Blockchain-Powered Smart System Platform Development: Use Case – Water Meter Readings

Mohamed, Shams Adhouha Ben; Talha, Amira; Ktari, Jalel; Taleb, Youssef Ben; Frikha, Tarek; Hamam, Habib

doi:10.2478/ias-2024-0015

Abstract

Today, blockchain technology represents a significant advancement in the field of data management, providing promising opportunities to transform various sectors. Indeed, it offers innovative solutions and builds trust within multi-stakeholder business networks. This article looks at multiple concrete applications of blockchain, highlighting its potential for optimizing water meter reading processes. Moreover, there’s a growing trend toward incorporating Artificial Intelligence (AI) for more accurate and efficient water meter readings. This integration enhances the precision and efficiency of water meter readings, providing valuable insights into water consumption. More specifically, it explores how this technology can be integrated into the development of a dedicated Android application. Thanks to this application, users can now easily and quickly take water meter readings from their smartphone. The data is then secured on the Ethereum blockchain, using smart contracts to guarantee the integrity and transparency of the information recorded. This revolutionary approach opens new perspectives for efficient and secure data management in the field of water resources management.

References

Akella, G. K., Wibowo, S., Grandhi, S., & Mubarak, S. (2023). A systematic review of blockchain technology adoption barriers and enablers for smart and sustainable agriculture. Big Data and Cognitive Computing, 7(2), 86.
Search in Google Scholar Back to article
Soares, B., Ferreira, A., & Veiga, P. M. (2023). The Benefits and Challenges of Blockchain Technology and eHealth Implementation in Estonia-A Literature Review. Applied Medical Informatics, 45(4).
Search in Google Scholar Back to article
Ezzeddini, L., Ktari, J., Zouaoui, I., Talha, A., Jarray, N. and Frikha, T. (2022). ‘Blockchain for the electronic voting system: case study: student representative vote in Tunisian institute’. In: 2022 15th International Conference on Security of Information and Networks (SIN), pp.01–07.
Search in Google Scholar Back to article
Raimundo, R. and Rosário, A. (2021). ‘Blockchain system in higher education’. European Journal of Investigation in Health, Psychology and Education, 11(1), pp.276–293.
Search in Google Scholar Back to article
Shah, F.A.S. et al. (2024). ‘Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain’. PeerJ Computer Science, 10, p.e1776. Available at: https://doi.org/10.7717/peerj-cs.1776.
Search in Google Scholar Back to article
Mazhar, T. et al. (2023). ‘Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods’. Future Internet, 15(2), p.83. Available at: https://doi. org/10.3390/fi15020083.
Search in Google Scholar Back to article
Khan, A.A. et al. (2022). ‘Healthcare ledger management: a blockchain and machine learning-enabled novel and secure architecture for medical industry’. Human-Centric Computing and Information Sciences, 12, p.55.
Search in Google Scholar Back to article
Frikha, T., Ktari, J., Zalila, B., Ghorbel, O. and Ben Amor, N. (2023). ‘Integrating blockchain and deep learning for intelligent greenhouse control and traceability’. Alexandria Engineering Journal, 79, pp.259-273.
Search in Google Scholar Back to article
Mitchell, I., Hara, S. and Sheriff, M. (2019). ‘dAppER: decentralised application for examination review’. In: 2019 IEEE 12th International Conference on Global Security, Safety and Sustainability (ICGS3), pp.1-14.
Search in Google Scholar Back to article
Ktari, J., Frikha, T., Hamdi, M. and Hamam, H. (2024). ‘Enhancing blockchain consensus with FPGA: accelerating implementation for efficiency’. IEEE Access. doi: 10.1109/ACCESS.2024.3379374
Open DOI Search in Google Scholar Back to article
Frikha, T., Ktari, J. and Hamam, H. (2022). ‘Blockchain olive oil supply chain’. In: CRiSIS2022: 17th International Conference on Risks and Security of Internet and Systems, Tunisia.
