Service discovery in the Social Internet of Things (SIoT) must be both efficient and trustworthy. Dense device graphs and heterogeneous link reliability make naïve traversal ineffective and risk-prone. We address this by constructing an Individual’s Small-World SIoT (ISWSIoT) search space via a trust threshold \(\alpha\) and proposing a class- and trust-based informed search algorithm that combines deterministic neighbor ranking, class-aware (friendship) selection, Top-K exploration, a hop bound H, and a selective fallback mechanism. This dual control ( \(\alpha\) and K) is designed to balance reliability and exploration while bounding search depth. We posit and evaluate four hypotheses: (H1) setting \(\alpha \ge 0.5\) improves discovery relative to permissive thresholds; (H2) \(\alpha\) -filtering with Top-K reduces exploration cost (visited nodes) while preserving short paths and low latency; (H3) increasing K and H boosts success with only moderate latency overhead; and (H4) fallback restores progress under strict trust filtering with limited cost. Experiments on five real-world graphs perform an SIoT-style \(\alpha\) -profiling of success, hops, latency, visited nodes, and LCC ratio. The results show that discovery improves from permissive to moderate \(\alpha\) and stabilizes near \(\alpha \ge 0.5\) ; visited nodes decrease as \(\alpha\) rises, while hops remain modest and latency low; larger K and H increase success with moderate latency increases; and fallback recovers discovery when strict \(\alpha\) yields few eligible neighbors. Runtime measurements indicate that the proposed method is competitive—often fastest on larger graphs—relative to standard graph searches. Overall, the findings validate the hypotheses and demonstrate that a trust- and class-aware, dual-controlled discovery algorithm provides effective, efficient, and robust service discovery in SIoT.
