Stochastic reliability modeling of an integrated cloud–fog–IoMT healthcare architecture with VANETs

The rapid advancement of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous remote monitoring and timely medical intervention. Although cloud computing, fog computing, and vehicular ad hoc networks (VANETs) have individually supported smart healthcare applications, the reliability assessment of an integrated IoMT–VANET–Fog–Cloud infrastructure remains largely unexplored. Motivated by the need for dependable healthcare services during emergencies and disaster scenarios, this study proposes an integrated cloud–fog-based smart healthcare architecture incorporating IoMT devices and VANET-assisted communication for continuous patient monitoring and real-time healthcare delivery. A stochastic analytical framework based on Reliability Block Diagrams (RBDs) and continuous-time Markov chains is developed to evaluate the reliability of the proposed architecture. The framework models the failure and repair behavior of heterogeneous hardware components and derives analytical expressions for overall system reliability. Extensive numerical investigations under varying operating conditions demonstrate the effectiveness of the proposed framework. A comparative reliability analysis with cloud-only and cloud–fog architectures quantifies the benefits of integrating fog computing and VANET technologies. The proposed model achieves a 20% improvement in reliability over the cloud-only architecture and a 23% improvement over the IoMT-enabled cloud–fog architecture. The analytical results are validated through MATLAB-based discrete-event simulation, showing close agreement with analytical results. The findings demonstrate that integrating fog computing and VANET-assisted communication significantly enhances system reliability under the assumed conditions. The proposed stochastic reliability framework provides a systematic tool for evaluating the dependability of next-generation smart healthcare systems and offers insights for designing reliable healthcare infrastructures.