Development of Real-Time Reservoir Surveillance Platforms Using Edge Computing and Wireless Sensor Networks
DOI:
https://doi.org/10.64137/XXXXXXXX/IJETET-V1I1P104Keywords:
Edge computing, Wireless sensor networks, Reservoir computing, Real-time surveillance, Energy efficiency, Fault detectionAbstract
Continuous surveillance in a reservoir is necessary for managing resources properly; yet traditional cloud services encounter delays, limited bandwidth and high energy costs. To overcome these limitations, the study presents a new system that merges edge computing with Wireless Sensor Networks (WSNs). The system achieves faster response times and decreased power use by processing information close to where sensors are placed. ESNs and other reservoir computing structures are key components of the platform for processing unbalanced sensor information smoothly. Real-time detection and prediction of unusual patterns is possible with ESNs because their reservoirs can automatically handle input signals. This process is made better by using a fault-tolerant edge provisioning framework that helps choose the best placement for nodes that balance being reliable, using less energy and delivering services fast. Specifically, the devices can preprocess their data and filter out noisy parts and then encode these important features before they send the state vectors to servers for deeper study. Experiments in water quality monitoring reveal how the system processes all the intended measurements (such as pH and dissolved oxygen) despite not having much bandwidth. The use of inkjet-printed sensors along with analog pulse-based telemetry helps to both lower costs and use less energy. There is a 40% decrease in delay time for transferring data and a 30% rise in successful fault detection, as shown by the results, compared to models that depend on the cloud
References
[1] Ren, J., Zhu, Q., & Wang, C. (2022). Edge Computing for Water Quality Monitoring Systems. Mobile Information Systems, 2022(1), 5056606.
[2] Matsumura, G., Honda, S., Kikuchi, T., Mizuno, Y., Hara, H., Kondo, Y., & Takei, K. (2025). Real-time personal healthcare data analysis using edge computing for multimodal wearable sensors. Device, 3(2).
[3] Kokalj-Filipovic, S., Toliver, P., Johnson, W., & Miller, R. (2021, August). Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors. In 2021 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 17-24). IEEE.
[4] Sharma, A., Samuel, P., Gey, G. M., & Kumar, S. (2020, October). Edge Computing: Continuous Surveillance and Management of Production Operations in a Cost Effective Manner. In SPE Annual Technical Conference and Exhibition? (p. D031S022R003). SPE.
[5] Jere, S., Said, K., Zheng, L., & Liu, L. (2023, October). Towards explainable machine learning: The effectiveness of reservoir computing in wireless receive processing. In MILCOM 2023-2023 IEEE Military Communications Conference (MILCOM) (pp. 667-672). IEEE.
[6] Zhu, B., Susilo, W., Qin, J., Guo, F., Zhao, Z., & Ma, J. (2019). A secure and efficient data sharing and searching scheme in wireless sensor networks. Sensors, 19(11), 2583.
[7] Ergen, M., Saoud, B., Shayea, I., El-Saleh, A. A., Ergen, O., Inan, F., & Tuysuz, M. F. (2024). Edge computing in future wireless networks: A comprehensive evaluation and vision for 6G and beyond. ICT Express.
[8] Nugraha, B., Nair, R., & Muhammad, K. (2020, January). Smart Real Time Data Transfer Surveillance with Edge Computing and Centralized Remote Monitoring System. In International Petroleum Technology Conference (p. D022S182R001). IPTC.
[9] Mohammed, H. R., & Abduljabbar, J. M. (2022). Edge Computing Enabled Wireless Sensor Network Requirement: A Survey. Quantum Journal of Engineering, Science and Technology, 3(3), 1-13..
[10] Aral, A., De Maio, V., & Brandic, I. (2021). Ares: Reliable and sustainable edge provisioning for wireless sensor networks. IEEE Transactions on Sustainable Computing, 7(4), 761-773.
[11] Malek, S. A., Avanzi, F., Brun-Laguna, K., Maurer, T., Oroza, C. A., Hartsough, P. C., & Glaser, S. D. (2017). Real-time alpine measurement system using wireless sensor networks. Sensors, 17(11), 2583.
[12] Potters, H., & Kapteijn, P. K. (2005, November). Reservoir surveillance and smart fields. In International Petroleum Technology Conference (pp. IPTC-11039). IPTC.
[13] Chorneyko, D. M. (2006, September). Real-time reservoir surveillance utilizing permanent downhole pressures—an operator's experience. In SPE Annual Technical Conference and Exhibition? (pp. SPE-103213). SPE.
[14] Roostaei, J., Wager, Y. Z., Shi, W., Dittrich, T., Miller, C., & Gopalakrishnan, K. (2023). IoT-based edge computing (IoTEC) for improved environmental monitoring. Sustainable computing: informatics and systems, 38, 100870.
[15] Fang, J., Hu, J., Wei, J., Liu, T., & Wang, B. (2020). An efficient resource allocation strategy for edge-computing based environmental monitoring system. Sensors, 20(21), 6125.
[16] Kirti Vasdev (2024). “Real-Time Spatial Data in Oil and Gas Asset Management and Operations”. Journal of Oil, Petroleum and Natural Gas Research. SRC-JOPNGR-24-E101, 1(1), 1-4.DOI: doi.org/10.47363/JOPNGR/2024(1)101
[17] Vegineni, Gopi Chand, and Bhagath Chandra Chowdari Marella. "Integrating AI-Powered Dashboards in State Government Programs for Real-Time Decision Support." AI-Enabled Sustainable Innovations in Education and Business, edited by Ali Sorayyaei Azar, et al., IGI Global, 2025, pp. 251-276. https://doi.org/10.4018/979-8-3373-3952-8.ch011
[18] Kirti Vasdev. (2019). “GIS in Disaster Management: Real-Time Mapping and Risk Assessment”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288561
[19] D. Kodi, “Designing Real-time Data Pipelines for Predictive Analytics in Large-scale Systems,” FMDB Transactions on Sustainable Computing Systems., vol. 2, no. 4, pp. 178–188, 2024.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
