Collect Fresh Data@Edge: with Freshness-Sensitive Server Placement & Traffic Management Strategies

Efficient data collection systems play a crucial role in enabling real-time decision-making in AIoT applications. However, traditional cloud-edge systems encounter challenges such as increased computational load and network transmission, leading to higher costs and latency. To address these issues, Multi-Access Edge Computing (MEC) has emerged as a solution by decentralizing computation and storage, reducing reliance on cloud data transmission. Nonetheless, in scenarios adopting the MEC paradigm, data freshness becomes imperative, necessitating further optimization of data collection. This paper aims to enhance data collection effectiveness from two perspectives: designing an appropriate edge placement strategy before deploying the data collection system, and dynamically adjusting the traffic scheduling strategy to maximize the age of information (AoI) metric while minimizing costs. Experimental results demonstrate that our proposed approach enhances system stability and effectiveness in AIoT applications, outperforming other baseline methods.