Multiple event localization in a sparse acoustic sensor network using UAVs as data mules

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Unmanned aerial vehicle (UAV) is an aircraft without a human pilot onboard and a sort of unmanned vehicle. UAV Network research activities in the real world can pose several challenges, especially in urban environments or in densely populated environments. This is due to the nature of the UAV which requires a large area of operation. Therefore, digital simulation is vital in conducting research related to UAV and its networks. One of the UAV simulators recently developed is FlyNetSim. FlyNetSim is an open-source UAV Network Simulator based on NS-3 and Ardupilot. In the application, UAV can have some network-related problems. One of them is when a UAV is in an environment that has a lot of interfering networks because it is contending a network channel. This can cause network delay. These delays will raise with the increase in the number of nodes that interfere with the UAV. To overcome the problem of interfering nodes, we propose to add a priority set module for UAV and Ground Control Station (GCS). The proposed module will set priority for the UAV and GCS socket. This module is intended to give priority to packets sent by UAVs or GCS. Later the packet that has priority will be sent first to reduce the delay caused by the appearance of interfering nodes. Setting priority in the socket may reduce the average delay caused by contending nodes. Test result shows that there is a 16.2% reduction of average delay. Test result also shows that there is a 4% raise in packet delivery ratio.

Autonomous formation flight has been a fast growing research area during the last decade. Unmanned aerial vehicles (UAVs) can achieve a wide range of benefits from formation flight, such as fuel saving and aerial refueling. In this paper, the development of a small UAV system, called Swallow UAV system, as testbed of formation flight, is presented. Swallow UAV system includes Swallow UAV itself and a Hardware-In-the-Loop Simulation Environment (HILSE). Swallow UAV is a fixed wing, pusher-type, electric motor driven aircraft with 1.4 meter wing span. Its avionics system includes an onboard computer and a sensor system with GPS, IMU, air data sensor, and motor monitoring unit to provide position, velocity, inertial data, and system health. Communications between UAVs and the ground control station (GCS) are conducted by wireless modem. A fuzzy navigation and control system (FNCS) is developed for the waypoint navigation and attitude control of the aircraft. GPS velocity based pseudo attitude and fuzzy logic control are applied to FNCS for attitude estimation and fast implementation of controllers. HILSE is built to verify FNCS with onboard computer on the ground. X-Plane flight simulator is applied to simulate aircraft dynamics and output sensor data. GCS software, built with data distributor, transmits these data to the onboard computer and receive control signal from it so the onboard computer can control the aircraft in X-Plane. HILSE can perform pre-flight test for software debug, which improves safety and efficiency of flight test. The design of Swallow UAV is verified by flight test and the result shows that it has sufficient flight performance with waypoint navigation and attitude control capability to be used as a formation flight testbed.

Information acquisition and rescue operation management can be facilitated by the use of unmanned aerial vehicles (UAVs) equipped with cameras and sensors that are capable of aerial photography and acquire important information from disaster-stricken areas. UAV technology is a relatively low-cost, flexible, and safe solution in missions that are difficult for humans or other systems. UAV usage in pre-and post-disaster assessment, response, and management is an active area of research. There are two important technical aspects of UAV applications in disaster situations. The first is the limited communication range. Information acquired and photography produced by UAVs are only transmitted in real-time to the ground control station (GCS) within its communication range. Remote real-time monitoring of distant areas beyond the UAV's communication range is not possible using a single UAV. It is only possible through communication among several UAVs. The second is the relatively short flight time due to limited battery capacity. UAV replacement is required in order to carry out a persistent monitoring mission. In this paper, we propose a new remote real-time monitoring system of a distant disaster area using multiple UAVs. Wireless communications between multiple UAVs constitutes a UAV network in our proposed system. Moreover, the proposed system replaces the UAVs in-flight with standby UAVs according to the remaining energy levels. We compare the performance of the proposed system with an existing UAV system. Our simulation results show that the proposed system can achieve efficient remote real-time monitoring.

