AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

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Publication Details

Published
2025-06-08
DOI
https://doi.org/10.1109/icc52391.2025.11161522
Citations
1
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
Field-Weighted Citation Impact
1.49
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article

AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

Victor C. M. Leung, Zemin Sun, Geng Sun, Gang Liu et al.
1 citations
Distributed Sensor Networks and Detection Algorithms
1.49
article

AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT

Victor C. M. Leung, Zemin Sun, Geng Sun, Gang Liu, Jiahui Li, Jiacheng Wang, Zifan Lang
article en
1 citations

Abstract

This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

Nanyang Technological University (SG), Jilin University (CN), Jilin Province Science and Technology Department (CN), Shenzhen Technology University (CN)
Openalex Percentile: Top 13%
Distributed Sensor Networks and Detection Algorithms
1.49
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AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT — Victor C. M. Leung, Zemin Sun, et al. · (2025) | TGRS Research Map | TGRS