  Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing
tasks, traditionally performed by employees or contractors, to a large group of
smart-phone users by means of an open call. With the increasing complexity of
the crowdsourcing applications, requesters find it essential to harness the
power of collaboration among the workers by forming teams of skilled workers
satisfying their complex tasks' requirements. This type of MCS is called
Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on
the aspect of team skills maximization. Other team formation studies on social
networks (SNs) have only focused on social relationship maximization. In this
paper, we present a hybrid approach where requesters are able to hire a team
that, not only has the required expertise, but also is socially connected and
can accomplish tasks collaboratively. Because team formation in CMCS is proven
to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge
about their SN neighbors and asks a designated leader to recruit a suitable
team. The proposed algorithm is inspired from the optimal stopping strategies
and uses the odds-algorithm to compute its output. Experimental results show
that, compared to the benchmark exponential optimal solution, the proposed
approach reduces computation time and produces reasonable performance results.
