Minimum-Cost Edge-Server Location Strategy in Mobile Crowdsensing

Abstract

Mobile crowdsensing has become a significant sensing technique which takes advantage of mobile devices to collect information about the surrounding. The traditional cloud-based centralized mobile crowdsensing architecture generates significant traffic on networks and computation burden on the cloud. In this paper, we investigate the edge-based mobile crowdsensing architecture, where a group of mobile edge servers is deployed at network edge as the bridge between the central server and mobile users for data filtering and aggregation. Each user may collect multiple types of data in mobile crowdsensing. To facilitate data aggregation, the same type of data carried by different users is supposed to be uploaded to the same mobile edge server. In this scenario, a problem emerges: which server should be activated for processing each type of data in order to minimize the total cost? The cost consists of the facility cost (activating server and processing data) and the service cost (the users' movement cost for uploading data). Furthermore, the problem is formulated as a variant of the uncapacitated multi-commodity facility location problem. In particular, two situations of the problem are studied in our work: (1) for the situation where each user carries at most two types of data, we propose a relaxation based approximation algorithm, which is proved to have a bound to the optimal solution; (2) for a more generalized situation where each user can carry multiple types of data, we propose a connected multi-agent simulated annealing algorithm. Finally, we conduct extensive simulations based on the widely-used real-world datasets: roma/taxi, epfl/mobility and geolife trajectory. The simulation results show that the proposed algorithms demonstrate their superiority over baseline methods and are consistent with the theoretical analysis.