Challenges for Quality of Data in Smart Cities

Barnaghi, Payam; Bermúdez-Edo, María; Tönjes, Ralf

doi:10.1145/2747881

Cited by 49 publications

(43 citation statements)

References 7 publications

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“…An approach advocated in [2] is to use lightweight dynamic semantics which our system uses to maintain interoperable descriptions of data sources inside the Data Lake. This metadata is then used to update a data mart in close to real-time.…”

Section: Background and Problem Statementmentioning

confidence: 99%

“…This ontology itself is built from a semantic data warehouse, which provides ontologies about all source data required for multidimensional analysis. However, this does not provide the flexible and more lightweight approach [2] necessary for online data sources. In [3], the authors again present a smart city ontology which is combined with a data collection system to harvest smart city data sources to provide smart city applications.…”

Section: Related Researchmentioning

confidence: 99%

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Data Analytics

2017

Lecture Notes in Computer Science

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Abstract. The development of smart city infrastructures is seen as an important strategic component for many countries with applications in the areas of government services, healthcare, transport and traffic management, energy, water, and the management of waste services. In general, each of these infrastructural components generates data as it delivers its service. The usage of this data is often crucial both to the continued development of the service and for forward planning. While the data sources are often not complex in structure, traditional decision support systems are not built to handle continuous data streams, new or disappearing data sources, or the integration of multiple online data sources. In this work, we develop a flexible ETL process to quickly process and integrate online smart city data sources to deliver information that can be used in close to real-time.

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Section: Background and Problem Statementmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Data Analytics

2017

Lecture Notes in Computer Science

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“…However, although it has been considered as one crucial item in data networks, the Quality of Information (QoI) is still not well defined and sometimes difficult to measure [19], [20]. In our model, we have designed the QoI module based on the IoT.est model, and enhanced it with some related concepts, as well as with the help of experts in the field of data networks and data applications.…”

Section: Qos and Qoi Modulementioning

confidence: 99%

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Kolozali

Puschmann

Bermúdez-Edo

et al. 2016

IEEE Internet Things J.

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Abstract-With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

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“…Over numerous integration platforms that have been developed for the Internet of Things (IoT), network Quality of Service (QoS) [1] has shown to be unsuitable for assessing the actual value of observations [2]. Basically, within datacentric systems, Quality of Observation (QoO) mainly depends on consumer needs and the specific context in which he/she consumes it.…”

Section: Introductionmentioning

confidence: 99%

Towards the internet of everything: Deployment scenarios for a QoO-aware integration platform

Auger

Expósito

Lochin

2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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Abstract-Built upon the Internet of Things (IoT), the Internet of Everything (IoE) acknowledges the importance of data quality within sensor-based systems, alongside with people, processes and Things. Nevertheless, the impact of many technologies and paradigms that pertain to the IoE is still unknown regarding Quality of Observation (QoO).This paper proposes to study experimental results from three IoE-related deployment scenarios in order to promote the QoO notion and raise awareness about the need for characterizing observation quality within sensor-based systems. We specifically tailor the definition of QoO attributes to each use case, assessing observation accuracy within Smart Cities, observation rate for virtual sensors and observation freshness within post-disaster areas. To emulate these different experiments, we rely on a custom-developed integration platform for the assessment of QoO as a service called iQAS.We show that QoO attributes should be used to specify what is an observation of "good quality", that virtual sensors may have specific and limiting capabilities impacting QoO and that network QoS and QoO are two complementary quality dimensions that should be used together to improve the overall service provided to end-users.

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Challenges for Quality of Data in Smart Cities

Cited by 49 publications

References 7 publications

Data Analytics

Data Analytics

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Towards the internet of everything: Deployment scenarios for a QoO-aware integration platform

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