Localization in the internet of things based on game theory with applications in the healthcare

Abstract

Accurate indoor localization is critical for healthcare Internet of Things (IoT) applications such as patient tracking and asset management. However, maintaining localization accuracy under node mobility, signal attenuation, and faulty or adversarial behaviors remains challenging. Existing schemes either assume benign environments or employ rigid filters that permanently exclude noisy nodes, causing unacceptable losses in patient monitoring coverage. To address this gap, we propose a trust-aware localization framework integrating repeated-game cooperation control, reputation-aware guide selection, and multi-triplet fusion-based trilateration. By modeling node interactions as a repeated game executed at the cluster head level, the framework suppresses unreliable nodes without imposing a high computational burden on resource-constrained devices. It employs a conservative trust mechanism that avoids aggressive, coverage-destroying blacklists. The method is evaluated against three baselines (HOFTELM, Dual Authentication GT, Reward-Punishment GT) within a shared hospital simulation environment. Empirical results demonstrate significant improvements across all evaluated metrics: Mean Squared Error of 5.43 m2 and Mean Localization Error of 1.56 m, representing error reductions of 97.5% and 82.2–87.5%, respectively. The method yields 80.24% localization accuracy (an 173.9% improvement) and a 98.33% success rate, while reducing communication overhead by at least 65.0%. Crucially, it preserves network integrity with zero false-positive blacklistings and only 1.67% coverage loss, proving its robustness, scalability, and deployability for continuous, safety–critical indoor healthcare monitoring.