Age of Information Aware Trajectory Planning of UAV

This paper investigates the planning of Unmanned aerial vehicles (UAVs) trajectory in UAV-assisted Internet of Things (IoT) networks with a massive number of IoT devices (IoTDs). Existing UAV-assisted IoT network data collection schemes mostly focus on optimizing energy consumption and data collecti...

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Veröffentlicht in:IEEE transactions on cognitive communications and networking 2024-01, Vol.10 (6), p.2344-2356
Hauptverfasser: Pan, Junnan, Li, Yun, Chai, Rong, Xia, Shichao, Zuo, Linli
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container_title IEEE transactions on cognitive communications and networking
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creator Pan, Junnan
Li, Yun
Chai, Rong
Xia, Shichao
Zuo, Linli
description This paper investigates the planning of Unmanned aerial vehicles (UAVs) trajectory in UAV-assisted Internet of Things (IoT) networks with a massive number of IoT devices (IoTDs). Existing UAV-assisted IoT network data collection schemes mostly focus on optimizing energy consumption and data collection throughput, while neglecting the temporal value of data collection. With the assistance of the age of information (AoI), the average AoI of data collected by the UAV from IoTDs is minimized to enhance information freshness. To strike a balance between trajectory planning and information freshness, a two-stage artificial intelligence (AI) algorithm is proposed in this paper. Firstly, to tackle the issue of prolonged flight time caused by the UAV sequentially collecting data from IoTDs, an improved clustering algorithm is introduced to determine the cluster centers of IoTDs. Secondly, considering that the UAV lacks prior knowledge of the IoT network environment, the AoI minimization problem is reformulated as a Markov decision process (MDP). A neural network algorithm based on twin-delayed deep deterministic policy gradient (TD3) is employed to optimize UAV trajectory. Simulation results show that the proposed algorithm is superior to the benchmark algorithms, particularly in scenarios involving a massive number of IoTDs.
doi_str_mv 10.1109/TCCN.2024.3412073
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Existing UAV-assisted IoT network data collection schemes mostly focus on optimizing energy consumption and data collection throughput, while neglecting the temporal value of data collection. With the assistance of the age of information (AoI), the average AoI of data collected by the UAV from IoTDs is minimized to enhance information freshness. To strike a balance between trajectory planning and information freshness, a two-stage artificial intelligence (AI) algorithm is proposed in this paper. Firstly, to tackle the issue of prolonged flight time caused by the UAV sequentially collecting data from IoTDs, an improved clustering algorithm is introduced to determine the cluster centers of IoTDs. Secondly, considering that the UAV lacks prior knowledge of the IoT network environment, the AoI minimization problem is reformulated as a Markov decision process (MDP). 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subjects age of information (AoI)
Algorithms
Artificial intelligence
Autonomous aerial vehicles
Clustering
Clustering algorithms
Data collection
deep reinforcement learning (DRL)
Energy consumption
Flight time
Freshness
Internet of Things
Internet of Things (IoT)
Markov processes
Neural networks
Quality of service
Real-time systems
Trajectory
Trajectory optimization
Trajectory planning
Unmanned aerial vehicle (UAV)
Unmanned aerial vehicles
title Age of Information Aware Trajectory Planning of UAV
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