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

TL;DR

This paper proposes a joint optimization framework for balancing system latency and data freshness in cache-enabled mobile crowdsensing networks, using a decomposition approach and real-time algorithms.

Contribution

It introduces a novel framework that jointly optimizes sensing, caching, and task allocation to improve latency and data freshness in MCS networks.

Findings

The proposed algorithm effectively balances latency and data freshness.

Simulation results show significant improvements over baseline methods.

The framework adapts to dynamic network conditions in real-time.

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.