  Task allocation or participant selection is a key issue in Mobile Crowd
Sensing (MCS). While previous participant selection approaches mainly focus on
selecting a proper subset of users for a single MCS task, multi-task-oriented
participant selection is essential and useful for the efficiency of large-scale
MCS platforms. This paper proposes TaskMe, a participant selection framework
for multi-task MCS environments. In particular, two typical multi-task
allocation situations with bi-objective optimization goals are studied: (1) For
FPMT (few participants, more tasks), each participant is required to complete
multiple tasks and the optimization goal is to maximize the total number of
accomplished tasks while minimizing the total movement distance. (2) For MPFT
(more participants, few tasks), each participant is selected to perform one
task based on pre-registered working areas in view of privacy, and the
optimization objective is to minimize total incentive payments while minimizing
the total traveling distance. Two optimal algorithms based on the Minimum Cost
Maximum Flow theory are proposed for FPMT, and two algorithms based on the
multi-objective optimization theory are proposed for MPFT. Experiments verify
that the proposed algorithms outperform baselines based on a large-scale
real-word dataset under different experiment settings (the number of tasks,
various task distributions, etc.).
