用无人机定期检查物联网设备，平衡信任更新与数据传输效率。
Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks
- 用深度强化学习优化无人机充电和检测策略
- 信任年龄降低88%，数据吞吐损失减少30%
- 适合关注物联网安全与无人机协同的从业者
物联网设备分布广泛且可能无防护，易受攻击，需频繁验证。本文提出一种基于太阳能充电站的无人机辅助验证框架，通过深度强化学习优化无人机的充电计划和每趟飞行中待验证设备的选择。核心挑战在于：验证时设备离线影响数据向网关传输，且充电站能量随时间波动，影响飞行时长与充电安排。仿真结果表明，该方案可将平均信任年龄降低88%，因验证导致的吞吐量损失减少30%。
原文摘要 · Abstract (English)
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%.
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