IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Elsaleh, Tarek; Enshaeifar, Shirin; Rezvani, Roonak; Acton, Sahr Thomas; Janeiko, Valentinas; Bermúdez-Edo, María

doi:10.3390/s20040953

Cited by 75 publications

(49 citation statements)

References 32 publications

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“…The focus here is to support application developers in building intelligent data processing actors in a common ecosystem. To continue the look into data streams in IoT, Elsaleh et al (2020) present a lightweight ontology to model IoT data streams to support easy data analytics and event detection services.…”

Section: Related Workmentioning

confidence: 99%

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Data-sharing markets for integrating IoT data processing functionalities

Kasrin

Benabbas

Elmamooz

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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The recent evolution of the Internet of Things into a cyber-physical reality has spawned various challenges from a data management perspective. In addition, IoT platform designers are faced with another set of questions. How can platforms be extended to smoothly integrate new data management functionalities? Currently, data processing related tasks are typically realized by manually developed code and functions which creates difficulties in maintenance and growth. Hence we need to explore other approaches to integration for IoT platforms. In this paper we cover both these aspects: (1) we explore several emerging data management challenges, and (2) we propose an IoT platform integration model that can combine disparate functionalities under one roof. For the first, we focus on the following challenges: sensor data quality, privacy in data streams, machine learning model management, and resource-aware data management. For the second, we propose an information-integration model for IoT platforms. The model revolves around the concept of a Data-Sharing Market where data management functionalities can share and exchange information about their data with other functionalities. In addition, data-sharing markets themselves can be combined into networks of markets where information flows from one market to another, which creates a web of information exchange about data resources. To motivate this work we present a use-case application in smart cities.

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Section: Related Workmentioning

confidence: 99%

“…IoT has introduced radical changes in the way data are processed. The amount of IoT data, the velocity of change, and variety of sources/formats implies new challenges to process and inter-operate between heterogeneous data sources and formats Elsaleh et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Data-sharing markets for integrating IoT data processing functionalities

Kasrin

Benabbas

Elmamooz

et al. 2021

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

show abstract

“…In order to monitor a variety of sensory data, we need to integrate and combine several sensors through systems, which usually use IoT and/or microservices architectures [34][35][36]. IoT health systems allow doctors to monitor patients constantly and to suggest corrective actions according to the analyzed data [29,37,38]. For example, one of the goals of City-4Age and ACTIVAGE H2020 projects is the assessment of frailty using IoT technology based on rules [39].…”

Section: Related Workmentioning

confidence: 99%

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

Garcia-Moreno

Bermúdez-Edo

Garrido

et al. 2020

Sensors

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The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty.

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“…The use of activity trackers in clinical practice where every day each physician treats up to 30 patients, requires an approach that would guarantee ease of use and standardization of data from their sampling to their annotation [ 97 ]. One way could be the Internet of Things (IoT) integration services, that would allow patients to use their own device vendors technical stacks and disparity of devices are challenges, despite notable initiatives for instance at the smartphone level [ 98 ].…”

Section: Limits Of Activity Tracker In Clinical Practicesmentioning

confidence: 99%

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

Davergne

Rakotozafiarison

Servy³

et al. 2020

Sensors

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In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

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IoT-Stream: A Lightweight Ontology for Internet of Things Data Streams and Its Use with Data Analytics and Event Detection Services

Cited by 75 publications

References 32 publications

Data-sharing markets for integrating IoT data processing functionalities

Data-sharing markets for integrating IoT data processing functionalities

A Microservices e-Health System for Ecological Frailty Assessment Using Wearables

Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

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