Cognitive internet of things for smart water pipeline monitoring system

Date
2019
Authors
Abdelhafidh, Maroua
Mohamed, Fourati
Fourati, Lamia Chaari
Mnaouer, Adel Ben
Mokhtar, Zid
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Water Pipeline Monitoring System (WPMS) is extremely important considering the several pipeline damages and the various hydraulic failures that cause a critical water loss. In this context, Cognitive Water Distribution System integrates Internet of Things (IoT) technology, based on smart sensors, actuators and connected objects, with a reliable Big Data processing for smart and robust Structural Health Monitoring (SHM) of pipelines. In this paper, we propose a cognitive IoT-based architecture where we used Apache Spark framework to maintain a real time processing of the large amount of collected data. This efficient processing of measured data and its correspondent calculated values simplify the transient simulations and leak detection and make it faster and easier. © 2018 IEEE.
Description
This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT) (2018), available online at: https://ieeexplore.ieee.org/document/8600999.
Keywords
Big data, Cognitive IoT, Simulation, Water Monitoring System, Data handling, Leak detection, Monitoring, Pipeline processing systems, Pipelines, Structural health monitoring (SHM), Water distribution systems, Water pipelines, Internet of Things (IoT), Pipeline monitoring, Realtime processing, Simulation, Transient simulation, Water monitoring systems
Citation
Abdelhafidh, M., Fourati, M., Fourati, L. C., Ben Mnaouer, A., & Mokhtar, Z. (2019). Cognitive Internet of Things for Smart Water Pipeline Monitoring System. In Proceedings of the 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2018 (pp. 212–219). https://doi.org/10.1109/DISTRA.2018.8600999