  The wide spread of mobile devices has enabled a new paradigm of innovation
called Mobile Crowdsourcing (MCS) where the concept is to allow entities, e.g.,
individuals or local authorities, to hire workers to help from the crowd of
connected people, to execute a task or service. Some complex tasks require the
collaboration of multiple workers to ensure its successful completion. In this
context, the task requester needs to hire a group of socially connected and
collaborative workers that, at the same time, have sufficient skills to
accomplish the task. In this paper, we develop two recruitment strategies for
collaborative MCS frameworks in which, virtual teams are formed according to
four different criteria: level of expertise, social relationship strength,
recruitment cost, and recruiter's confidence level. The first proposed strategy
is a platform-based approach which exploits the platform knowledge to form the
team. The second one is a leader-based approach that uses team members'
knowledge about their social network (SN) neighbors to designate a group leader
that recruits its suitable team. Both approaches are modeled as integer linear
programs resulting in optimal team formation. Experimental results show a
performance trade-off between the two virtual team grouping strategies when
varying the members SN edge degree. Compared to the leader-based strategy, the
platform-based strategy recruits a more skilled team but with lower SN
relationships and higher cost.
