The rapid evolution of IoT in smart traffic systems introduces new vulnerabilities, where specifically, transient faults caused by environmental interference and resource constraints. These faults threaten data integrity, system reliability and real-time responsiveness. This paper presents a predictive Fault Tolerance Mechanism (FTM) based on Communication-Induced Checkpointing (CIC) integrated with Long Short-Term Memory (LSTM) networks, tailored for traffic-oriented IoT environments. The proposed Checkpoint at Intermediate Nodes (CIN) CIC-FTM framework places checkpoints at intermediate nodes, based on LSTM-predicted fault likelihood, enabling lightweight and proactive recovery while minimizing rollback overhead. The system architecture designed with IoT edge sensors, fog nodes and a centralized coordination layer to support local fault detection, predictive analytics and consistent checkpoint management. Real-time traffic and communication metadata are used for fault prediction, covering transient faults such as sequence mismatches, checksum failures, presence of null character and out-of-range sensor values. Evaluation across network sizes of 5 to 100 nodes, demonstrates reduced checkpoint frequency by 70-85%, improved fault detection and prediction accuracy by ⁓92% and efficient resource usage. Comparative analysis with existing CIC models confirms significant improvements in recovery time, scalability and adaptability. This hybrid approach combines deep learning, real-time fault detection and selective, proactive checkpointing, offering a robust, energy-efficient and deployment-ready solution for fault tolerant smart traffic infrastructures.
Keywords
Transient Faults, Fault Tolerance Mechanism, Communication- Induced Checkpointing, Long Short-Term Memory Model, Deep Learning, Internet of Things, Traffic Data.
Luanzheng Guo, Dong Li, “MOARD: Modeling Application Resilience to Transient Faults on Data Objects,” License CC BY-NC-ND 4.0, DOI:10.48550/arXiv.2102.06899, 2021.
M. Nikseresht, J. Vankeirsbilck, and J. Boydens, “A Study on Selective Implementation Approaches for Soft Error Detection Using S-SWIFT-R,” Electronics, vol. 11, no. 20, p. 3380, Oct. 2022, doi: 10.3390/electronics11203380.
N. Temene, A. Naoum, C. Sergiou, C. Georgiou, and V. Vassiliou, “A fault tolerant node placement algorithm for WSNs and IoT networks,” Computer Networks, vol. 254, p. 110835, Dec. 2024, doi: 10.1016/j.comnet.2024.110835.
K. R. S. Jammalamadaka, B. Chokara, S. B. Jammalamadaka, and B. K. Duvvuri, “Making IoT Networks Highly Fault-Tolerant Through Power Fault Prediction, Isolation and Composite Networking in the Device Layer,” Journal of Sensor and Actuator Networks, vol. 14, no. 2, p. 24, Feb. 2025, doi: 10.3390/jsan14020024.
M. Norris et al., “IoTRepair: Flexible Fault Handling in Diverse IoT Deployments,” ACM Transactions on Internet of Things, vol. 3, no. 3, pp. 1–33, Jul. 2022, doi: 10.1145/3532194.
A. M. Seba, K. A. Gemeda, and P. J. Ramulu, “Prediction and classification of IoT sensor faults using hybrid deep learning model,” Discover Applied Sciences, vol. 6, no. 1, Jan. 2024, doi: 10.1007/s42452-024-05633-7.
J. Dongarra, T. Herault, and Y. Robert, “Fault Tolerance Techniques for High-Performance Computing,” Fault-Tolerance Techniques for High-Performance Computing, pp. 3–85, 2015, doi: 10.1007/978-3-319-20943-2_1.
K. Kalair and C. Connaughton, “Anomaly detection and classification in traffic flow data from fluctuations in the flow–density relationship,” Transportation Research Part C: Emerging Technologies, vol. 127, p. 103178, Jun. 2021, doi: 10.1016/j.trc.2021.103178.
I. Taheri Sarteshnizi, S. A. Bagloee, M. Sarvi, and N. Nassir, “Traffic Anomaly Detection: Exploiting Temporal Positioning of Flow-Density Samples,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 5, pp. 4166–4180, May 2024, doi: 10.1109/tits.2023.3322695.
M. Bawaneh and V. Simon, “Machine Learning-Based Anomaly Detection in Smart City Traffic: Performance Comparison and Insights,” Proceedings of the 11th International Conference on Vehicle Technology and Intelligent Transport Systems, pp. 309–318, 2025, doi: 10.5220/0013141100003941.
