BIONIB: Blockchain-based IoT using Novelty Index in Bridge Health Monitoring (2402.14902v1)
Abstract: Bridge health monitoring becomes crucial with the deployment of IoT sensors. The challenge lies in securely storing vast amounts of data and extracting useful information to promptly identify unhealthy bridge conditions. To address this challenge, we propose BIONIB, wherein real-time IoT data is stored on the blockchain for monitoring bridges. One of the emerging blockchains, EOSIO is used because of its exceptional scaling capabilities for monitoring the health of bridges. The approach involves collecting data from IoT sensors and using an unsupervised machine learning-based technique called the Novelty Index (NI) to observe meaningful patterns in the data. Smart contracts of EOSIO are used in implementation because of their efficiency, security, and programmability, making them well-suited for handling complex transactions and automating processes within decentralized applications. BIONIB provides secure storage benefits of blockchain, as well as useful predictions based on the NI. Performance analysis uses real-time data collected from IoT sensors at the bridge in healthy and unhealthy states. The data is collected with extensive experimentation with different loads, climatic conditions, and the health of the bridge. The performance of BIONIB under varying numbers of sensors and various numbers of participating blockchain nodes is observed. We observe a tradeoff between throughput, latency, and computational resources. Storage efficiency can be increased by manifolds with a slight increase in latency caused by NI calculation. As latency is not a significant concern in bridge health applications, the results demonstrate that BIONIB has high throughput, parallel processing, and high security while efficiently scaled.
- S. Sony, S. Laventure, and A. Sadhu, “A literature review of next-generation smart sensing technology in structural health monitoring,” Structural Control and Health Monitoring, vol. 26, no. 3, p. e2321, 2019.
- E. Skordilis and R. Moghaddass, “A deep reinforcement learning approach for real-time sensor-driven decision making and predictive analytics,” Computers & Industrial Engineering, vol. 147, p. 106600, 2020.
- L. Yi, X. Deng, L. T. Yang, H. Wu, M. Wang, and Y. Situ, “Reinforcement-learning-enabled partial confident information coverage for iot-based bridge structural health monitoring,” IEEE Internet of Things Journal, vol. 8, no. 5, pp. 3108–3119, 2020.
- J. Xu, H. Liu, and Q. Han, “Blockchain technology and smart contract for civil structural health monitoring system,” Computer-Aided Civil and Infrastructure Engineering, vol. 36, no. 10, pp. 1288–1305, 2021.
- M. S. Ali, M. Vecchio, M. Pincheira, K. Dolui, F. Antonelli, and M. H. Rehmani, “Applications of blockchains in the internet of things: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 2, pp. 1676–1717, 2018.
- B. W. Jo, R. M. A. Khan, and Y.-S. Lee, “Hybrid blockchain and internet-of-things network for underground structure health monitoring,” Sensors, vol. 18, no. 12, p. 4268, 2018.
- Z. Chen, X. Zhou, X. Wang, L. Dong, and Y. Qian, “Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study,” Sensors, vol. 17, no. 9, p. 2151, 2017.
- W. M. Alenazy, “Blockchain-enabled internet of things for unsupervised structural health monitoring in potential building structures,” in Handbook of Research on Innovations and Applications of AI, IoT, and Cognitive Technologies. IGI Global, 2021, pp. 158–170.
- E. Akintunde, S. E. Azam, A. Rageh, and D. G. Linzell, “Unsupervised machine learning for robust bridge damage detection: full-scale experimental validation,” Engineering Structures, vol. 249, p. 113250, 2021.
- S. Ardani, E. Akintunde, D. Linzell, S. E. Azam, and Q. Alomari, “Evaluating pod-based unsupervised damage identification using controlled damage propagation of out-of-service bridges,” Engineering Structures, vol. 286, p. 116096, 2023.
- N. He, R. Zhang, H. Wang, L. Wu, X. Luo, Y. Guo, T. Yu, and X. Jiang, “{{\{{EOSAFE}}\}}: security analysis of {{\{{EOSIO}}\}} smart contracts,” in 30th USENIX Security Symposium (USENIX Security 21), 2021, pp. 1271–1288.
