Qualitative Evaluation of IoT-Driven eHealth

Lokshina, Izabella; Lanting, Cees J. M.

doi:10.4018/ijitn.2018100102

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…The framework demonstrates the utilization of Internet of Things (IoT) applications, which involve gathering data from users of Smart devices. This assertion is supported by the findings of Uden and He (2017) and Lokshina and Lanting (2019). Acquiring knowledge through data gathering is crucial in influencing decision-making inside organizations.…”

Section: Figure 5 Conceptual and Operational Frameworkmentioning

confidence: 64%

Unlocking Internet of Things (IoT) for Enhanced Knowledge Management and Organizational Performance: A Case Study of Diverse Industry Players in the Philippines

Sangalang,

Ancla,

Chong

et al. 2024

IJMABER

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Web 3.0 resulted in the increased decentralization of information, especially upon the emergence of the Internet of Things (IoT). IoT has revolutionized data transfer and has since reshaped knowledge management in organizations. This research focuses on how the IoT enables real-time data sharing and its impact on enhancing knowledge management and business processes in various local businesses. The study examines how five Philippine enterprises utilize the IoT through a qualitative embedded cross-case analysis. Several methods, like interviews, focus groups, and questionnaires, helped gather insights into how the IoT influences knowledge management processes. The research follows a case study approach, and the findings reveal that IoT boosts the generation, storage, sharing, and utilization of knowledge, aiding organizations in enhancing operational efficiency and product and service quality, thus improving business processes. Further, the research offers insights into the IoT possibilities. It provides practical recommendations for seamless integration through the companies' capabilities that enhance their knowledge management and operational results, gaining insights for strategic implementation.

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Section: Figure 5 Conceptual and Operational Frameworkmentioning

confidence: 64%

Unlocking Internet of Things (IoT) for Enhanced Knowledge Management and Organizational Performance: A Case Study of Diverse Industry Players in the Philippines

Sangalang,

Ancla,

Chong

et al. 2024

IJMABER

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“…Security for information sharing is another significant concern in the context of Big Data (Lokshina&Lanting,2018b,2018c.Manyonlineservicesrequireustoshareprivateinformation. However,beyondrecord-levelaccesscontrol,wedonotunderstandwhatitmeanstosharedata,how theshareddatacanbelinked,andhowtogiveusersfine-grainedcontroloverthissharing.…”

Section: Evaluation Of Ethical Issues In the Context Of Big Datamentioning

confidence: 99%

The IoT- and Big Data-Driven Data Analysis Services

Lokshina

Durkin

Lanting³

2018

International Journal of Knowledge Management

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Ubiquitous sensing devices, enabled by wireless sensor network (WSN) technologies, cut across every area of modern day living, affecting individuals and businesses and offering the ability to measure and understand environmental indicators. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT). The IoT provides the tools to establish a major global data-driven ecosystem with its emphasis on Big Data. Now business models may focus on the provision of services, i.e., the Internet of Services (IoS). These models assume the presence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated. Different business models may support potential opportunities to create revenue and value for various types of customers. This article contributes to the literature by considering, for the first time, business models, strategic implications and business opportunities for third-party data analysis services.

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“…Differential privacy does not increase the risk while participating in statistical database [17]. We are further motivated by the investigation of [18], which observes that on-demand, differential privacy protection of healthcare data and integrated approach of privacy protected clinical analytics are critical requirements to build IoT-enabled automated health screening system. It is witnessed that such a requirement is yet to be captured and a suitable solution is not proposed.…”

Section: Introductionmentioning

confidence: 99%

“…We achieve this privacy guarantee through differential privacy [17,23]. In fact, differential privacy is emerged as the appropriate model to protect healthcare data [18].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking

Ukil

Jara

Marín

2019

Sensors

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Remote and automated healthcare management has shown the prospective to significantly impact the future of human prognosis rate. Internet of Things (IoT) enables the development and implementation ecosystem to cater the need of large number of relevant stakeholders. In this paper, we consider the cardiac health management system to demonstrate that data-driven techniques produce substantial performance merits in terms of clinical efficacy by employing robust machine learning methods with relevant and selected signal processing features. We consider phonocardiogram (PCG) or heart sound as the exemplary physiological signal. PCG carries substantial cardiac health signature to establish our claim of data-centric superior clinical utility. Our method demonstrates close to 85% accuracy on publicly available MIT-Physionet PCG datasets and outperform relevant state-of-the-art algorithm. Due to its simpler computational architecture of shallow classifier with just three features, the proposed analytics method is performed at edge gateway. However, it is to be noted that healthcare analytics deal with number of sensitive data and subsequent inferences, which need privacy protection. Additionally, the problem of healthcare data privacy prevention is addressed by de-risking of sensitive data management using differential privacy, such that controlled privacy protection on sensitive healthcare data can be enabled. When a user sets for privacy protection, appropriate privacy preservation is guaranteed for defense against privacy-breaching knowledge mining attacks. In this era of IoT and machine intelligence, this work is of practical importance, which enables on-demand automated screening of cardiac health under minimizing the privacy breaching risk.

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Qualitative Evaluation of IoT-Driven eHealth

Cited by 9 publications

References 21 publications

Unlocking Internet of Things (IoT) for Enhanced Knowledge Management and Organizational Performance: A Case Study of Diverse Industry Players in the Philippines

Unlocking Internet of Things (IoT) for Enhanced Knowledge Management and Organizational Performance: A Case Study of Diverse Industry Players in the Philippines

The IoT- and Big Data-Driven Data Analysis Services

Data-Driven Automated Cardiac Health Management with Robust Edge Analytics and De-Risking

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