Abstract
Mobile crowdsensing (MCS) is a novel data-collection paradigm in the internet of things. Social welfare is an important factor in the task allocation because it integrates the interests of all parties involved in MCS and represents societal satisfaction. The ultimate goal of task allocation is to maximize social welfare as much as possible. Existing social welfare optimization research does not consider the moral and psychological characteristics of people in the real world. In this study, the real-world situation is considered. A task allocation strategy, which includes two stages, is formulated for task allocation. A generalized shortest path algorithm and an optimal pricing algorithm are proposed for each stage. To evaluate the proposed algorithms, extensive simulation experiments are conducted on two real-world datasets. The experimental results demonstrate that the proposed algorithms produce the desired effects, and the proposed strategy significantly increases social welfare by 19% compared to another method.
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