User satisfaction aware maximum utility task assignment in mobile crowdsensing

摘要

In mobile crowdsensing systems (MCS) efficient task assignment is the key problem that defines the performance of the system. The current state-of-the-art solutions consider the problem from system's point of view and target an assignment that optimizes the overall system utility such as minimizing the cost of sensing or maximizing the collected data quality. However, users (i.e., task requesters and task performers or workers) may have individual preferences, hence the resulting assignment may not satisfy the users and can discourage them from participation in the future. Stable matching based solutions can help achieving satisfactory assignments for the users, but they may degrade the system utility especially when the number of eligible task performers for each task is limited, hence may not be desired for the MCS platform. To address this problem, in this paper, we study the task assignment problem that aims to maximize the system utility and user satisfaction simultaneously as much as possible. As the problem is NP-complete, we first solve the problem using Integer Linear Programming (ILP) and provide two different heuristic based polynomial solutions. We perform extensive simulations using real dataset and show that the proposed solutions provide close to optimal results, complementing each other at different scenarios.