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

TL;DR

This paper presents a DRL-based approach to optimize UAV trajectories and charging schedules in IoT networks, significantly reducing trust age and throughput loss while ensuring device attestation.

Contribution

It introduces a novel DRL framework for joint UAV trajectory and charging schedule optimization in solar-powered UAV-assisted IoT networks.

Findings

Reduces average age of trust by 88%

Decreases throughput loss due to attestation by 30%

Demonstrates effectiveness of DRL in UAV trajectory planning

Abstract

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%.