Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2107.11015v1
• Date: Fri, 23 Jul 2021 03:33:29 GMT
• Title: Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach
• Authors: Yang Wang, Zhen Gao, Jun Zhang, Xianbin Cao, Dezhi Zheng, Yue Gao, Derrick Wing Kwan Ng, Marco Di Renzo
• Abstract summary: In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
• Score: 93.67588414950656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a sophisticated three-dimensional (3D) environment, where the UAV's trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplified two-dimensional scenario and the availability of perfect channel state information (CSI), this paper considers a practical 3D urban environment with imperfect CSI, where the UAV's trajectory is designed to minimize data collection completion time subject to practical throughput and flight movement constraints. Specifically, inspired from the state-of-the-art deep reinforcement learning approaches, we leverage the twin-delayed deep deterministic policy gradient (TD3) to design the UAV's trajectory and present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. In particular, we set an additional information, i.e., the merged pheromone, to represent the state information of UAV and environment as a reference of reward which facilitates the algorithm design. By taking the service statuses of IoT nodes, the UAV's position, and the merged pheromone as input, the proposed algorithm can continuously and adaptively learn how to adjust the UAV's movement strategy. By interacting with the external environment in the corresponding Markov decision process, the proposed algorithm can achieve a near-optimal navigation strategy. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.

Related papers

• ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction [89.89610257714006]
  Existing methods prioritize higher accuracy to cater to the demands of these tasks. We introduce a series of targeted improvements for 3D semantic occupancy prediction and flow estimation. Our purelytemporalal architecture framework, named ALOcc, achieves an optimal tradeoff between speed and accuracy.
  arXiv  Detail & Related papers  (2024-11-12T11:32:56Z)
• 3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation [10.91369146380236]
  Planning 3D UAV trajectories over a continuous temporal-spatial domain is a computationally intensive problem. We propose a matrix-based differential evolution with constraint (MDECH) algorithm to address non-efficient constrained optimization problems.
  arXiv  Detail & Related papers  (2024-10-08T07:33:48Z)
• Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms [5.57865728456594]
  Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications. These applications include providing wireless services to ground users and collecting data from areas affected by disasters. UAVs' limited resources, energy budget, and strict mission completion time have posed challenges in adopting UAVs for these applications.
  arXiv  Detail & Related papers  (2024-01-20T05:05:39Z)
• UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer [8.203870302926614]
  Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. 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.
  arXiv  Detail & Related papers  (2023-11-08T17:13:19Z)
• Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System [25.32139119893323]
  Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems. The UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV.
  arXiv  Detail & Related papers  (2022-10-27T06:27:40Z)
• 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning [75.78929539923749]
  Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. It is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT) In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices.
  arXiv  Detail & Related papers  (2021-06-06T14:08:41Z)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• NOMA in UAV-aided cellular offloading: A machine learning approach [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T17:38:48Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.