  In this paper, we propose to design an automated service discovery process to
allow mobile crowdsourcing task requesters select a small set of devices out of
a large-scale Internet-of-things (IoT) network to execute their tasks. To this
end, we proceed by dividing the large-scale IoT network into several virtual
communities whose members share strong social IoT relations. Two community
detection algorithms, namely Louvain and order statistics local method (OSLOM)
algorithms, are investigated and applied to a real-world IoT dataset to form
non-overlapping and overlapping IoT devices groups. Afterwards, a natural
language process (NLP)-based approach is executed to handle crowdsourcing
textual requests and accordingly find the list of IoT devices capable of
effectively accomplishing the tasks. This is performed by matching the NLP
outputs, e.g., type of application, location, required trustworthiness level,
with the different detected communities. The proposed approach effectively
helps in automating and reducing the service discovery procedure and
recruitment process for mobile crowdsourcing applications.
