Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks
This addresses security and efficiency challenges for IoT network operators, but it is incremental as it applies existing DRL methods to a specific UAV-aided scenario.
The paper tackles the problem of securing IoT networks by optimizing UAV trajectories for device attestation, balancing trust and throughput, and achieves an 88% reduction in average age of trust and a 30% reduction in throughput loss.
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%.