The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

In this paper, we consider a single-cell cellular network with a number of cellular users (CUs) and unmanned aerial vehicles (UAVs), in which multiple UAVs upload their collected data to the base station (BS). Two transmission modes are considered to support the multi-UAV communications, i.e., UAV-to-network (U2N) and UAV-to-UAV (U2U) communications. Specifically, the UAV with a high signal-to-noise ratio (SNR) for the U2N link uploads its collected data directly to the BS through U2N communication, while the UAV with a low SNR for the U2N link can transmit data to a nearby UAV through underlaying U2U communication for the sake of quality of service. We first propose a cooperative UAV sense-and-send protocol to enable the UAV-to-X communications, and then formulate the subchannel allocation and UAV speed optimization problem to maximize the uplink sum-rate. To solve this NP-hard problem efficiently, we decouple it into three sub-problems: U2N and cellular user (CU) subchannel allocation, U2U subchannel allocation, and UAV speed optimization. An iterative subchannel allocation and speed optimization algorithm (ISASOA) is proposed to solve these sub-problems jointly. The simulation results show that the proposed ISASOA can upload 10% more data than the greedy algorithm.

Cellular UAV-to-X Communications: Design and Optimization for Multi-UAV Networks

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN) By providing computational and storage resources at the edge, MEC can reduce latency for end users Hence, this article investigates MEC for 5G and IoT comprehensively It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios

Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things

Unmanned aerial vehicles, also known as drones, are expected to play major roles in future smart cities, for example, by delivering goods and merchandise, serving as mobile hotspots for broadband wireless access, and maintaining surveillance and security. The goal of this survey article is to study various potential cyber and physical threats that may arise from the use of UAVs, and subsequently review various ways to detect, track, and interdict malicious drones. In particular, we review techniques that rely on ambient radio frequency signals (emitted from UAVs), radars, acoustic sensors, and computer vision techniques for detection of malicious UAVs. We present some early experimental and simulation results on range estimation of UAVs and receding horizon tracking of UAVs. Finally, we summarize common techniques that are considered for interdiction of UAVs.

Detection, Tracking, and Interdiction for Amateur Drones

Effective emergency and natural disaster management depend on the efficient mission-critical voice and data communication between first responders and victims. Land mobile radio system (LMRS) is a legacy narrowband technology used for critical voice communications with limited use for data applications. Recently, long term evolution (LTE) emerged as a broadband communication technology that has a potential to transform the capabilities of public safety technologies by providing broadband, ubiquitous, and mission-critical voice and data support. For example, in the United States, FirstNet is building a nationwide coast-to-coast public safety network based on LTE broadband technology. This paper presents a comparative survey of legacy and the LTE-based public safety networks, and discusses the LMRS-LTE convergence as well as mission-critical push-to-talk over LTE. A simulation study of LMRS and LTE band class 14 technologies is provided using the NS-3 open source tool. An experimental study of APCO-25 and LTE band class 14 is also conducted using software-defined radio to enhance the understanding of the public safety systems. Finally, emerging technologies that may have strong potential for use in public safety networks are reviewed.

A Survey on Legacy and Emerging Technologies for Public Safety Communications

In this letter, a receding horizon path planning algorithm is introduced for unmanned aerial vehicle swarms to cooperatively localize a moving radio frequency transmitter. In the core of the proposed algorithm is a model to predict the Fisher information matrix. Using this prediction model, we formulate the most favorable course of action to solve the path planning using local optimization, which helps the system as a whole to achieve the goal over a finite receding horizon. The effectiveness of the proposed algorithm is demonstrated by comparing the proposed mechanism with the non-predictive cooperative technique through computer simulation. It is shown that the expected estimation error can be significantly reduced using the proposed method.

Receding Horizon Multi-UAV Cooperative Tracking of Moving RF Source

The localization of a radio-frequency transmitter with intermittent transmissions is considered via a group of unmanned aerial vehicles (UAVs) equipped with omnidirectional received signal strength sensors. This group embarks on an autonomous patrol to localize and track the target with a specified accuracy, as quickly as possible. The challenge can be decomposed into two stages: 1) estimation of the target position given previous measurements (localization) and 2) planning the future trajectory of the tracking UAVs to get lower expected localization error given current estimation (path planning). For each stage, we compare two algorithms in terms of performance and computational load. For the localization stage, we compare a detection-based extended Kalman filter (EKF) and a recursive Bayesian estimator. For the path planning stage, we compare a steepest descent posterior Cramer–Rao lower bound path planning and a bioinspired heuristic path planning. Our results show that the steepest descent path planning outperforms the bioinspired path planning by an order of magnitude, and recursive Bayesian estimator narrowly outperforms detection-based EKF.

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Raveling is one of the critical modes of failure in open graded asphalt mixtures. The Florida Department of Transportation (FDOT) Districts 4 and 6, located in southeastern Florida, have experienced a large amount of premature raveling with Open Graded Friction Course mixes (OGFC) compared to the other districts in Florida. To investigate the reason of raveling occurrence, the research team devised a data collection tool for smartphones and developed software to measure the raveling area and its location and severity in video images using smartphone GPS capability. The data used in this study were extracted from different FDOT database systems, such as the Laboratory Information Management System (LIMS) and the Electronic Data Management System (EDMS), as well as from the National Oceanic and Atmospheric Administration (NOAA). Ten projects were surveyed by the research team, and numerical raveling statistics were developed and compared with qualitative raveling ratings provided by FDOT. Good correlation was found between the two methods. The comparison was used to establish thresholds of good performance. Such thresholds can be used to develop performance specifications or warranty benchmarks. The relative effects of mix design, construction, and environmental factors were also studied. A project level of analysis was conducted. Several hypotheses were evaluated to determine the cause of premature raveling. Data analysis results indicate significant correlations between raveling and ambient temperature, mix temperature, mix spread rate, and gradation. In light of this study, recommendations were made to enhance the longevity of the OGFC mixtures.

Farshad Koohifar

Papers

Detection, Tracking, and Interdiction for Amateur Drones

A Survey on Legacy and Emerging Technologies for Public Safety Communications

Receding Horizon Multi-UAV Cooperative Tracking of Moving RF Source

Autonomous Tracking of Intermittent RF Source Using a UAV Swarm

Investigation of pavement raveling performance using smartphone