Abdulhamid, AlhassanKabir, SohagGhafir, IbrahimLei, Ci2024-01-182024-02-052024-01-182024-02-052024-01Abdulhamid A, Kabir S, Ghafir I et al (2024) Reliability Assessment of IoT-enabled Systems using Fault Trees and Bayesian Networks. 5th International Conference on Advances in Distributed Computing and Machine Learning (ICADCML). 5-6 Jan, VIT-AP University, India in collaboration with College of Engineering, San Diego State University, USA.RMSID:212772124http://hdl.handle.net/10454/19795NoThe Internet of Things (IoT) has brought significant advancements in various domains, providing innovative and efficient solutions. However, ensuring the safe design and operation of IoT devices is crucial, as the consequences of component failure can range from system downtime to dangerous operating states. Several methods have been proposed to evaluate the failure behaviours of IoT-based systems, including Fault Tree Analysis (FTA), a methodology adopted from other safetycritical domains. This study integrated FTA and Bayesian Network (BN) models to assess IoT system reliability based on components’ reliability data and other statistical information. The integrated model achieved efficient predictive failure analysis, considering combinations of 12 basic events to quantify the overall system’s reliability. The model also enables criticality analysis, ranking basic events based on their contributions to system failure and providing a guide for design modification in order to enhance IoT safety. By comparing failure data in FTA and criticality indices obtained using the BN model, the proposed integration offers a probabilistic estimation of IoT system failure and a viable safety guide for designing IoT systems.enInternet of ThingsSystem safetyReliability analysisFault treeBayesian networkRisk assessmentConference paperUnspecified2024-01-18