Performance analysis of a user-centric crowd-sensing water quality assessment system

Rapousis, Nikolaos; Papadopouli, Maria

doi:10.1109/cyswater.2016.7469058

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…When applying the proposed trust framework as an extension of an application that already uses two types of data, fewer modifications would be required. For example, QoWater [56] employs a wireless sensor network to monitor water distribution network infrastructure through objective measurements and collects feedback from users about the water quality (subjective measurements about water taste, color, odor, appearance and pressure). In this system, we could apply the proposed trust framework and derive prosumers’ trust attitudes based on comparing their subjective measurements and objective values obtained via wireless sensors.…”

Section: Discussionmentioning

confidence: 99%

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Zupancic

Žalik

2019

Sensors

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Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users’ trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang’s method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.

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Section: Discussionmentioning

confidence: 99%

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users’ Trust Dispositions’ Consideration

Zupancic

Žalik

2019

Sensors

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“…124 Contamination warning systems in water supply systems that combine real-time monitoring data with crowdsourcing using customer feedback on water quality via smartphones have been tested but require extended pilot field studies before implementation. 125 Further, remote mobile sensors moving along the pipe with the flow could increase the likelihood of detecting contamination and fully functional prototypes should be available in the near future. 25 The data-driven approaches above outlined allow the management and operation of network-based infrastructures to be improved by saving water and protecting environmental and human health.…”

Section: Approaches To Data-driven Uwmmentioning

confidence: 99%

“…Additionally, the monitoring of biological markers via, for example, quantitative polymerase chain reaction requires further development before being field ready . Contamination warning systems in water supply systems that combine real-time monitoring data with crowdsourcing using customer feedback on water quality via smartphones have been tested but require extended pilot field studies before implementation . Further, remote mobile sensors moving along the pipe with the flow could increase the likelihood of detecting contamination and fully functional prototypes should be available in the near future …”

Section: Approaches To Data-driven Uwmmentioning

confidence: 99%

The Potential of Knowing More: A Review of Data-Driven Urban Water Management

Eggimann

Mutzner

Wani

et al. 2017

Environ. Sci. Technol.

198

131

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The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.

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“…4. The device owner requires a sensing device interface that provides the ability to (1) subscribe to DCC(s), (2) unsubscribe from DCC(s), (3) configure sensor data sharing, (4) update the device's node data and (5) view a list of plugins (value-added applications) present on the device.…”

Section: Proposed Solutionmentioning

confidence: 99%

“…Leveraging the ability to collect data by pervasive, sensor equipped, mobile devices (often referred to as Crowdsensing) has attracted significant attention from mobile computing researchers, seeing applications in, e.g., environmental [1,2,3,4], infrastructure [5,6,7] and social [8,9] scenarios.…”

Section: Introductionmentioning

confidence: 99%