Solar energy is one of the most strategic energy sources for the world’s economic development. This has caused the number of solar photovoltaic plants to increase around the world; consequently, they are installed in places where their access and manual inspection are arduous and risky tasks. Recently, the inspection of photovoltaic plants has been conducted with the use of unmanned aerial vehicles (UAV). Although the inspection with UAVs can be completed with a drone operator, where the UAV flight path is purely manual or utilizes a previously generated flight path through a ground control station (GCS). However, the path generated in the GCS has many restrictions that the operator must supply. Due to these restrictions, we present a novel way to develop a flight path automatically with coverage path planning (CPP) methods. Using a DL server to segment the region of interest (RoI) within each of the predefined PV plant images, three CPP methods were also considered and their performances were assessed with metrics. The UAV energy consumption performance in each of the CPP methods was assessed using two different UAVs and standard metrics. Six experiments were performed by varying the CPP width, and the consumption metrics were recorded in each experiment. According to the results, the most effective and efficient methods are the exact cellular decomposition boustrophedon and grid-based wavefront coverage, depending on the CPP width and the area of the PV plant. Finally, a relationship was established between the size of the photovoltaic plant area and the best UAV to perform the inspection with the appropriate CPP width. This could be an important result for low-cost inspection with UAVs, without high-resolution cameras on the UAV board, and in small plants.

We study the problem of finding the minimum-length curvature constrained closed path through a set of regions in the plane. This problem is referred to as the Dubins Traveling Salesperson Problem with Neighborhoods (DTSPN). An algorithm is presented that uses sampling to cast this infinite dimensional combinatorial optimization problem as a Generalized Traveling Salesperson Problem (GTSP) with intersecting node sets. The GTSP is then converted to an Asymmetric Traveling Salesperson Problem (ATSP) through a series of graph transformations, thus allowing the use of existing approximation algorithms. This algorithm is shown to perform no worse than the best existing DTSPN algorithm and is shown to perform significantly better when the regions overlap. We report on the application of this algorithm to route an Unmanned Aerial Vehicle (UAV) equipped with a radio to collect data from sparsely deployed ground sensors in a field demonstration of autonomous detection, localization, and verification of multiple acoustic events.

Unmanned Aerial Vehicles (UAVs) can collaborate to collect and relay data for ground sensors in remote and hostile areas. In multi-UAV-assisted wireless sensor networks (MA-WSN), the UAVs' movements impact on channel condition and can fail data transmission, this situation along with newly arrived data give rise to buffer overflows at the ground sensors. Thus, scheduling data transmission is of utmost importance in MA-WSN to reduce data packet losses resulting from buffer overflows and channel fading. In this paper, we investigate the optimal ground sensor selection at the UAVs to minimize data packet losses. The optimization problem is formulated as a multi-agent Markov decision process, where network states consist of battery levels and data buffer lengths of the ground sensor, channel conditions, and waypoints of the UAV along the trajectory. In practice, an MA-WSN contains a large number of network states, while the up-to-date knowledge of the network states and other UAVs' sensor selection decisions is not available at each agent. We propose a Multi-UAV Deep Reinforcement Learning based Scheduling Algorithm (MUAIS) to minimize the data packet loss, where the UAVs learn the underlying patterns of the data and energy arrivals at all the ground sensors. Numerical results show that the proposed MUAIS achieves at least 46 % and 35% lower packet loss than an optimal solution with single-UAV and an existing non-learning greedy algorithm, respectively.

As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber–physical systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead a UAV to fly into a sensitive area and threaten public safety and private security. The conventional spoofing detection methods need too much overhead, which stops efficient detection from working in a computation-constrained UAV and provides an efficient response to attacks. In this paper, we propose a novel approach to obtain a lightweight detection model in the UAV system so that GPS spoofing attacks can be detected from a long distance. With long-short term memory (LSTM), we propose a lightweight detection model on the ground control stations, and then we distill it into a compact size that is able to run in the control system of the UAV with knowledge distillation. The experimental results show that our lightweight detection algorithm runs in UAV systems reliably and can achieve good performance in GPS spoofing detection.

