ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting

• URL: http://arxiv.org/abs/2405.11013v1
• Date: Fri, 17 May 2024 16:53:19 GMT
• Title: ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting
• Authors: Praveen Kumar, Priyadarshni, Rajiv Misra,
• Abstract summary: Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning ( CPP) We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments.
• Score: 3.746548465186206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unmanned Aerial Vehicles (UAVs) have gained popularity in data harvesting (DH) and coverage path planning (CPP) to survey a given area efficiently and collect data from aerial perspectives, while data harvesting aims to gather information from various Internet of Things (IoT) sensor devices, coverage path planning guarantees that every location within the designated area is visited with minimal redundancy and maximum efficiency. We propose the ARDDQN (Attention-based Recurrent Double Deep Q Network), which integrates double deep Q-networks (DDQN) with recurrent neural networks (RNNs) and an attention mechanism to generate path coverage choices that maximize data collection from IoT devices and to learn a control scheme for the UAV that generalizes energy restrictions. We employ a structured environment map comprising a compressed global environment map and a local map showing the UAV agent's locate efficiently scaling to large environments. We have compared Long short-term memory (LSTM), Bi-directional long short-term memory (Bi-LSTM), Gated recurrent unit (GRU) and Bidirectional gated recurrent unit (Bi-GRU) as recurrent neural networks (RNN) to the result without RNN We propose integrating the LSTM with the Attention mechanism to the existing DDQN model, which works best on evolution parameters, i.e., data collection, landing, and coverage ratios for the CPP and data harvesting scenarios.

Related papers

• PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling [84.34811206119619]
  We propose a two-stage approach to pre-routing timing prediction. First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist. Second, we use a novel node updating scheme for message passing on GCN, following the topological sorting sequence of the learned graph embedding and circuit graph. Experiments on 21 real world circuits achieve a new SOTA R2 of 0.93 for slack prediction, which is significantly surpasses 0.59 by previous SOTA method.
  arXiv  Detail & Related papers  (2024-02-27T02:23:07Z)
• 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)
• Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes [99.54065757867554]
  We invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. A deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. We further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism.
  arXiv  Detail & Related papers  (2021-10-28T14:18:56Z)
• Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization [79.96177511319713]
  We invoke deep learning (DL) to assist routing in aeronautical ad-hoc networks (AANETs) A deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. We extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime.
  arXiv  Detail & Related papers  (2021-10-28T14:18:22Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  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.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints [0.0]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints. We propose a decentralized deep reinforcement learning approach to solve the problem.
  arXiv  Detail & Related papers  (2021-04-03T22:22:20Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning [18.266087952180733]
  We propose a multi-agent reinforcement learning (MARL) approach that can adapt to profound changes in the scenario parameters defining the data harvesting mission. We show that our proposed network architecture enables the agents to cooperate effectively by carefully dividing the data collection task among themselves.
  arXiv  Detail & Related papers  (2020-10-23T14:59:30Z)
• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach [18.266087952180733]
  We propose a new end-to-end reinforcement learning approach to UAV-enabled data collection from Internet of Things (IoT) devices. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. We show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters.
  arXiv  Detail & Related papers  (2020-07-01T15:14:16Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.