User satisfaction aware maximum utility task assignment in mobile crowdsensing
User satisfaction aware maximum utility task assignment in mobile crowdsensing
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DOI：
10.1016/j.comnet.2020.107156
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
F. Yucel;E. Bulut
中科院分区：
文献类型：
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作者：
F. Yucel;E. Bulut
在移动的人群感知系统中，有效的任务分配是决定系统性能的关键问题。当前最先进的解决方案从系统的角度考虑问题，并以优化整个系统效用的分配为目标，例如最小化感测成本或最大化收集的数据质量。然而，用户（即，任务请求者和任务执行者或工作者）可能具有个人偏好，因此所得到的分配可能不满足用户，并且可能使他们不愿在将来参与。基于稳定匹配的解决方案可以帮助实现用户满意的分配，但是它们可能降低系统效用，特别是当每个任务的合格任务执行者的数量有限时，因此可能不期望MCS平台。为了解决这个问题，在本文中，我们研究的任务分配问题，旨在最大限度地提高系统的效用和用户满意度尽可能同时。由于该问题是NP完全的，我们首先解决了这个问题，使用非线性规划（ILP），并提供了两个不同的启发式基于多项式的解决方案。我们使用真实的数据集进行了大量的模拟，并表明所提出的解决方案提供了接近最佳的结果，在不同的情况下相互补充。
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.