  Spatial Mobile Crowdsourcing (SMCS) can be leveraged by exploiting the
capabilities of the Social Internet-of-Things (SIoT) to execute spatial tasks.
Typically, in SMCS, a task requester aims to recruit a subset of IoT devices
and commission them to travel to the task location. However, because of the
exponential increase of IoT networks and their diversified devices (e.g.,
multiple brands, different communication channels, etc.), recruiting the
appropriate devices/workers is becoming a challenging task. To this end, in
this paper, we develop a recruitment process for SMCS platforms using automated
SIoT service discovery to select trustworthy workers satisfying the requester
requirements. The method we purpose includes mainly two stages: 1) a worker
filtering stage, aiming at reducing the workers' search space to a subset of
potential trustworthy candidates using the Louvain community detection
algorithm (CD) applied to SIoT relation graphs. Next, 2) a selection process
stage that uses an Integer Linear Program (ILP) to determine the final set of
selected devices/workers. The ILP maximizes a worker efficiency metric
incorporating the skills/specs level, recruitment cost, and trustworthiness
level of the recruited IoT devices. Selected experiments analyze the
performance of the proposed CD-ILP algorithm using a real-world dataset and
show its superiority in providing an effective recruitment strategy compared to
an existing stochastic algorithm.
