Joint System Latency and Data Freshness Optimization for Cache-enabled Mobile Crowdsensing Networks

2025-01-24

AI Summary

This work addresses cache-enhanced mobile crowdsensing networks, jointly optimizing sensing decisions, user–channel selection, task allocation, and caching strategies under smartphone energy and storage constraints to balance system responsiveness and data freshness (Age of Information, AoI). To this end, we formulate a novel bi-objective optimization problem integrating latency and weighted AoI minimization—first of its kind in this domain. We propose a hierarchical decomposition framework coupled with a Bayesian-driven adaptive cache update mechanism and design a lightweight real-time online algorithm. Our method integrates mixed-integer non-convex optimization, sequential single-step subproblem decomposition, and sensing–caching co-decision making. Extensive simulations demonstrate that our approach reduces average system latency by 32.7% and AoI by 28.5% compared to baseline methods, significantly improving the trade-off between timeliness and energy efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based 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
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Mobile crowdsensing (MCS) networks enable large-scale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cache-enabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach.

Problem

• Mobile Sensing Networks
• Energy Efficiency
• Data Freshness

Innovation

• Mobile Crowd Sensing
• Data Freshness Optimization
• Response Speed Enhancement