SIoT-enabled opportunistic sensing under partial observability: evaluating recruitment policies for human digital twin context estimation

Human Digital Twins (HDTs) rely on reliable context estimation to support personalized services, adaptive decision-making, and context-aware interaction, yet local ego sensing can be noisy, intermittent, or ambiguous. This study investigates whether Social Internet of Things (SIoT)-enabled opportunistic recruitment of external sources can improve HDT context estimation under partial observability. A controlled synthetic simulator is developed in which the SIoT layer is represented as a typed object graph supporting graph-constrained candidate discovery, bounded relationship-guided discovery, and cost-aware recruitment. The evaluation compares ego-only sensing, a high-coverage opportunistic-all reference, SIoT-aware bounded recruitment, and privacy-aware SIoT recruitment across nominal conditions, degraded ego sensing, ambiguous local context, and noisy/untrusted external sources. Performance is assessed with strict joint overall context accuracy, mean variable accuracy, per-variable Macro-F1, operational cost, effective recovery cost, source-cap-matched baselines, discovery-mode comparisons, and graph-size scalability. The results show that opportunistic-all recruitment gives the highest raw OCA because it recruits many sources, whereas bounded SIoT-aware policies provide lower-cost operating points and preserve nearly the same accuracy as exhaustive SIoT discovery in the discovery-mode comparison. The findings are therefore framed as accuracy-cost-privacy-scalability trade-offs in a synthetic, reproducible testbed rather than as deployment-level claims of universal policy superiority.