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

TL;DR

This paper introduces a UAV-assisted data forwarding system using distributed beamforming and deep reinforcement learning to minimize age of information and energy consumption in IoT networks.

Contribution

It presents a novel joint optimization framework for UAV trajectories and communication schedules using DRL to improve AoI and energy efficiency.

Findings

The proposed DRL algorithm outperforms benchmark algorithms.

Distributed beamforming extends communication range and reduces flight frequency.

The system effectively minimizes AoI and energy consumption.

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.