Age-of-Information and Latency Minimization for Cache-Enabled Mobile Crowdsensing Networks

Mobile crowdsensing (MCS) enables large-scale data collection but suffers from excessive resource consumption due to repetitive sensing operations. By introducing edge caching capabilities, MCS systems can significantly reduce redundant data collection through caching re-use. However, it presents a critical trade-off between sensing cost efficiency and data freshness in cache-enabled MCS systems. To address this challenge, we investigate the balance between re-using cached task results and re-sensing tasks in MCS networks, aiming to minimize the system latency while ensuring the freshness of cached task information. To this end, we formulate a weighted sum of latency and age of information (AoI) minimization problem by jointly optimizing sensing decisions, user selection, data size and subchannel allocation, along with caching strategy. To tackle this non-convex and mixed-integer programming problem, we first decompose the original long-term optimization problem into multiple one-shot sub-problems. For each one-shot problem, we reformulate it as a system latency minimization sub-problem to determine the optimal solution under the assumption of task re-sensing. The system latency is then compared with the AoI value to determine task sensing decisions. Subsequently, we develop a Bayesian caching algorithm to optimize cache utilization. Building upon these, a time-efficient algorithm is proposed for the long-term optimization problem considering temporal correlations. Rigorous theoretical analysis reveals that our proposed solution achieves a bounded optimality gap compared to the offline optimal solution. Furthermore, extensive simulation results validate that our method significantly outperforms various baseline schemes.