Integration of multi-constraint trust and mechanical reliability in IoT piezoelectric sensor networks

The rapid growth of the Internet of Things (IoT) has intensified the need for autonomous, energy-efficient, and reliable sensing systems. Piezoelectric sensors that simultaneously harvest vibration energy and sense data offer a sustainable solution, but they introduce challenges such as vibration-induced signal distortion, resonance effects, material fatigue, and intermittent energy availability. These mechanical factors, combined with dynamic environments, complicate trust evaluation and data security in IoT networks. To address this, the study proposes a Multi-Constraint Trust Analysis Model (MCTAM) that integrates data security mechanisms with mechanical awareness. MCTAM incorporates piezoelectric constitutive behavior, vibration–resonance interactions, mechanical health–based trust, mounting configuration effects, adaptive data security, and anomaly detection. Experimental results show that MCTAM accurately distinguishes mechanical disturbances from cyberattacks, achieving a low false-positive rate of about 3.8% and an overall accuracy of approximately 95.8% under random vibration and impact conditions. Resonant nodes demonstrate high energy harvesting efficiency, around 15.2 mJ per cycle, with stable trust performance, while gradual mechanical degradation causes only moderate voltage drops without system failure. Routing guided by MCTAM improves packet delivery to 95.6% and extends network lifetime by 27%. Overall, mechanically aware and energy-conscious trust modeling enables resilient, secure, and long-term IoT deployments for smart infrastructure and applications.