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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.