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

Zifan Lang, Guixia Liu, Geng Sun, Jiahui Li, Zemin Sun, Jiacheng Wang, Victor C. M. Leung

TL;DR

This work tackles the problem of timely data delivery in UAV-assisted IoT by jointly optimizing UAV trajectories and SN-UAV scheduling to minimize time-averaged AoI and UAV energy. It introduces a UAV-enabled vehicle aerial array (VAA) with distributed beamforming to extend communication range and reduce flight frequency, enabling continuous data relay. To solve the resulting non-convex, dynamic optimization, the authors propose SAC-TLA, a SAC-based DRL algorithm enhanced with temporal sequence processing, LNGRU, and attention, plus dynamic proximity-based action mapping to stabilize learning and accelerate convergence. Simulation results show SAC-TLA outperforms benchmark DRL methods in AoI reduction and energy efficiency, validating its effectiveness in dynamic UAV-IoT environments with distributed beamforming.

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.

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

TL;DR

This work tackles the problem of timely data delivery in UAV-assisted IoT by jointly optimizing UAV trajectories and SN-UAV scheduling to minimize time-averaged AoI and UAV energy. It introduces a UAV-enabled vehicle aerial array (VAA) with distributed beamforming to extend communication range and reduce flight frequency, enabling continuous data relay. To solve the resulting non-convex, dynamic optimization, the authors propose SAC-TLA, a SAC-based DRL algorithm enhanced with temporal sequence processing, LNGRU, and attention, plus dynamic proximity-based action mapping to stabilize learning and accelerate convergence. Simulation results show SAC-TLA outperforms benchmark DRL methods in AoI reduction and energy efficiency, validating its effectiveness in dynamic UAV-IoT environments with distributed beamforming.

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.

Paper Structure

This paper contains 13 sections, 11 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Models
  3. System Overview
  4. Network Model
  5. AoI Model
  6. UAV Energy Cost Model
  7. Problem Formulation
  8. The Proposed SAC-TLA
  9. Motivations and MDP
  10. Standard SAC Algorithm
  11. SAC-TLA Algorithm
  12. Simulation Results and Analysis
  13. Conclusion

Figures (4)

  • Figure 1: A UAV-assisted AoI-sensitive data forwarding system in IoT based on distributed beamforming.
  • Figure 2: Time allocation for transmission and moving processes.
  • Figure 3: The framework of the SAC-TLA algorithm.
  • Figure 4: Simulation results. (a) Cumulative rewards training curve. (b) The optimization objective values of SAC-TLA, TQC, SAC, PPO, and TD3.