Improving PHY-Security of UAV-Enabled Transmission with Wireless Energy Harvesting: Robust Trajectory Design and Communications Resource Allocation

In this paper, we consider an unmanned aerial vehicle (UAV) assisted communications system, including two cooperative UAVs, a wireless-powered ground destination node leveraging simultaneous wireless information and power transfer (SWIPT) technique, and a terrestrial passive eavesdropper. One UAV delivers confidential information to destination and the other sends jamming signals to against eavesdropping and assist destination with energy harvesting. Assuming UAVs have partial information about eavesdropper's location, we propose two transmission schemes: friendly UAV jamming (FUJ) and Gaussian jamming transmission (GJT) for the cases when jamming signals are known and unknown a priori at destination, respectively. Then, we formulate an average secrecy rate maximization problem to jointly optimize the transmission power and trajectory of UAVs, and the power splitting ratio of destination. Being non-convex and hence difficult to solve the formulated problem, we propose a computationally efficient iterative algorithm based on block coordinate descent and successive convex approximation to obtain a suboptimal solution. Finally, numerical results are provided to substantiate the effectiveness of our proposed multiple-UAV schemes, compared to other existing benchmarks. Specifically, we find that the FUJ demonstrates significant secrecy performance improvement in terms of the optimal instantaneous and average secrecy rate compared to the GJT and the conventional single-UAV counterpart.Comment: This paper has been accepted by IEEE Transactions on Vehicular Technolog

Optimal Secure Multi-Layer IoT Network Design

With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two-layer with the cloud and cellular networks coexisting with the underlaid device-to-device (D2D) communications. The connectivity of IoTs plays an important role in information dissemination for mission-critical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denial-of-service (DoS) and jamming attacks, resulting in link removals in IoT network. In this work, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things (IoBT) to corroborate and illustrate our obtained results.Comment: 12 pages, to appear in IEEE Transactions on Control of Network System

Secure UAV Communication with Cooperative Jamming and Trajectory Control

This paper presents a new cooperative jamming approach to secure the unmanned aerial vehicle (UAV) communication by leveraging jamming from other nearby UAVs to defend against the eavesdropping. In particular, we consider a two-UAV scenario when one UAV transmitter delivers the confidential information to a ground node (GN), and the other UAV jammer cooperatively sends artificial noise (AN) to confuse the ground eavesdropper for protecting the confidentiality of the data transmission. By exploiting the fully-controllable mobility, the two UAVs can adaptively adjust their locations over time (a.k.a. trajectories) to facilitate the secure communication and cooperative jamming. We assume that the two UAVs perfectly know the GN's location and partially know the eavesdropper's location {\emph{a-priori}}. Under this setup, we maximize the average secrecy rate from the UAV transmitter to the GN over one particular time period, by optimizing the UAVs' trajectories, jointly with their communicating/jamming power allocations. Although the formulated problem is non-convex, we propose an efficient solution by applying the techniques of alternating optimization and successive convex approximation (SCA).Comment: 5 pages, 2 figures, accepted for publication at the IEEE Communications Lette

UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming

Cellular systems are vulnerable to jamming attacks, especially smart jammers that choose their jamming policies such as the jamming channel frequencies and power based on the ongoing communication policies and network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular communication framework against jamming. In this scheme, UAVs use reinforcement learning methods to choose the relay policy for mobile users in cellular systems, if the serving base station is heavily jammed. More specifically, we propose a deep reinforcement learning based UAV relay scheme to help cellular systems resist smart jamming without being aware of the jamming model and the network model in the dynamic game based on the previous anti-jamming relay experiences and the observed current network status. This scheme can achieve the optimal performance after enough interactions with the jammer. Simulation results show that this scheme can reduce the bit error rate of the messages and save energy for the cellular system compared with the existing scheme

Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions

The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are becoming increasingly popular for various commercial, industrial, and public-safety applications. However, drones with uncontrolled deployment poses challenges for highly security-sensitive areas such as President house, nuclear plants, and commercial areas because they can be used unlawfully. In this article, to cope with security-sensitive challenges, we propose point-to-point and flying ad-hoc network (FANET) architectures to assist the efficient deployment of monitoring drones (MDr). To capture amateur drone (ADr), MDr must have the capability to efficiently and timely detect, track, jam, and hunt the ADr. We discuss the capabilities of the existing detection, tracking, localization, and routing schemes and also present the limitations in these schemes as further research challenges. Moreover, the future challenges related to co-channel interference, channel model design, and cooperative schemes are discussed. Our findings indicate that MDr deployment is necessary for caring of ADr, and intensive research and development is required to fill the gaps in the existing technologies.Comment: arXiv admin note: text overlap with arXiv:1510.07390 by other author

