Stable Worker–Task Assignment in Mobile Crowdsensing Applications

Mobile crowdsensing (MCS) is an emerging form of crowdsourcing, which facilitates the sensing data collection with the help of mobile participants (workers). A central problem in MCS is the assignment of sensing tasks to workers. Existing works in the field mostly seek a system-level optimization of task assignments (e.g., maximize the number of completed tasks or minimize the total distance traveled by workers) without considering individual preferences of task requesters and workers. However, users may be reluctant to participate in MCS campaigns that disregard their preferences as this can cause the majority of users to find their assignments dissatisfying and consequently to cease participating in the campaign, putting the long-term success of the campaign in jeopardy. Moreover, dissatisfying task assignments may hinder the effective functioning of the campaign, as unhappy users may refuse to fulfill the assignments made by the platform. Thus, it is critically important to consider user preferences during the task assignment process of MCS campaigns. In this book chapter, we review the recent studies in the mobile crowdsensing domain that seek preference-aware assignments between the workers and tasks in the system. While these studies build their designs on the Stable Matching Theory, existing methods to find stable preference-aware assignments cannot be applied directly due to the type of the MCS scenario (e.g., participatory, opportunistic, or hybrid) as well as the constraints (e.g., budget of task requesters, Quality of Service (QoS) required from completed tasks, capacity of workers (i.e., the maximum number of tasks they can perform), and rewards given to each task) given in MCS setting and the assignment types allowed between workers and tasks such as many to one and many to many with additive or non-additive utility of workers. We highlight these differences in each MCS scenario studied together with how the stability is defined. We then discuss if stable solutions are possible in each and provide a brief summary of corresponding solution approaches. Finally, we provide a set of open problems that need to be studied to find stable work–task assignments.