Design and Evaluation of an Adaptive Fault Detection and Tolerance Mechanism for Underwater Wireless Sensor Networks
DOI:
https://doi.org/10.22399/ijcesen.4056Keywords:
Underwater Wireless Sensor Networks (UWSNs), Fault Detection, Fault Tolerance, Hybrid Monitoring, Energy-Aware ProtocolsAbstract
Underwater Wireless Sensor Networks (UWSNs) are a pivotal technology for ocean monitoring, underwater surveillance, and environmental sensing. However, these networks are vulnerable to numerous faults caused by adverse aquatic environments, scarce node resources, and unreliable acoustic communication. This paper presents an adaptive fault detection and tolerance mechanism specially designed for UWSNs. The proposed system integrates a hybrid fault detection approach, combining explicit and implicit approaches with a light-weight tolerance strategy leveraging multi-path rerouting and load-aware decision-making. A dynamic thresholding mechanism is used to enhance fault detection sensitivity and accuracy while ensuring energy efficiency. The mechanism is implemented and evaluated using MATLAB simulations with different fault densities and node behaviors. The findings show significant improvement in detection accuracy, less false alarm, lower latency, and better network lifetime when compared with traditional fault management schemes. This research is a step toward constructing fault-tolerant UWSNs that can maintain operations under the rough and uncertain nature of underwater environments.
References
[1] Wang, P., Zheng, J., & Li, C. (2007). An agreement-based fault detection mechanism for underwater sensor networks. IEEE International Conference on Communications, 1195–1200
[2] Ding, M., & Chen, D. (2005). Localized fault-tolerant event boundary detection in sensor networks. Proceedings IEEE INFOCOM 2005, 902–913.
[3] Jiang, P. (2009). A new method for node fault detection in wireless sensor networks. Sensors, 9(2), 1282–1294.
[4] Guo, Z., Wang, B., Xie, P., Zeng, W., & Cui, J. (2009). Efficient error recovery with network coding in underwater sensor networks. Ad Hoc Networks, 7(4), 791–802.
[5] Goyal, N., Dave, M., & Verma, A. K. (2017). A novel fault detection and recovery technique for cluster-based underwater wireless sensor networks. International Journal of Communication Systems, 31(4), e3485.
[6] Das, A. P., & Thampi, S. M. (2017). Fault-resilient localization for underwater sensor networks. Ad Hoc Networks, 55, 132–142.
[7] Asim, M., Mokhtar, H. M., & Merabti, M. (2008). A fault management architecture for wireless sensor network. 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, 779–785.
[8] Yuvaraja, M., & Sabrigiriraj, M. (2015). Fault detection and recovery scheme for routing and lifetime enhancement in WSN. Wireless Networks, 23(1), 267–277.
[9] Zidi, S., Moulahi, T., & Alaya, B. (2018). Fault detection in wireless sensor networks through SVM classifier. IEEE Sensors Journal, 18(1), 340–347.
[10] Wang, Y., Li, F., & Chen, Z. (2021). Energy-aware fault detection in underwater sensor networks. Sensors, 21(2), 478–490.
[11] El-Tantawy, A., & El-Mahdy, H. (2020). A hybrid trust-based fault detection scheme for UWSNs. Ad Hoc Networks, 102, 102127.
[12] Raza, S., Khalid, S., & Nawaz, M. (2022). Federated learning for anomaly detection in distributed WSNs. IEEE Access, 10, 59813–59825.
[13] Zhang, H., Liu, T., & Ma, K. (2023). Adaptive hybrid thresholding for underwater anomaly detection. Wireless Networks, 29, 2113–2125.
[14] Rehman, A., Ahmed, T., & Khan, M. (2021). Cluster-based routing with fault tolerance for UWSNs. Journal of Network and Computer Applications, 178, 102983.
[15] Ahmad, Z., Usman, M., & Qadir, R. (2022). Reinforcement learning-based recovery in underwater sensor networks. Sensors, 22(5), 2050.
[16] Nadeem, Q., Farooq, M., & Iqbal, S. (2021). Fault-tolerant routing using consensus voting in UWSNs. Computer Communications, 170, 144–155.
[17] Goyal, N., Dave, M., & Verma, A. K. (2017). A novel fault detection and recovery technique for cluster-based underwater wireless sensor networks. International Journal of Communication Systems, 31(4), e3485.
[18] Guo, Z., Wang, B., Xie, P., Zeng, W., & Cui, J. (2009). Efficient error recovery with network coding in underwater sensor networks. Ad Hoc Networks, 7(4), 791–802.
[19] Pu Wang, Jun Zheng, & Chunxiao Li. (2007). An agreement-based fault detection mechanism for underwater sensor networks. 2007 International Conference on Communications, 1195–1200.
[20] Ding, M., & Chen, D. (2005). Localized fault-tolerant event boundary detection in sensor networks. Proceedings of IEEE INFOCOM, 902–913.
[21] Jiang, P. (2009). A new method for node fault detection in wireless sensor networks. Sensors, 9(2), 1282–1294.
[22] Asim, M., Mokhtar, H. M., & Merabti, M. (2008). A fault management architecture for wireless sensor network. IEEE International Conference on Sensor Networks, 779–785.
[23] Zidi, S., Moulahi, T., & Alaya, B. (2018). Fault detection in wireless sensor networks through SVM classifier. IEEE Sensors Journal, 18(1), 340–347.
[24] Das, A. P., & Thampi, S. M. (2017). Fault-resilient localization for underwater sensor networks. Ad Hoc Networks, 55, 132–142.
[25] Chirdchoo, N., Soh, W. S., & Chua, K. C. (2008). Aloha-based MAC protocols with collision avoidance for underwater acoustic networks. INFOCOM 2007, 2271–2275.
[26] Ayaz, M., Baig, I., Abdullah, A., & Faye, I. (2011). A survey on fault tolerance techniques in underwater sensor networks. International Journal of Distributed Sensor Networks, 2011, 1–12.
[27] Singh, R., & Kaushik, B. (2023). Multi-agent architecture for fault management in UWSNs. International Journal of Communication Systems, 36(1), e5162.
[28] Basnet, B., Shrestha, M., & Kim, J. (2022). Fault detection using SVM in energy-constrained wireless sensor networks. Sensors, 22(14), 5231.
[29] Shaikh, A., Hussain, F., & Chen, L. (2024). A CNN-based lightweight model for real-time anomaly detection in UWSNs. IEEE Internet of Things Journal, 11(2), 1139–1150.
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