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

Automated Service Discovery for Social Internet-of-Things Systems