Data Movement in the Internet of Things Domain

D'Andria, Francesco; Field, Daniel; Kopaneli, Aliki; Kousiouris, George; García-Pérez, David; Pernici, Barbara; Plebani, Pierluigi

doi:10.1007/978-3-319-24072-5_17

Cited by 10 publications

(6 citation statements)

References 13 publications

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“…Focusing on the optimization of the data movement, it is fundamental to properly manage the information logistics (Michelberger et al, 2013 ), i.e., the delivery of information at the right time, in the right place, and with the right quality and format to the user (D'Andria et al, 2015 ). As a consequence, user requirements can be defined in terms of functional aspects, i.e., contents, and non-functional ones, i.e., time, location, representation, and quality (Plebani et al, 2017 ).…”

Section: Fog Computingmentioning

confidence: 99%

“…In particular, (Michelberger et al, 2013 ) identifies different perspectives around which Information Logistics can be studied: e.g., from an organizational standpoint in terms of how to exploit the data collected and managed inside an organization for strategy purposes or how to properly distribute the data in a supply chain management. The so-called user-oriented Information Logistics (i.e., the delivery of information at the right time, place, and with the right quality and format to the user) advocates data movement (D'Andria et al, 2015 ). The issue of inter-process communication has been faced also in Vitali and Pernici ( 2016 ) based on healthcare scenario.…”

Section: Related Workmentioning

confidence: 99%

“…The aim of this work is to present how the principles of the Fog Computing paradigm can be adopted to improve health monitoring with the aim of providing information at the right time, in the right place, and with the right quality and format for the user (D'Andria et al, 2015 ). For this reason, the proposed framework is based on two main elements:…”

Section: Introductionmentioning

confidence: 99%

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Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

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Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.

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Section: Fog Computingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Health Monitoring With Adaptive Data Movement in Fog Computing

et al. 2020

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“…Finally, a data movement scenario is considered, where moving large quantities of data takes time, and resilience could be weakened due to late/missing data. In this case, preparation strategies to anticipate possible failures can be envisioned, such as transferring data in advance, pre-selecting data, changing data location and redundancy policies [10].…”

Section: Resiliency Scenariosmentioning

confidence: 99%

Design-time Models for Resiliency

Marrella

Mecella

Pernici

et al. 2017

Conceptual Modeling Perspectives

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Resiliency in process-aware information systems is based on the availability of recovery flows and alternative data for coping with missing data. In this paper, we discuss an approach to process and information modeling to support the specification of recovery flows and alternative data. In particular, we focus on processes using sensor data from different sources. The proposed model can be adopted to specify resiliency levels of information systems, based on event-based and temporal constraints.

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“…Knowledge can be represented in the form of predictive analytics queries, tasks, or explanatory-driven models and statistics, e.g., regression coefficients, histograms, digests. ENs tend to improve their computational resources as the processors and memory become even more powerful, while their connectivity is not guaranteed [21]. The local storage of Manuscript received April, 2020.…”

Section: Introductionmentioning

confidence: 99%

Proactive & Time-Optimized Data Synopsis Management at the Edge

Kolomvatsos

Anagnostopoulos

Koziri

et al. 2020

IEEE Trans. Knowl. Data Eng.

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Internet of Things offers the infrastructure for smooth functioning of autonomous context-aware devices being connected towards the Cloud. Edge Computing (EC) relies between the IoT and Cloud providing significant advantages. One advantage is to perform local data processing (limited latency, bandwidth preservation) with real time communication among IoT devices, while multiple nodes become hosts of the collected data (reported by IoT devices). In this work, we provide a mechanism for the exchange of data synopses (summaries of extracted knowledge) among EC nodes that are necessary to give the knowledge on the data present in EC environments. The overarching aim is to intelligently decide on when nodes should exchange data synopses in light of efficient execution of tasks. We enhance such a decision with a stochastic optimization model based on the Theory of Optimal Stopping. We provide the fundamentals of our model and the relevant formulations on the optimal time to disseminate data synopses to network edge nodes. We report a comprehensive experimental evaluation and comparative assessment related to the optimality achieved by our model and the positive effects on EC.

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Data Movement in the Internet of Things Domain

Cited by 10 publications

References 13 publications

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

Improving Health Monitoring With Adaptive Data Movement in Fog Computing

Design-time Models for Resiliency

Proactive & Time-Optimized Data Synopsis Management at the Edge

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