Event-driven edge processing model for industrial IoT systems

Abstract

Efficient and reliable cold chain management requires rapid detection and interpretation of temperature deviations, equipment anomalies, and operational disturbances. Traditional IoT-based monitoring architectures rely heavily on cloud connectivity, resulting in latency, reduced autonomy, and limited resilience in time-critical situations. This paper proposes an event-driven processing logic integrated into an edge computing architecture for intelligent cold chain logistics platforms.
The model introduces a structured four-tuple event representation, a deviation descriptor for quantifying abnormal behaviour, an adaptive severity classification function, and a rule-based edge decision mechanism. Formulas (1)–(4) formalize the transformation of raw sensor measurements into contextualized events, analytically evaluated deviations, severity levels, and real-time edge-level actions.
Modelling results using realistic temperature trajectories—stable control, slow thermal drift, and door-abuse patterns—demonstrate the model’s ability to distinguish benign fluctuations from harmful deviations, escalate severity dynamically, and operate autonomously during connectivity interruptions. The proposed approach significantly enhances responsiveness, resilience, and operational reliability of cold chain monitoring systems.