mlsanityData to 14 Oct 2026

Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks

arXiv 2507.03950 ↗First posted 5 Jul 2025cs.NI cs.AI cs.LGcited by 0

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Abstract (arXiv)

Devices operating in Internet of Things (IoT) networks may be deployed across vast geographical areas and interconnected via multi-hop communications. Further, they may be unguarded. This makes them vulnerable to attacks and motivates operators to check on devices frequently. To this end, we propose and study an Unmanned Aerial Vehicle (UAV)-aided attestation framework for use in IoT networks with a charging station powered by solar. A key challenge is optimizing the trajectory of the UAV to ensure it attests as many devices as possible. A trade-off here is that devices being checked by the UAV are offline, which affects the amount of data delivered to a gateway. Another challenge is that the charging station experiences time-varying energy arrivals, which in turn affect the flight duration and charging schedule of the UAV. To address these challenges, we employ a Deep Reinforcement Learning (DRL) solution to optimize the UAV's charging schedule and the selection of devices to be attested during each flight. The simulation results show that our solution reduces the average age of trust by 88% and throughput loss due to attestation by 30%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 5 Jul 2025.

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5 of 28 references.

References · 5

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2014Data-Driven Stochastic Models and Policies for Energy Harvesting Sensor Communicationscited by 2
  5. 2024Age of Trust (AoT): A Continuous Verification Framework for Wireless Networkscited by 1

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  3. 2026Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning
  4. 2025AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
  5. 2024Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
  6. 2025Wireless Laser Power Transfer for Low-altitude Uncrewed Aerial Vehicle-assisted Internet of Things: Paradigms, Challenges, and Solutions ↗
  7. 2024Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading
  8. 2025Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach