International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 10 | Year 2026 | Article Id. IJETT-V74I10P112 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I10P112

Two-Stage Fault Management Framework for IoT-based Wireless Sensor Network through Resilient and Energy-Efficient Approach


Suman Murahari, Rekha Redamalla

Received Revised Accepted Published
03 Apr 2026 08 Sep 2026 18 Sep 2026 27 Oct 2026

Citation :

Suman Murahari, Rekha Redamalla, "Two-Stage Fault Management Framework for IoT-based Wireless Sensor Network through Resilient and Energy-Efficient Approach," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 10, pp. 157-176, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I10P112

Abstract

The Internet of Things (IoT) is built on the foundation of Wireless Sensor Networks (WSNs), which is the technology that provides a smooth connection and real-time data transmission in smart cities and industrial settings. Nevertheless, IoT-based WSNs are prone to several challenges due to hardware, software, and time-related faults, which affect stability and reliability. In the research, a two-step fault management system is proposed to improve fault detection, classification, and resilience in IoT-based WSNs. At the initial step, the identification of faulty nodes in heterogeneous environments is done with a Siamese-Residual Attention Convolutional Neural Network - Bidirectional Long Short-Term Memory (SRA-CNN-BiLSTM) model. Next, the second step used a Self-Learning Residual Shrinkage Network (SLRSN) to categorize the types of faults effectively. The optimization of the model parameters is performed using a new Clan Cooperative Crested Porcupine-based Optimizer (3CPO) that guarantees high accuracy and convergence rate. Python simulations indicate a 0.98% increase in the accuracy of fault detection and significant energy savings. The framework is therefore a reliable and scalable system of fault management of IoT-enabled WSNs.

Keywords

Internet of Things, Wireless Sensor Network, Fault management, Fault detection, Fault classification, Resilient networks, Energy-efficient recovery, Fault tolerance, Energy-efficient approach.

