Deep Reinforcement Learning for Joint Cruise Control and Intelligent Data Acquisition in UAVs-Assisted Sensor Networks

Unmanned aerial vehicle (UAV)-assisted sensor networks (UASNets), which play a crucial role in creating new opportunities, are experiencing significant growth in civil applications worldwide. UASNets improve disaster management through timely surveillance and advance precision agriculture with detailed crop monitoring, thereby significantly transforming the commercial economy. UASNets revolutionize the commercial sector by offering greater efficiency, safety, and cost-effectiveness, highlighting their transformative impact. A fundamental aspect of these new capabilities and changes is the collection of data from rugged and remote areas. Due to their excellent mobility and maneuverability, UAVs are employed to collect data from ground sensors in harsh environments, such as natural disaster monitoring, border surveillance, and emergency response monitoring. One major challenge in these scenarios is that the movements of UAVs affect channel conditions and result in packet loss. Fast movements of UAVs lead to poor channel conditions and rapid signal degradation, resulting in packet loss. On the other hand, slow mobility of a UAV can cause buffer overflows of the ground sensors, as newly arrived data is not promptly collected by the UAV. Our proposal to address this challenge is to minimize packet loss by jointly optimizing the velocity controls and data collection schedules of multiple UAVs.Furthermore, in UASNets, swift movements of UAVs result in poor channel conditions and fast signal attenuation, leading to an extended age of information (AoI). In contrast, slow movements of UAVs prolong flight time, thereby extending the AoI of ground sensors.To address this challenge, we propose a new mean-field flight resource allocation optimization to minimize the AoI of sensory data

UAV Aided Data Collection for Wildlife Monitoring using Cache-enabled Mobile Ad-hoc Wireless Sensor Nodes

Unmanned aerial vehicle (UAV) assisted data collection is not a new concept and has been used in various mobile ad hoc networks. In this paper, we propose a caching assisted scheme alternative to routing in MANETs for the purpose of wildlife monitoring. Rather than deploying a routing protocol, data is collected and transported to and from a base station using a UAV. Although some literature exists on such an approach, we propose the use of intermediate caching between the mobile nodes and compare it to a baseline scenario where no caching is used. The paper puts forward our communication design where we have simulated the movement of multiple mobile sensor nodes in a field that move according to the Levy walk model imitating wildlife animal foraging and a UAV that makes regular trips across the field to collect data from them. The unmanned aerial vehicle can collect data not only from the current node it is communicating with but also data of other nodes that this node came into contact with. Simulations show that exchanging cached data is highly advantages as the drone can indirectly communicate with many more mobile nodes

UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer

Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm's decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer non-convex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems