Cross-Level Dependability Assessment With a Distributed Split Mechanism for Wireless Communication Systems

Abstract

Wireless techniques are playing an increasingly important role in the Industrial Internet of Things. However, the dependability of wireless communication systems is challenged facing high requirements for industrial applications. Dependability assessment models especially supported by machine learning somehow ease the issue, while a cross-level model for distributed wireless devices is lacking. Therefore, this paper proposes a cross-level dependability assessment model with a distributed split mechanism. The model includes device-level assessment sub-models and a system-level assessment sub-model which are trained together but are split and deployed separately by the distributed split mechanism. We implemented this model on a realistic measurement dataset. Comparative experiments indicate that the proposed model not only completes cross-level assessment but also has better performance than past single-level assessment models. Further, experiments on execution time show that the model based on the distributed split mechanism is faster than a centralized mechanism because the split sub-models can process data locally.