Optimal Power Allocation for Secure Directional Modulation Networks with a Full-duplex UAV User

This paper make an investigation of a secure unmanned aerial vehicle (UAV)-aided communication network based on directional modulation(DM), in which one ground base station (Alice), one legitimate full-duplex (FD) user (Bob) and one illegal receiver (Eve) are involved. In this network, Alice acts as a control center to transmit confidential message and artificial noise (AN). The UAV user, moving along a linear flight trajectory, is intended to receive the useful information from Alice. At the same time, it also sends AN signals to further interference Eve's channel. Aiming at maximizing secrecy rate during the UAV flight process, a joint optimization problem is formulated corresponding to power allocation (PA) factors, beamforming vector, AN projection matrices. For simplicity, maximum ratio transmission, null-space projection and the leakage-based method are applied to form the transmit beamforming vector, AN projection matrix at Alice, and AN projection vector at Bob, respectively. Following this, the optimization problem reduces into a bivariate optimization programme with two PA factors. We put forward an alternating iterative algorithm to optimize the two PA factors. Simulation results demonstrate that the proposed strategy for FD mode achieves a higher SR than the half-duplex (HD) mode, and outperforms the FD mode with fixed PA strategy

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.Comment: 37 pages, 13 figures, 6 tables, 174 reference paper

Intelligent Physical Layer Security Approach for V2X Communication

Intelligent transportation systems (ITS) with advanced sensing and computing technologies are expected to support a whole new set of services including pedestrian and vehicular safety, internet access for vehicles, and eventually, driverless cars. Wireless communication is a major driving factor behind ITS, enabling reliable communication between vehicles, infrastructure, pedestrians and network, generally referred to as vehicle to everything (V2X) communication. However, the broadcast nature of wireless communication renders it prone to jamming, eavesdropping and spoofing attacks which can adversely affect ITS. Keeping in view this issue, we suggest the use of an intelligent security framework for V2X communication security, referred to as intelligent V2X security (IV2XS), to provide a reliable and robust solution capable of adapting to different conditions, scenarios and user requirements. We also identify the conditions that impact the security and describe the open challenges in achieving a realistic IV2XS system

3D Trajectory Optimization for Secure UAV Communication with CoMP Reception

This paper studies a secrecy unmanned aerial vehicle (UAV) communication system with coordinated multi-point (CoMP) reception, in which one UAV sends confidential messages to a set of distributed ground nodes (GNs) that can cooperate in signal detection, in the presence of several colluding suspicious eavesdroppers. Different from prior works considering the two-dimensional (2D) horizontal trajectory design in the non-CoMP scenario, this paper additionally exploits the UAV's vertical trajectory (or altitude) control for further improving the secrecy communication performance with CoMP. In particular, we jointly optimize the three dimensional (3D) trajectory and transmit power allocation of the UAV to maximize the average secrecy rate at GNs over a particular flight period, subject to the UAV's maximum flight speed and maximum transmit power constraints. To solve the non-convex optimization problem, we propose an alternating-optimization-based approach, which optimizes the transmit power allocation and trajectory design in an alternating manner, by convex optimization and successive convex approximation (SCA), respectively. Numerical results show that in the scenario with CoMP reception, our proposed 3D trajectory optimization significantly outperforms the conventional 2D horizontal trajectory design, by exploiting the additional degree of freedom in vertical trajectory.Comment: 6 pages, 5 figures, submitted to IEEE Conference for possible publicatio

Robust Trajectory and Resource Allocation Design for Secure UAV-aided Communications

This paper aims to enhance the physical layer security against potential internal eavesdroppings by exploiting the maneuverability of an unmanned aerial vehicle (UAV). We consider a scenario where two receivers with different security clearance levels require to be served by a legitimate transmitter with the aid of the UAV. We jointly design the trajectory and resource allocation to maximize the accumulated system confidential data rate. The design is formulated as a mixed-integer non-convex optimization problem which takes into account the partial position information of a potential eavesdropper. To circumvent the problem non-convexity, a series of transformations and approximations are proposed which facilitates the design of a computationally efficient suboptimal solution. Simulation results are presented to provide important system design insights and demonstrate the advantages brought by the robust joint design for enhancing the physical layer security.Comment: 6 pages, 3 figures. This work has been accepted by IEEE ICC 201