Optimizing Age-of-Information in Piggyback Networks with Recurrent Data Generation

2025-05-20

AI Summary

This paper addresses data freshness assurance for resource-constrained IoT devices by optimizing periodic sensing data collection and transmission in UAV-based patrol systems, aiming to minimize the network-wide maximum Age of Information (MAI). Method: We first prove that MAI minimization is NP-complete. Leveraging combinatorial optimization and AoI modeling, we propose two efficient path-planning algorithms with provable approximation ratios, integrating the Store-Carry-Forward (SCF) communication paradigm. Contribution/Results: Theoretical analysis establishes rigorous approximation guarantees for both algorithms. Extensive experiments across diverse scenarios demonstrate that our approaches achieve near-optimal MAI—closely approaching the theoretical lower bound—while maintaining tractable runtime complexity. Moreover, they significantly outperform existing baseline strategies in terms of both MAI reduction and computational efficiency.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

Age-of-information (AoI) is a critical metric that quantifies the freshness of data in communication systems. In the era of the Internet of Things (IoT), data collected by resource-constrained devices often need to be transmitted to a central server to extract valuable insights in a timely manner. However, maintaining a stable and direct connection between a vast number of IoT devices and servers is often impractical. The Store-Carry-Forward (SCF) communication paradigm, such as Piggyback networks, offers a viable solution to address the data collection and transmission challenges in distributed IoT systems by leveraging the mobility of mobile nodes. In this work, we investigate AoI within the context of patrolling data collection drones, where data packets are generated recurrently at devices and collected by a patrolling drone to be delivered to a server. Our objective is to design a patrolling route that minimizes the Maximum Age-of-Information (MAI) across the system. We demonstrate that determining whether a route with an MAI below a certain threshold can be constructed is NP-Complete. To address this challenge, we propose two approaches with approximation guarantees. Our evaluation results show that the proposed approaches can achieve near-optimal routes in reasonable time across various scenarios

Problem

• Optimizing Age-of-Information in piggyback networks with recurrent data generation
• Designing drone patrolling routes to minimize Maximum Age-of-Information
• Addressing NP-Complete challenge of route construction with approximation guarantees

Innovation

• Optimize Age-of-Information in piggyback networks
• Use Store-Carry-Forward paradigm for IoT data
• Design NP-Complete patrolling routes with guarantees