Facility Location Strategy for Minimizing Cost in Edge-Based Mobile Crowdsensing

Mobile crowdsensing emerges as a powerful sensing paradigm which utilizes a group of users with mobile devices to perform sensing tasks cooperatively. The traditional mobile crowdsensing architecture is centralized and cloud-based, where the users upload the sensing data directly to the central server. Due to the fact that the amount of sensing data is very large, it will bring much burden to upload and process data collected by mobile users on the central server. In this paper, we propose an edge-based mobile crowdsensing architecture, which introduces a new intermediate layer for data storage, processing and aggregation through deploying mobile edge servers between the traditional cloud server and the user layer. Considering the limited budget, a decision-maker has to decide which server is activated to process each data type at minimum cost, where the cost consists of the facility cost for activating server and processing data, and the service cost measured by the distance of mobile users' movement during the process of uploading data. Since a user may have multiple types of data, this problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. Furthermore, an approximation algorithm is proposed to solve it for minimizing cost, which is proved to have a bound to the optimal solution. We conduct extensive simulations based on the widely-used real-world datasets: roma taxi set, epfl mobility set and geolife trajectory set. The experiment results show that the proposed approximation algorithm outperforms other baseline algorithms and accords with the theoretical bound results.