Extensive Survey on Cloud-based IoT-Healthcare and Security using Machine Learning

Ghosal, Puja; Das, Debanjan; Das, Indrajit

doi:10.1109/icrcicn.2018.8718717

Cited by 13 publications

(9 citation statements)

References 10 publications

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“…These technologies have made human life smarter by providing costeffective and easily accessible services [1,2]. These technologies have also had a dramatic impact on the healthcare industry, making the healthcare services prompt and extending their outreach [13,39,66]. But the digital revolution is also the harbinger of serious issues and challenges.…”

Section: Results and Findingsmentioning

confidence: 99%

“…This study discussed the security issues and challenges of the Internet of Things (IoT) systems. Ghosal et al [39] performed an extensive survey on cloud based IoT that used ML for data analysis and cyber security in healthcare. This study proposed a model for data analysis and secure data access.…”

Section: Existing Related Workmentioning

confidence: 99%

“…It immediately starts after the planning phase. It includes research identification, primary studies selection, quality assessment, data extraction, data synthesis (summarization) [33,[35][36][37][38][39]. Research identification ensures the retrieval of all the research studies related to the research questions.…”

Section: Conducting Phasementioning

confidence: 99%

See 2 more Smart Citations

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs). The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches. B.A. Kitchenham guidelines have been practiced as a manual to conduct this work. Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies. Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers. K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data. Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies. This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively. Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

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Section: Results and Findingsmentioning

confidence: 99%

Section: Existing Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…From a safety and defense viewpoint of IoT, cyber-attacks, mainly data breaches and identity theft, are growing; thus, necessitating real-time connected devices to support sufficient security and defense in an integrated way [10]. In the relevant literature, an intelligent intrusion-detection system tailored to the IoT environment was developed: a deep-learning algorithm can detect malicious traffic in IoT networks through simulating and providing evidence of scalability and interoperability between various IoT-running protocols upon network communication [11].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Kyriakopoulos

Ntanos

Anagnostopoulos

et al. 2020

IJERPH

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Everyday life of the elderly and impaired population living in smart homes is challenging because of possible accidents that may occur due to daily activities. In such activities, persons often lean over (to reach something) and, if they not cautious, are prone to falling. To identify fall incidents, which could stochastically cause serious injuries or even death, we propose specific temporal inference models; namely, CM-I and CM-II. These models can infer a fall incident based on classification methods by exploiting wearable Internet of Things (IoT) altimeter sensors adopted by seniors. We analyzed real and synthetic data of fall and lean over incidents to test the proposed models. The results are promising for incorporating such inference models to assist healthcare for fall verification of seniors in smart homes. Specifically, the CM-II model achieved a prediction accuracy of 0.98, which is the highest accuracy when compared to other models in the literature under the McNemar’s test criterion. These models could be incorporated in wearable IoT devices to provide early warning and prediction of fall incidents to clinical doctors.

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“…Table I provides a comparison between IoT, CC, and BGD and provides a brief overview of all three approaches. This will help in getting an overview of all three paradigms quickly [44][45][46][47][48][49][50][51][52][53][54]. Although these three approaches have their purpose and importance, however, the convergence of these three is very beneficial in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

Humayun¹

2020

IJACSA

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Although the Internet of things (IoT), cloud computing (CC), and Big Data (BGD) are three different approaches that have evolved independently of each other over time; however, with time, they are becoming increasingly interconnected. The convergence of IoT, CC, and BGD provides new opportunities in various real-time applications, including telecommunication, healthcare, business, education, science, and engineering. Together, these approaches are facing various challenges during data gathering, processing, and management. The focus of this research paper is to pinpoint the emerging trends in IoT, CC, and BGD. The convergence of these approaches and their impact on various real-time applications, benefits, and challenges associated with all these approaches, current industry trends, and future research directions with especial focus on the healthcare domain. The paper also provides a conceptual framework that integrates IoT, CC, and BGD and provides an IoT centric cloud infrastructure using BGD. Finally, this paper summarizes by providing directions for researchers and practitioners about how to leverage the benefits of combining these approaches.

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Extensive Survey on Cloud-based IoT-Healthcare and Security using Machine Learning

Cited by 13 publications

References 10 publications

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Internet of Things (IoT)-Enabled Elderly Fall Verification, Exploiting Temporal Inference Models in Smart Homes

Role of Emerging IoT Big Data and Cloud Computing for Real Time Application

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