Achieving Smart Water Network Management Through Semantically Driven Cognitive Systems

IoT-based smart water supply network management applications generate a huge volume of data from the installed sensing devices which are required to be processed (sometimes in-network), stored and transmitted to a remote centre for decision making. When the volume of data produced by diverse IoT smart sensing devices intensify, processing and storage of these data begin to be a serious issue. The large data size acquired from these applications increases the computational complexities, occupies the scarce bandwidth of data transmission and increases the storage space. Thus, data size reduction through the use of data compression algorithms is essential in IoT-based smart water network management applications. In this paper, the performance evaluation of four different data compression algorithms used for this purpose is presented. These algorithms, which include RLE, Huffman, LZW and Shanon-Fano encoding were realised using MATLAB software and tested on six water supply system data. The performance of each of these algorithms was evaluated based on their compression ratio, compression factor, percentage space savings, as well as the compression gain. The results obtained showed that the LZW algorithm shows better performance base on the compression ratio, compression factor, space savings and the compression gain. However, its execution time is relatively slow compared to the RLE and the two other algorithms investigated. Most importantly, the LZW algorithm has a significant reduction in the data sizes of the tested files than all other algorithms

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Innovative and sustainable methodologies for smart water network management

Civil Engineering and Environmental Systems ◽

10.1080/10286608.2016.1148144 ◽

2016 ◽

Vol 33 (1) ◽

pp. 1-2

Author(s):

Marco Franchini ◽

Bruno Brunone

Keyword(s):

Network Management ◽

Water Network ◽

Smart Water

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Fixing the Future of Wastewater Systems With Smart Water Network Modeling

American Water Works Association ◽

10.5942/jawwa.2015.107.0057 ◽

2015 ◽

Vol 107 (4) ◽

pp. 72-80 ◽

Cited By ~ 4

Author(s):

PAUL F. Boulos ◽

Andrew T. Walker

Keyword(s):

Network Modeling ◽

Water Network ◽

Smart Water ◽

The Future ◽

Wastewater Systems

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Improving Conservation of Florida Manatees (Trichechus manatus latirostris): Conceptualization and Contributions Toward a Regional Warm-Water Network Management Strategy for Sustainable Winter Habitat

Environmental Management ◽

10.1007/s00267-012-9985-4 ◽

2012 ◽

Vol 51 (1) ◽

pp. 154-166 ◽

Cited By ~ 2

Author(s):

Richard Owen Flamm ◽

John Elliot Reynolds ◽

Craig Harmak

Keyword(s):

Network Management ◽

Management Strategy ◽

Warm Water ◽

Water Network ◽

Winter Habitat ◽

Trichechus Manatus ◽

Trichechus Manatus Latirostris

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Using optimisation for integrated water network management

Civil Engineering Systems ◽

10.1080/02630259108970633 ◽

1991 ◽

Vol 8 (4) ◽

pp. 241-245 ◽

Cited By ~ 1

Author(s):

Pierre-Antoine Jarrige ◽

Tony Harding ◽

David Knight ◽

David Howes

Keyword(s):

Network Management ◽

Water Network

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On-line Measuring Sensors for Smart Water Network Monitoring

10.29007/4fcr ◽

2018 ◽

Author(s):

Armando Di Nardo ◽

David Baquero Gonzalez ◽

Tom Baur ◽

Romeo Bernini ◽

Sergio Bodini ◽

...

Keyword(s):

Smart Cities ◽

Information And Communications Technology ◽

Divide And Conquer ◽

Water Networks ◽

Water Sector ◽

Water Network ◽

Network Efficiency ◽

Smart Meters ◽

Smart Water ◽

On Line

Smart cities are getting essential to drive economic growth, increase social prospects and improve high-quality lifestyle for citizens. To meet the goal of smart cities, Information and Communications Technology (ICT) have a key role. The application of smart solutions will allow the cities to use ICT and big data to improve infrastructure and services (i.e. network efficiency, protection from contamination, etc.). In the water sector, the integration of smart meters and sensors coupled with cloud computing and the paradigm of “divide and conquer” introduces a novel and smart management of the water network allowing an efficient online monitoring and transforming the traditional water networks into modern Smart WAter Networks (SWAN). The Ctrl+SWAN (Cloud Technologies & ReaL time monitoring+Smart WAter Network) Action Group (AG) was created within the European Innovation Partnership on Water, in order to promote innovation in the water sector by advancing existing smart solutions. The paper presents an update of a previous work on the state of the art on the best On-line Measuring Sensors (OMS) already available on the market and innovative technologies in the Research and Development (R&D) phases.

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A nonparametric framework for water consumption data cleansing: an application to a smart water network in Naples (Italy)

Journal of Hydroinformatics ◽

10.2166/hydro.2020.133 ◽

2020 ◽

Vol 22 (4) ◽

pp. 666-680 ◽

Cited By ~ 2

Author(s):

Roberta Padulano ◽

Giuseppe Del Giudice

Keyword(s):

Time Series ◽

Water Consumption ◽

Remote Monitoring ◽

Prior Information ◽

Water Network ◽

Smart Water ◽

Consumption Data ◽

Large Databases ◽

Unsupervised Approach ◽

Water Uses

Abstract Remote monitoring and collection of water consumption has gained pivotal importance in the field of demand understanding, modelling and prediction. However, most of the analyses that can be performed on such databases could be jeopardized by inconsistencies due to technological or behavioural issues causing significant amounts of missing or anomalous values. In the present paper, a nonparametric, unsupervised approach is presented to investigate the reliability of a consumption database, applied to the dataset of a district metering area in Naples (Italy) and focused on the detection of suspicious amounts of zero or outlying data. Results showed that the methodology is effective in identifying criticalities both in terms of unreliable time series, namely time series having huge amounts of invalid data, and in terms of unreliable data, namely data values suspiciously different from some suitable central parameters, irrespective of the source causing the anomaly. As such, the proposed approach is suitable for large databases when no prior information is known about the underlying probability distribution of data, and it can also be coupled with other nonparametric, pattern-based methods in order to guarantee that the database to be analysed is homogeneous in terms of water uses.

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