- N. He, H. Wang, L. Wu, X. Luo, Y. Guo, and X. Chen, “A survey on eosio systems security: Vulnerability, attack, and mitigation,” arXiv preprint arXiv:2207.09227, 2022.
- J. Abou Jaoude and R. G. Saade, “Blockchain applications–usage in different domains,” Ieee Access, vol. 7, pp. 45 360–45 381, 2019.
- M. Wu, K. Wang, X. Cai, S. Guo, M. Guo, and C. Rong, “A comprehensive survey of blockchain: From theory to iot applications and beyond,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8114–8154, 2019.
- U. Bodkhe, S. Tanwar, K. Parekh, P. Khanpara, S. Tyagi, N. Kumar, and M. Alazab, “Blockchain for industry 4.0: A comprehensive review,” IEEE Access, vol. 8, pp. 79 764–79 800, 2020.
- D. S. Gadiraju, V. Lalitha, and V. Aggarwal, “Secure regenerating codes for reducing storage and bootstrap costs in sharded blockchains,” in 2020 IEEE International Conference on Blockchain (Blockchain). IEEE, 2020, pp. 229–236.
- X. Cai, S. Geng, J. Zhang, D. Wu, Z. Cui, W. Zhang, and J. Chen, “A sharding scheme-based many-objective optimization algorithm for enhancing security in blockchain-enabled industrial internet of things,” IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7650–7658, 2021.
- P. W. Khan, Y.-C. Byun, and N. Park, “Iot-blockchain enabled optimized provenance system for food industry 4.0 using advanced deep learning,” Sensors, vol. 20, no. 10, p. 2990, 2020.
- D. S. Gadiraju, Lalitha, and V. Aggarwal, “An optimization framework based on deep reinforcement learning approaches for prism blockchain,” IEEE Transactions on Services Computing, vol. 16, no. 4, pp. 2451–2461, 2023.
- A. Aljuhani, P. Kumar, R. Alanazi, T. Albalawi, O. Taouali, A. N. Islam, N. Kumar, and M. Alazab, “A deep learning integrated blockchain framework for securing industrial iot,” IEEE Internet of Things Journal, 2023.
- A. Du and A. Ghavidel, “Parameterized deep reinforcement learning-enabled maintenance decision-support and life-cycle risk assessment for highway bridge portfolios,” Structural Safety, vol. 97, p. 102221, 2022.
- S. Da Silva, M. Dias Junior, and V. Lopes Junior, “Structural health monitoring in smart structures through time series analysis,” Structural Health Monitoring, vol. 7, no. 3, pp. 231–244, 2008.
- M. Gordan, Z. Ismail, F. A. Mohd Rahim, O. Z. Chao, Z. Ibrahim, H. Hashim, and L. C. Kiong, “Defining a conceptual framework for vibration-based damage detection platforms using blockchain,” J. Civ. Eng. Mater. Appl, vol. 5, no. 1, pp. 25–33, 2021.
- W. Zheng, Z. Zheng, H.-N. Dai, X. Chen, and P. Zheng, “Xblock-eos: Extracting and exploring blockchain data from eosio,” Information Processing & Management, vol. 58, no. 3, p. 102477, 2021.
- D. Perez, J. Xu, and B. Livshits, “Revisiting transactional statistics of high-scalability blockchains,” in Proceedings of the ACM Internet Measurement Conference, 2020, pp. 535–550.
- J. Liu, W. Zheng, D. Lu, J. Wu, and Z. Zheng, “From decentralization to oligopoly: A data-driven analysis of decentralization evolution and voting behaviors on eosio,” IEEE Transactions on Computational Social Systems, 2022.
- J. Á. Fernández-Carrasco, X. Echeberria-Barrio, D. Paredes-García, F. Zola, and R. Orduna-Urrutia, “Chronoeos 2.0: Device fingerprinting and eosio blockchain technology for on-running forensic analysis in an iot environment,” Smart Cities, vol. 6, no. 2, pp. 897–912, 2023.
- I. Grigg, “Eos-an introduction,” White paper. https://whitepaperdatabase. com/eos-whitepaper, 2017.
- EOSIO, “EOSIO GitHub Organization,” https://github.com/EOSIO, 2024.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.