This paper looks at the trends in design procedures in Unmanned Aerial Vehicles (UAVs). Rapid advances in technology are enabling more and more capability to be placed on smaller airframes which is spurring a large increase in the number of UAVs being deployed in the army. The military role of UAV is growing at unprecedented rates. The UAV is an acronym for Unmanned Aerial Vehicle, which is an aircraft with no pilot on board. UAVs can be remote controlled aircraft (e.g. flown by a pilot at a ground control station) or can fly autonomously based on pre-programmed flight plans or more complex dynamic automation systems. A variety of design configurations are in use. The primary driving parameters in all UAVs is the need for maximizing available wing area and wing effectiveness, while minimizing the required storage volume. The major factors in determining the relative merit of the different concepts are the evaluation of structural viability, mechanical complexity and overall system survivability by G forces. This paper examines some of the design methodologies and hardware-in-the loop simulation environment to support and validate the UAV hardware and software development.

Communication architecture and operations for SDR-enabled UAVs network in disaster-stressed areas

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

The optimal flight trajectory of an unmanned aerial vehicle acting as a primary central communication node for multiple randomly placed unattended ground sensors in minimum time is considered. An algorithm is developed to ensure minimum required data transfer between an airborne agent and a sparse unattended ground sensor network. In this modified travelling salesman problem, the control policy enables the airborne vehicle to change altitude, heading and velocity to best communicate with each ground sensor via an on-board omni-directional antenna. Optimal flight trajectory planning algorithms are proposed to ensure the unmanned aerial vehicle successfully receives the required data transmission from each ground sensor in minimum time.

A cost-efficient elastic UAV relay network construction method with guaranteed QoS

The Department of the Defense has transitioned smart devices into the battlefield as a portable hand-held unmanned aerial vehicle ground control station without adequate cyber security protections, putting critical mission data at risk to cyber security attacks. Industry has developed software apps for smart phones and tablets that allows soldiers to not only pilot unmanned aerial vehicles (UAVs) but to share and receive intelligence and reconnaissance videos and images remotely from the ground control station (GCS) or directly from the UAV. The Department of Defense has not developed a secure communication network that will support a large quantity of smart devices, nor certification, standards or policies for operation of secure smart devices. Therefore, mission critical information will be shared through unsecured, mobile and wireless networks and through unclassified, unsecure smart technology that are vulnerable to cyber security risks. Lack of security of the mobile and wireless networks and smart devices could result in the unintentional sharing of data as well as loss of control of the UAV to enemies. The Department of Defense has failed to develop a threat model and risk assessment to identify the cyber security threats and ensure the proper security countermeasures are in place. This paper will analyze the cyber security vulnerabilities within the communication links, smart devices hardware, specifically smart phones and tablets, and software applications to develop a risk model of the threat profile of the GCS networking hub. This model will help designers and users of the military and civilian UAV communities to understand the threat profile of the GCS networking hub to develop a secure communication network based upon the vulnerabilities identified for smart phones and tablets.

The use of unmanned aerial vehicles (UAVs) in various military and civil applications is the subject of much current attention. With recent developments in personal computer technology, and the availability at affordable cost of peripherals, and electronic and optical sensors, UAVs for long endurance missions, with flight autonomy beyond the visual range, have become an attractive challenge for study in universities and research institutes. This paper describes the development of a target-lock-on optical remote sensing system to be used as a payload in a university-class UAV. To accomplish autonomous way-point navigation for the conduct of optical sensing surveillance, a gimbaled-platform with servo control and an Attitude and Heading Reference System (AHRS) navigation system for UAV position and attitude measurements have been developed. The UAV also utilises a Global Position System (GPS) receiver, a pressure altimeter, gyroscopes and an electric compass. A novel mathematical model is proposed to calculate the optimal parameters for orientating the CCD camera line of sight with a ground target, designated in real time from a ground control station. Both ground and flight test results have demonstrated the feasibility of the navigation control scheme and the UAV’s ability to conduct ground target acquisition and image transmission.