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

2025-07-05

AI Summary

To address the vulnerability of devices, frequent security authentication requirements, and resultant communication disruptions and energy constraints in UAV-assisted multi-hop IoT networks, this paper proposes a joint optimization framework for Age of Trust (AoT) and throughput. We innovatively model the time-varying energy characteristics of solar-powered charging stations and integrate multi-hop communication, energy harvesting, and task scheduling constraints. A deep reinforcement learning–based algorithm is designed for joint UAV trajectory planning and charging scheduling, dynamically balancing authentication frequency, data transmission latency, and UAV endurance limitations. Simulation results demonstrate an 88% reduction in average AoT and a 30% decrease in throughput loss induced by authentication, significantly enhancing both network security and data timeliness.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Authentication, authorization, and access control
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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%.

Problem

• Optimizing UAV trajectory for device attestation in IoT networks
• Balancing device attestation and network throughput trade-off
• Managing UAV charging with time-varying solar energy supply

Innovation

• UAV-aided attestation framework for IoT networks
• Deep Reinforcement Learning optimizes UAV charging
• DRL selects devices for attestation efficiently