AoI-minimizing Scheduling in UAV-relayed IoT Networks
Biplav Choudhury, Vijay K. Shah, Aidin Ferdowsi, Jeffrey H. Reed, and, Y. Thomas Hou

TL;DR
This paper addresses the challenge of minimizing Age of Information in two-hop UAV-relayed IoT networks by proposing optimal and deep learning-based scheduling policies, demonstrating their effectiveness through simulations.
Contribution
It introduces a low-complexity MAF-MAD scheduler and a DQN-based scheduler for AoI minimization in UAV-relayed IoT networks, with analysis of their performance.
Findings
MAF-MAD is optimal under ideal conditions.
DQN outperforms baseline schedulers in small networks.
MAF-MAD scales better for larger networks.
Abstract
Due to flexibility, autonomy and low operational cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as \textit{relays} to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
