AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
It addresses AoI and energy efficiency for IoT networks using UAVs, but appears incremental as it combines existing techniques like distributed beamforming and DRL for optimization.
This paper tackles the problem of high age of information (AoI) and energy consumption in UAV-assisted IoT networks by proposing a system that uses distributed beamforming and deep reinforcement learning to optimize UAV trajectories and communication schedules, resulting in improved performance over benchmark algorithms.
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.