Y. Lin, “Design of urban road fault detection system based on artificial neural network and deep learning,” Frontiers in Neuroscience, vol. 18, Apr. 2024, doi: 10.3389/fnins.2024.1369832.
R. Khan, U. Saeed, and I. Koo, “FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks,” Electronics, vol. 13, no. 24, p. 4907, Dec. 2024, doi: 10.3390/electronics13244907.
A. Sowjanya Lakshmi, C. Vani Priya, and G. Gupta, “Communication Induced Checkpointing based Fault Tolerance Mechanism – A Review and CIAC-FTM Framework in IoT Environment,” 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 1–6, Nov. 2022, doi: 10.1109/icccis56430.2022.10037637.
Kim Do-Hyung and Park Chang-Soon, “A communication-induced checkpointing algorithm using virtual checkpoint on distributed systems,” Proceedings Seventh International Conference on Parallel and Distributed Systems (Cat. No.PR00568), pp. 145–150, doi: 10.1109/icpads.2000.857693.
G. M. D. Vieira, I. C. Garcia, L. E. Buzato, “Systematic Analysis of Index-based Checkpointing Algorithms using Simulation,” SCTF ’01: Proc. of the IX Brazilian Symposium on Fault-Tolerant Computing, pp. 31–42, 2001.
Y. Luo and D. Manivannan, “FINE: A Fully Informed aNd Efficient communication-induced checkpointing protocol for distributed systems,” Journal of Parallel and Distributed Computing, vol. 69, no. 2, pp. 153–167, Feb. 2009, doi: 10.1016/j.jpdc.2008.07.012.
J. Ahn, “Communication-Induced Checkpointing with Message Logging beyond the Piecewise Deterministic (PWD) Model for Distributed Systems,” Electronics, vol. 10, no. 12, p. 1428, Jun. 2021, doi: 10.3390/electronics10121428.
B. H. Sababha and O. A. Rawashdeh, “Evaluation of Communication Induced Checkpointing in Resource Constrained Embedded Systems,” Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B, pp. 39–45, Jan. 2011, doi: 10.1115/detc2011-48634.
A. C. Simón, S. E. P. Hernandez, J. R. P. Cruz, R. B. Halima, and H. H. Kacem, “Self-healing in autonomic distributed systems based on delayed communication-induced checkpointing,” International Journal of Autonomous and Adaptive Communications Systems, vol. 9, no. 3/4, p. 183, 2016, doi: 10.1504/ijaacs.2016.079621.
N. Malhotra and M. Bala, “Fault-Tolerant Communication Induced Checkpointing and Recovery Protocol Using IoT,” Intelligent Automation & Soft Computing, vol. 30, no. 3, pp. 945–960, 2021, doi: 10.32604/iasc.2021.019082.
J.-M. Hélary, A. Mostefaoui, R. H. B. Netzer, and M. Raynal, “Communication-based prevention of useless checkpoints in distributed computations,” Distributed Computing, vol. 13, no. 1, pp. 29–43, Jan. 2000, doi: 10.1007/s004460050003.
F. Quaglia, R. Baldoni, and B. Ciciani, “On the No-Z-Cycle Property in Distributed Executions,” Journal of Computer and System Sciences, vol. 61, no. 3, pp. 400–427, Dec. 2000, doi: 10.1006/jcss.2000.1720.
Tanish Baranwal, Srihari Varada, Santanu Das, Mohammad R. Haider, “Fault-Tolerant IoT System Using Software-Based “Digital Twin”,” arxiv, 2025.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Sowjanya Lakshmi A and Vanipriya C H;
Methodology: Sowjanya Lakshmi A;
Software: Vanipriya C H;
Data Curation: Sowjanya Lakshmi A;
Writing- Original Draft Preparation: Sowjanya Lakshmi A and Vanipriya C H;
Visualization: Sowjanya Lakshmi A;
Investigation: Vanipriya C H;
Supervision: Sowjanya Lakshmi A;
Validation: Vanipriya C H;
Writing- Reviewing and Editing: Sowjanya Lakshmi A and Vanipriya C H; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr.Vanipriya C H for this research completion and support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
Data sharing is not applicable to this article as no new data were created or analysed in this study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Sowjanya Lakshmi A
Sir M. Visvesvaraya Institute of Technology, Bengaluru, Karnataka, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Sowjanya Lakshmi A and Vanipriya C H, “Fault Tolerance Mechanism for Transient Faults in IoT Based Traffic Data Transaction”, Journal of Machine and Computing, vol.5, no.4, pp. 2495-2503, October 2025, doi: 10.53759/7669/jmc202505191.
