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

Kexin Shi, Yaru Fu, Yongna Guo, Fu Lee Wang, Yan Zhang

TL;DR

The paper tackles the challenge of minimizing system latency while maintaining data freshness in cache-enabled Mobile Crowdsensing (MCS) networks by formulating a weighted delay and Age of Information (AoI) objective. It introduces a decomposition into sequential one-shot subproblems and a time-efficient online algorithm that leverages the Hungarian algorithm for latency minimization and a Bayesian update scheme for cache management. Key contributions include (i) a joint optimization framework for sensing decisions, user/subchannel allocation, task caching, and data size allocation, (ii) a closed-form solution for sensing data distribution under energy constraints, (iii) a cache-management strategy based on posterior probabilities to balance freshness and relevance, and (iv) a scalable algorithm with proven complexity and strong experimental gains over baselines. The results demonstrate substantial reductions in the weighted latency-AoI metric, validating the approach's practicality for real-time, edge-assisted MCS deployments.

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.

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

TL;DR

The paper tackles the challenge of minimizing system latency while maintaining data freshness in cache-enabled Mobile Crowdsensing (MCS) networks by formulating a weighted delay and Age of Information (AoI) objective. It introduces a decomposition into sequential one-shot subproblems and a time-efficient online algorithm that leverages the Hungarian algorithm for latency minimization and a Bayesian update scheme for cache management. Key contributions include (i) a joint optimization framework for sensing decisions, user/subchannel allocation, task caching, and data size allocation, (ii) a closed-form solution for sensing data distribution under energy constraints, (iii) a cache-management strategy based on posterior probabilities to balance freshness and relevance, and (iv) a scalable algorithm with proven complexity and strong experimental gains over baselines. The results demonstrate substantial reductions in the weighted latency-AoI metric, validating the approach's practicality for real-time, edge-assisted MCS deployments.

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.
Paper Structure (14 sections, 2 theorems, 26 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 14 sections, 2 theorems, 26 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Network Description and AoI Mechanism
  4. Task Sensing and Transmission Model
  5. Problem Formulation
  6. Algorithm Design
  7. Problem Transformation
  8. System Latency Minimization Sub-problem
  9. Task Sensing and Caching Sub-problem
  10. Task sensing decision
  11. Caching policy
  12. Time-efficient Joint Optimization Algorithm
  13. Simulation Result
  14. Conclusion

Key Result

Theorem 2

The system latency minimization optimization problem eq:minD_bar is equivalent to:

Figures (5)

  • Figure 1: An example of the cache-enabled MCS systems.
  • Figure 2: Weighted sum of delay and AoI versus the transmit power.
  • Figure 3: Weighted sum of delay and AoI versus the number of users.
  • Figure 4: Weighted sum of delay and AoI versus the number of subchannels.
  • Figure 5: Weighted sum of delay and AoI versus task bit size.

Theorems & Definitions (7)

  • proof
  • Theorem 2
  • proof
  • proof
  • Definition 4
  • Theorem 5
  • proof