IoT Data Validation Using Spatial and Temporal Correlations

Sartori, Fábio; Melen, Riccardo; Giudici, F.

doi:10.1007/978-3-030-36599-8_7

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Future works are devoted to implement such solutions: in particular, the spatial-temporal algorithm presented in [ 9 ] will be exploited as a starting point.…”

Section: Discussionmentioning

confidence: 99%

The Role of Data Storage in the Design of Wearable Expert Systems

Sartori

Savi

Melen

2021

Metadata and Semantic Research

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Wearable technologies are transforming research in software and knowledge engineering research fields. In particular, expert systems have the opportunity to manage knowledge bases varying according to real-time data collected by position sensors, movement sensors, and so on. This opportunity launches a series of challenges, from the role of network technologies to allow reliable connection between applications and sensors to the definition of functions and methods to assess the quality and reliability of gathered data. In this paper, we reflect about the last point, presenting recent reflections on the wearable environment notion. An architecture for the reliable acquisition of data in the IoT context is proposed, together with first experiments conducted to evaluate its effectiveness in improving the quality of data elaborated by applications.

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“…Future works are devoted to implement such solutions: in particular, the spatial-temporal algorithm presented in [ 9 ] will be exploited as a starting point.…”

Section: Discussionmentioning

confidence: 99%

The Role of Data Storage in the Design of Wearable Expert Systems

Sartori

Savi

Melen

2021

Metadata and Semantic Research

Self Cite

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“…Consequently, given that, in a wearable environment, WEAR-IT acts as an intermediate data collector, it is important to ensure that the quality of data stored is high enough (see Section 5.2 ). This issue is currently addressing our research [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

An API for Wearable Environments Development and Its Application to mHealth Field †

Sartori

2020

Sensors

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Wearable technologies are transforming research in traditional paradigms of software and knowledge engineering. Among them, expert systems have the opportunity to deal with knowledge bases dynamically varying according to real-time data collected by position sensors, movement sensors, etc. However, it is necessary to design and implement opportune architectural solutions to avoid expert systems are responsible for data acquisition and representation. These solutions should be able to collect and store data according to expert systems desiderata, building a homogeneous framework where data reliability and interoperability among data acquisition, data representation and data use levels are guaranteed. To this aim, the wearable environment notion has been introduced to treat all those information sources as components of a larger platform; a middleware has been designed and implemented, namely WEAR-IT, which allows considering each sensor as a source of information that can be dynamically tied to an expert system application running on a smartphone. As an application example, the mHealth domain is considered.

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“…STID FC repairs faulty thematic values [98,178,184] or imprecise timestamps [91,138,157,162,188]. Pumpichet et al [178] employ a belief-based approach to identify a group of helpful neighboring sensors based on the consistency of their data streams, estimating replacement values for dirty readings based on the time and distance over the identified group.…”

Section: Fault Correction (Fc)mentioning

confidence: 99%

“…When these vectors indicate a provisionally unreliable data source, such a source is replaced by an alternate virtual data source that is created based on spatiotemporal analysis and interpolation methods. Providing a centralized data validation method, Sartori et al [184] measure the Pearson correlation coefficients between the most recent reading sequences of adjacent sensors and find repairs for missing and anomalous readings from a single sensor based on the readings from correlated sensors.…”

Section: Fault Correction (Fc)mentioning

confidence: 99%

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

Jensen

et al. 2022

ACM Comput. Surv.

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With the continued deployment of the Internet of Things (IoT) , increasing volumes of devices are being deployed that emit massive spatially referenced data. Due in part to the dynamic, decentralized, and heterogeneous architecture of the IoT, the varying and often low quality of spatial IoT data (SID) presents challenges to applications built on top of this data. This survey aims to provide unique insight to practitioners who intend to develop IoT-enabled applications and to researchers who wish to conduct research that relates to data quality in the IoT setting. The survey offers an inventory analysis of major data quality dimensions in SID and covers significant data characteristics and associated quality considerations. The survey summarizes data quality related technologies from both task and technique perspectives. Organizing the technologies from the task perspective, it covers recent progress in SID quality management, encompassing location refinement, uncertainty elimination, outlier removal, fault correction, data integration, and data reduction; and it covers low-quality SID exploitation, encompassing querying, analysis, and decision-making techniques. Finally, the survey covers emerging trends and open issues concerning the quality of SID.

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IoT Data Validation Using Spatial and Temporal Correlations

Cited by 5 publications

References 21 publications

The Role of Data Storage in the Design of Wearable Expert Systems

The Role of Data Storage in the Design of Wearable Expert Systems

An API for Wearable Environments Development and Its Application to mHealth Field †

Spatial Data Quality in the Internet of Things: Management, Exploitation, and Prospects

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