Search in Google Scholar Back to article
Kuzlu, M., Pipattanasomporn, M., Gurses, L. and Rahman, S. (2019). ‘Performance analysis of a Hyperledger Fabric blockchain framework: throughput, latency and scalability’. In: 2019 IEEE International Conference on Blockchain (Blockchain), pp.536-540.
Search in Google Scholar Back to article
Qureshi, A. and Megías Jiménez, D. (2020). ‘Blockchain-based multimedia content protection: review and open challenges’. Applied Sciences, 11(1), p.1. Available at: https://doi.org/10.3390/app11010001.
Search in Google Scholar Back to article
Abdelmaboud, A. et al. (2022). ‘Blockchain for IoT applications: taxonomy, platforms, recent advances, challenges and future research directions’. Electronics, 11, p.630. Available at: https://doi.org/10.3390/electronics11040630.
Search in Google Scholar Back to article
Zuo, Y. (2021). ‘Making smart manufacturing smarter – a survey on blockchain technology in Industry 4.0’. Enterprise Information Systems, 15, pp.1323-1353. Available at: https://doi.org/10.1080/17517575.2020.1 856425.
Search in Google Scholar Back to article
Hasan, M.K. et al. (2022). ‘Blockchain technology on smart grid, energy trading, and big data: security issues, challenges, and recommendations’. Wireless Communications and Mobile Computing, 2022, pp.1-26. Available at: https://doi.org/10.1155/2022/9065768.
Search in Google Scholar Back to article
Khoshavi, N., Tristani, G. and Sargolzaei, A. (2021). ‘Blockchain applications to improve operation and security of transportation systems: a survey’. Electronics, 10, p.629. Available at: https://doi.org/10.3390/electronics10050629.
Search in Google Scholar Back to article
Raja Santhi, A. and Muthuswamy, P. (2022). ‘Influence of blockchain technology in manufacturing supply chain and logistics’. Logistics, 6, p.15. Available at: https://doi.org/10.3390/logistics6010015.
Search in Google Scholar Back to article
Pahontu, B., Arsene, D., Predescu, A. and Mocanu, M. (2020). ‘Application and challenges of blockchain technology for real-time operation in a water distribution system’. In: 2020 24th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, pp.739-744. Available at: https://doi.org/10.1109/ICSTCC50638.2020.9259732.
Search in Google Scholar Back to article
Suresh, M., Muthukumar, U. and Chandapillai, J. (2017). ‘A novel smart water-meter based on IoT and smartphone app for city distribution management’. In: 2017 IEEE Region 10 Symposium (TENSYMP), Cochin, India, pp.1-5. Available at: https://doi.org/10.1109/TENCONSpring.2017.8070088.
Search in Google Scholar Back to article
Yang, F., Jin, L., Lai, S., Gao, X. and Li, Z. (2019). ‘Fully convolutional sequence recognition network for water meter number reading’. IEEE Access, 7, pp.11679-11687. Available at: https://doi.org/10.1109/ACCESS.2019.2891767.
Search in Google Scholar Back to article
Naim, A., Aaroud, A., Akodadi, K. and El Hachimi, C. (2021). ‘A fully AI-based system to automate water meter data collection in Morocco country’. Array, 10, p.100056. Available at: https://doi.org/10.1016/j.array.2021.100056.
Search in Google Scholar Back to article
Bordel, B., Martin, D., Alcarria, R. and Robles, T. (2019). ‘A blockchain-based water control system for the automatic management of irrigation communities’. In: 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, pp.1-2. Available at: https://doi.org/10.1109/ICCE.2019.8661940.
Search in Google Scholar Back to article
Enescu, F.M. et al. (2020). ‘Implementing blockchain technology in irrigation systems that integrate photovoltaic energy generation systems’. Sustainability, 12(4), p.1540. Available at: https://doi.org/10.3390/su12041540.
Search in Google Scholar Back to article
Furones, A.R. and Monzón, J.I.T. (2023). ‘Blockchain applicability in the management of urban water supply and sanitation systems in Spain’. Journal of Environmental Management, 344, p.118480.
Search in Google Scholar Back to article
Zeng, H., Dhiman, G., Sharma, A., Sharma, A. and Tselykh, A. (2023). ‘An IoT and blockchain-based approach for the smart water management system in agriculture’. Expert Systems, 40(4).
