the third part, we examine the 5G communication technologies in 5G IoT systems and discuss their challenges and opportunities when coping with physical-layer threats, including massive multiple-input-multiple-output (MIMO), millimeter wave (mmWave) communications, nonorthogonal multiple access (NOMA), full-duplex technology, energy harvesting (EH), visible light communication (VLC), and unmanned aerial vehicle (UAV) communications. Finally, we discuss open research problems and future works about PLS in the IoT system with technologies of 5G and beyond.

Physical-Layer Security of 5G Wireless Networks for IoT: Challenges and Opportunities

A survey on the 5G network and its impact on agriculture: Challenges and opportunities

Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve the propagation environment and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To tackle the formulated non-convex problem, we divide it into two subproblems, namely, passive beamforming and trajectory optimization. We first derive a closed-form phase-shift solution for any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the optimal phase-shift solution, we obtain a suboptimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results demonstrate that the proposed algorithm can considerably improve the average achievable rate of the system.

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

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UAV-Aided MIMO Communications for 5G Internet of Things

Unmanned aerial vehicle (UAV)-enabled backscatter communications (BackComm) is deemed to be a vital technique enabling the data transmission over massive battery-less devices for Internet of Things (IoT). However, the UAV-enabled Backcomm suffers from information leakage due to the broadcasting nature of wireless channels. To cope with the security issue, in this paper, a UAV-enabled multi-user secure BackComm system is developed using analog beamforming (ABF) and randomized continuous wave (RCW) techniques, where the multiple users are supported by the multi-carrier RCW over a single low-complexity radio frequency (RF) chain. By exploiting the RCW transmitted towards backscatter, the eavesdropping link can be eroded without any specific jamming signals. The closed-form of the secrecy rate is studied with the approximations, which is then maximized by jointly optimizing the beamforming together with the UAV’s location and the RCW settings. Simulation results are carried out to confirm the accuracy of the proposed approximation, while the convergence behavior of the optimization algorithm is analyzed. As a result, it can be shown that the secrecy rate can be significantly improved compared with the benchmark schemes.

UAV-Enabled Secure Multiuser Backscatter Communications With Planar Array

Recently, Siamese trackers have received more attention in visual tracking due to their satisfactory balance between performance and efficiency. However, most Siamese trackers neglect the misalignment between classification confidence and regression accuracy, which limits their best performance. In this paper, we proposed a novel reciprocal localization-aware classification and residual regression architecture with Siamese network named SiamRCRR. The proposed SiamRCRR consists of three subnetworks: a residual regression subnetwork to fully exploit the accuracy of object localization through the refinement of the bounding box, a localization-aware classification subnetwork to predict comprehensive scores of classification confidence and regression accuracy, and a Siamese subnetwork to extract features for classification and regression. The interaction of the residual regression subnetwork and localization-aware classification subnetwork forms a closed-loop structure to take advantage of the mutual benefit between classification and regression tasks. Therefore, the consistency between classification confidence and regression accuracy is guaranteed during tracking. Experiments on five challenging benchmarks including GOT-10k, OTB-100, LaSOT, VOT2019, and NFS show that SiamRCRR significantly outperforms its well-behaved counterparts in tracking accuracy and execution effectiveness. 

Siamese reciprocal classification and residual regression for robust object tracking

The traditional military communication network is based on IP architecture, which has the problems of rigid architecture and challenging quality of service guarantee. The rapid development of various new applications has put differentiated demands on the whole network's service quality. In recent years, software-defined network technology has been developing, which has the characteristics of decoupling the control and data planes. Its control plane has excellent global control capability and network equipment information collection capability, which has natural advantages for optimizing the quality of service. Firstly, the SDN network architecture, quality of service performance parameters, and service model are introduced; secondly, the different requirements of quality of service for standard and typical service application scenarios are analyzed, and the current research status of quality of service enhancement through routing optimization is described; finally, the outlook is summarized, and two new ideas for quality of service enhancement in SDN networks and the development trend of quality of service research in military communication networks are proposed.

A Review of Routing Optimization Techniques for Quality of Service Assurance in Software-Defined Networks

We study the whole process of using Low Earth Orbit (LEO) satellite constellation to localize Automatic Identification System (AIS) emitter, especially the methodologies of intelligently improving and modifying LEO satellite constellation positioning algorithm based on AIS data. In this vein, we design a simulation system for cooperative localization of vessels based on the AIS signals they emit, with a LEO satellite constellation. We implement key modules such as vessel motion simulation, AIS signal simulation, satellite-to-ground link simulation, localization algorithm module and system visualization. The simulation results of single satellite and multi-satellites localization systems verify the effectiveness of the proposed system.

Jingchao Wang

Papers

UAV-Aided MIMO Communications for 5G Internet of Things

UAV-Enabled Secure Multiuser Backscatter Communications With Planar Array

Siamese reciprocal classification and residual regression for robust object tracking

A Review of Routing Optimization Techniques for Quality of Service Assurance in Software-Defined Networks

An AIS-Based Vessel Localization Simulation Platform with Low-Orbital Satellites