  With the rich set of embedded sensors installed in smartphones and the large
number of mobile users, we witness the emergence of many innovative commercial
mobile crowdsensing applications that combine the power of mobile technology
with crowdsourcing to deliver time-sensitive and location-dependent information
to their customers. Motivated by these real-world applications, we consider the
task selection problem for heterogeneous users with different initial
locations, movement costs, movement speeds, and reputation levels. Computing
the social surplus maximization task allocation turns out to be an NP-hard
problem. Hence we focus on the distributed case, and propose an asynchronous
and distributed task selection (ADTS) algorithm to help the users plan their
task selections on their own. We prove the convergence of the algorithm, and
further characterize the computation time for users' updates in the algorithm.
Simulation results suggest that the ADTS scheme achieves the highest Jain's
fairness index and coverage comparing with several benchmark algorithms, while
yielding similar user payoff to a greedy centralized benchmark. Finally, we
illustrate how mobile users coordinate under the ADTS scheme based on some
practical movement time data derived from Google Maps.