Search in Google Scholar Back to article
Kim, J. and Ju-Yeong, S. (2020). ‘Comparison of Faster-RCNN, YOLO, and SSD for real-time vehicle type recognition’. In: 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Seoul, Korea (South): IEEE, pp.1-4.
Search in Google Scholar Back to article
Melek, G.C., Sonmez, E.B. and Albayrak, S. (2019). ‘Object detection in shelf images with YOLO’. In: IEEE EUROCON 2019-18th International Conference on Smart Technologies, Novi Sad, Serbia: IEEE, pp.1-5.
Search in Google Scholar Back to article
Liu, Y., Liu, J. and Ke, Y. (2020). ‘A detection and recognition system of pointer meters in substations based on computer vision’. Measurement, 152, p.107333.
Search in Google Scholar Back to article
Li, C. et al. ‘YOLOv6: a single-stage object detection framework for industrial applications’. Available at: http://arxiv.org/abs/2209.02976.
Search in Google Scholar Back to article
Xu, S. et al. ‘PP-YOLOE: an evolved version of YOLO’. Available at: http://arxiv.org/abs/2203.16250.
Search in Google Scholar Back to article
Ge, Z. et al. ‘YOLOX: exceeding YOLO series in 2021’. Available at: http://arxiv.org/abs/2107.08430.
Search in Google Scholar Back to article
Wang, C. and Alexey, A. ‘YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors’. Available at: http://arxiv.org/abs/2207.02696.
Search in Google Scholar Back to article
Chatrasi, A. L. V. S. S., Batchu, A. G., Kommareddy, L. S., & Garikipati, J. (2023, April). Pedestrian and object detection using image processing by yolov3 and yolov2. In 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1667-1672). IEEE.
Search in Google Scholar Back to article
Hong, Q. et al. (2021). ‘Image-based automatic watermeter reading under challenging environments’. Sensors, 21(2), p.434.
Search in Google Scholar Back to article
Martinelli, F., Francesco, M. and Antonella, S. (2022). ‘Water meter reading for smart grid monitoring’. Sensors, 23(1), p.75.
Search in Google Scholar Back to article
Zhao, S., Lu, Q., Zhang, C., Ahn, C. K., & Chen, K. (2024). Effective recognition of word-wheel water meter readings for smart urban infrastructure. IEEE Internet of Things Journal.
Search in Google Scholar Back to article
Salomon, G., Rayson, L. and David, M. (2020). ‘Deep learning for image-based automatic dial meter reading: dataset and baselines’. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom: IEEE, pp.1-8. Available at: https://ieeexplore.ieee.org/document/9207318/.
Search in Google Scholar Back to article
Anis, A. et al. (2017). ‘Digital electric meter reading recognition based on horizontal and vertical binary pattern’. In: 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), Khulna: IEEE, pp.1-6. Available at: http://ieeexplore.ieee.org/document/8275241/.
Search in Google Scholar Back to article
Koščević, K. and Marko, S. (2019). ‘Automatic visual reading of meters using deep learning’. In: Proceedings of the Croatian Conference on Visual Pattern Recognition, pp.1-6. Available at: https://www. fer.unizg.hr/crv/ccvw.2018.0002.
Search in Google Scholar Back to article
Laroca, R. et al. (2019). ‘Convolutional neural networks for automatic meter reading’. Journal of Electronic Imaging, 28(1), p.1.
Search in Google Scholar Back to article
Dongmei, S., Shuhua, M. and Chunguo, J. (2007). ‘Study of the automatic reading of watt meter based on image processing technology’. In: 2007 2nd IEEE Conference on Industrial Electronics and Applications, Harbin, China: IEEE, pp.i-i. Available at: http://ieeexplore.ieee.org/document/4318336/.
Search in Google Scholar Back to article
Chun-Ming, T. and John, K. (2018). ‘Digits detection in watt hour meter’. In: Tenth International Conference on Advanced Computational Intelligence (ICACI).
Search in Google Scholar Back to article
Ultralytics. ‘yolov8’. GitHub repository. Available at: https://github.com/ultralytics/ultralytics. (accessed on 15 December 2023)
Search in Google Scholar Back to article

Blockchain-Powered Smart System Platform Development: Use Case – Water Meter Readings

Abstract

Paradigm

My account