Preference and Mobility-Aware Task Assignment in Participatory Sensing

Participatory Sensing is a new paradigm of mobile sensing where users are actively involved in leveraging the power of their smart devices to collect and share information. Motivated by its potential applications, we tackle in this paper the task assignment problem for a requester encountering a crowd of participants while considering their mobility model and sensing preferences. We aim to minimize the overall processing time of sensing tasks. Hence, we introduce first the Mobility-Aware Task Assignment scheme in both oFfline (MATAF) and oNline (MATAN) models where requesters investigate the participants' arrival model in different compounds of the sensing region. Further, we enhance such schemes by jointly taking into account participants' mobility and sensing preferences. We advocate then two other task assignment models, P-MATAF (offline) and P-MATAN (online). All proposed algorithms adopt a greedy-based selection strategy and address the minimization of the average makespan of all sensing tasks. We conduct extensive performance evaluation based on real traces while varying the number of tasks and associated workloads. Results proved that our proposed schemes have achieved lower average makespan and higher number of delegated tasks.