Competing failure analysis considering random propagation time for phased mission systems in IoT

• Published in: Reliability engineering & system safety Vol. 264; p. 111388
• Main Authors: Zhao, Guilin, Dai, Yuanshun, Wang, Yujie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2025
• Subjects: IoT; Phased mission systems; Probabilistic failure competition; Reliability modeling and analysis; WSN; Reliability modeling and analysis; Phased mission systems; Probabilistic failure competition; WSN; IoT
• ISSN: 0951-8320
• DOI: 10.1016/j.ress.2025.111388

The reliability of wireless sensor networks (WSNs) in Internet of Things (IoT) environment is crucial for ensuring seamless communication and operational continuity in applications, e.g., healthcare, aerospace, and home automation. WSNs consist of components such as sensor nodes, relays, and sink devices, all of which contribute to overall connectivity and data processing. However, they face significant reliability challenges in dynamic environments, particularly due to probabilistic functional dependence (PFD) between the relay and its dependent sensor nodes. The PFD behavior creates competition between probabilistic failure isolation and failure propagation in the time domain, posing challenges to IoT reliability modeling. Additionally, WSNs in IoTs often exhibit phased mission behavior that involves multiple, consecutive, non-overlapping phases of operations, and cross-phase dependencies of system components, making IoT reliability modeling more complex. Unlike existing studies that assume instantaneous failure propagation, we propose a novel combinatorial and analytical methodology to model phased mission behaviors and probabilistic competition while addressing random failure propagation times. The proposed method imposes no restrictions on component time-to-failure or propagation time distributions. An example WSN of a smart home system is analyzed step-by-step to demonstrate the proposed method, with numerical validation using the continuous-time Markov chain-based approach.