References

  1. Daudi Mnyanghwalo, and Shamte Kawambwa, “Deployment of an IoT based Sensor Node for Faults Detection and Classification in Electrical Secondary Distribution Networks,” Cogent Engineering, vol. 11, no. 1, pp. 1-17, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  2. Ahmad Saeedi, Marjan Kuchaki Rafsanjani, and Samaneh Yazdani, “Energy Efficient Clustering in IoT-based Wireless Sensor Networks using Binary Whale Optimization Algorithm and Fuzzy Inference System,” The Journal of Supercomputing, vol. 81, no. 1, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  3. Ehsan Heidari, “A Novel Energy-Aware Method for Clustering and Routing in IoT based on Whale Optimization Algorithm and Harris Hawks Optimization,” Computing, vol. 106, no. 3, pp. 1013-1045, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  4. Tabassum Kanwal et al., “Energy-Efficient Internet of Things-based Wireless Sensor Network for Autonomous Data Validation for Environmental Monitoring,” Computer Systems Science and Engineering, vol. 49, no. 1, pp. 185-212, 2025. 
    [
    CrossRef] [Publisher Link]
  5. Walid Osamy, “LBAS: Load-Balancing Aware Clustering Scheme for IoT-based Heterogeneous Wireless Sensor Networks,” IEEE Sensors Journal, vol. 24, no. 9, pp. 15472-15490, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link] 
  6. Preeti Sharma et al., “NHARSO-IWSN: A Novel Hybridized Adaptive-Network-based Fuzzy Inference System with Reptile Search Optimization Algorithm-based Routing Protocol for Internet of Things-Enabled Wireless Sensor Networks,” IEEE Transactions on Consumer Electronics, vol. 70, no. 3, pp. 6293-6302, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  7. E. Silambarasan et al., “Energy Efficient Heterogeneous Clustering Scheme using Improved Golden Eagle Optimization Algorithm for WSN-based IoT,” International Journal of Information Technology, vol. 17, no. 3, pp. 1753-1760, 2024. 
    [
    CrossRef] [Google Scholar] [Publisher Link]
  8. G. Mahalakshmi, S. Ramalingam, and A. Manikandan, “An Energy Efficient Data Fault Prediction based Clustering and Routing Protocol using Hybrid Asso with MERNN in Wireless Sensor Network,” Telecommunication Systems, vol. 86, no. 1, pp. 61-82, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  9. Nagarajan B, and Santhosh Kumar SVN, “An Intelligent Sailfish Optimization based Fault Diagnosis and Classification in Wireless Sensor Networks,” Peer-to-Peer Networking and Applications, vol. 18, no. 1, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  10. Abdennabi Morchid et al., “IoT-based Smart Irrigation Management System to Enhance Agricultural Water Security using Embedded Systems, Telemetry Data, and Cloud Computing,” Results in Engineering, vol. 23, pp. 1-16, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  11. Šarūnas Kilius et al., “Time Delay Characterization in Wireless Sensor Networks for Distributed Measurement Applications,” Journal of Sensor and Actuator Networks, vol. 13, no. 3, pp. 1-16, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  12. Ashutosh Kumar Rao, Kapil Kumar Nagwanshi, and Manoj Kumar Shukla, “An Optimized Secure Cluster-based Routing Protocol for IoT-based WSN Structures in Smart Agriculture with blockchain-based Integrity Checking,” Peer-to-Peer Networking and Applications, vol. 17, no. 5, pp. 3159-3181, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  13. Garima Thakur et al., “A Privacy-Preserving Three-Factor Authentication System for IoT-Enabled Wireless Sensor Networks,” Journal of Systems Architecture, vol. 154, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  14. B. Nagarajan, and Santhosh Kumar SVN, “An Intelligent Fault Node Diagnosis and Classification Approach using XGBoost with Whale Optimization in Wireless Sensor Networks,” Peer-to-Peer Networking and Applications, vol. 18, no. 1, pp. 1-20, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  15. Gummarekula Sattibabu, Nagarajan Ganesan, and R. Senthil Kumaran, “IoT-Enabled Wireless Sensor Networks Optimization based on Federated Reinforcement Learning for Enhanced Performance,” Peer-to-Peer Networking and Applications, vol. 18, no. 2, 2025.
    [
    CrossRef] [Google Scholar] [Publisher Link] 
  16. Gianluca Tabella et al., “Bayesian Fault Detection and Localization Through Wireless Sensor Networks in Industrial Plants,” IEEE Internet of Things Journal, vol. 11, no. 8, pp. 13231-13246, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  17. S. Chakaravarthi et al., “IoT-based Trusted Wireless Communication Framework by Machine Learning Approach,” Measurement: Sensors, vol. 34, pp. 1-8, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link] 
  18. Yuanlong Li et al., “Deep-Reinforcement-Learning-based Wireless IoT Device Identification using Channel State Information,” Symmetry, vol. 15, no. 7, pp. 1-21, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]  
  19. I. Surenther, K.P. Sridhar, and Michelraj Kingston Roberts, “Enhancing Data Transmission Efficiency in Wireless Sensor Networks through Machine Learning-Enabled Energy Optimization: A Grouping Model Approach,” Ain Shams Engineering Journal, vol. 15, no. 4, pp. 1-14, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  20. S. Ramalingam et al., “Performance Enhancement of Efficient Clustering and Routing Protocol for Wireless Sensor Networks using Improved Elephant Herd Optimization Algorithm,” Wireless Networks, vol. 30, no. 3, pp. 1773-1789, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  21. Rehan Khan, Umer Saeed, and Insoo Koo, “FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks,” Electronics, vol. 13, no. 24, pp. 1-20, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link] 
  22. Ramineni Padmasree, and Aravalli Sainath Chaithanya, “Fault Detection in Single-Hop and Multi-Hop Wireless Sensor Networks using a Deep Learning Algorithm,” International Journal of Informatics and Communication Technology (IJ-ICT), vol. 13, no. 3, pp. 453-461, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link] 
  23. Daniel Godfrey et al., “An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks,” Sensors, vol. 23, no. 20, pp. 1-22, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  24. Divya Gupta et al., “EEDC: An Energy Efficient Data Communication Scheme based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications,” Sensors, vol. 23, no. 21, pp. 1-14, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  25. Arash Heidari et al., “Assessment of Reliability and Availability of Wireless Sensor Networks in Industrial Applications by Considering Permanent Faults,” Concurrency and Computation: Practice and Experience, vol. 36, no. 27, pp. 1-21, 2024. 
    [
    CrossRef] [Google Scholar] [Publisher Link]
  26. Liyazhou Hu et al., “Security Enhancement for Deep Reinforcement Learning-based Strategy in Energy-Efficient Wireless Sensor Networks,” Sensors, vol. 24, no. 6, pp. 1-14, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  27. Aysha Munir Sheikh, and Sunil Joshi, “Improved Smart Energy-based Routing Approach for IoT Networks in Wireless Sensor Nodes,” Journal of Engineering and Applied Science, vol. 71, no. 1, pp. 1-26, 2024. 
    [
    CrossRef] [Google Scholar] [Publisher Link]
  28. Prasanth Aruchamy, Lavina Balraj, and K.K. Devi Sowndarya, “An Energy-Aware Link Fault Detection and Recovery Scheme for QoS Enhancement in Internet of Things-Enabled Wireless Sensor Network,” Computers and Electrical Engineering, vol. 123, 2025.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  29. Nassima Jihani, Mohammed Nabil Kabbaj, and Mohammed Benbrahim, “Kalman Filter based Sensor Fault Detection in Wireless Sensor Network for Smart Irrigation,” Results in Engineering, vol. 20, pp. 1-6, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  30. Roopali Dogra, Shalli Rani, and Gabriele Gianini, “REERP: A Region-based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks,” Energies, vol. 16, no. 17, pp. 1-16, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  31. Gagandeep Kaur, and Prasenjit Chanak, “An Intelligent Fault Tolerant Data Routing Scheme for Wireless Sensor Network-Assisted Industrial Internet of Things,” IEEE Transactions on Industrial Informatics, vol. 19, no. 4, pp. 5543-5553, 2023.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  32. Zhiyi Xin et al., “SiamRAAN: Siamese Residual Attentional Aggregation Network for Visual Object Tracking,” Neural Processing Letters, vol. 56, no. 2, pp. 1-22, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  33. Wei Dai et al., “Network Intrusion Detection Method based on CNN-BILSTM-Attention Model,” IEEE Access, vol. 12, pp. 53099-53111, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  34. Xingwang Lv et al., “Self-Learning Guided Residual Shrinkage Network for Intelligent Fault Diagnosis of Planetary Gearbox,” Engineering Applications of Artificial Intelligence, vol. 139, 2025.
    [
    CrossRef] [Google Scholar] [Publisher Link]
  35. Haijun Liu et al., “Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO,” Mathematics, vol. 12, no. 19, pp. 1-41, 2024.
    [
    CrossRef] [Google Scholar] [Publisher Link]