The Internet of Things (IoT) Augmentation in Healthcare: An Application Analytics

Adhikary, Tathagata; Jana, Amrita Deb; Chakrabarty, Arindam; Jana, Saikat Kumar

doi:10.1007/978-981-13-8461-5_66

Cited by 22 publications

(11 citation statements)

References 18 publications

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“…Many researchers applied data mining techniques in healthcare on the cloud to use the power of resources in the cloud [12,15]. Knowledge mining tools are multidisciplinary because it uses machine learning, statistics, and database.…”

Section: Healthcare Data-based Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

IoT Wearable Devices for Health Issue Monitoring Using 5G Networks’ Opportunities and Challenges

Ismail

Abdelrazek

El-Henawy

2021

Blockchain for 5g-Enabled IoT

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Section: Healthcare Data-based Applicationsmentioning

confidence: 99%

“…The healthcare devices can send the streaming data to smartphones which can contain mobile applications to collect the data to the cloud or just to show the users the sports activities that impact their calories and health status [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

IoT Wearable Devices for Health Issue Monitoring Using 5G Networks’ Opportunities and Challenges

Ismail

Abdelrazek

El-Henawy

2021

Blockchain for 5g-Enabled IoT

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“…Internet of things [10][11][12] refers to the astronomical physical devices connected to the internet that collects and shares data. With the ubiquity of wireless networks and low-price hardware devices, something as small as a tiny toy to something as big as an aircraft can be turned into a part of IoT.…”

Section: Internet Of Things (Iot)mentioning

confidence: 99%

Designing of an Immaculate Smart City with Cloud Based Predictive Analytics

Sengupta¹,

Chinnasamy²

2020

IJCDS

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The major objective of a smart city is to provide ripest and rightest services to its residents with minimal human intervention. The data collected from various sensors, receptors and receivers can be stored in a reliable, cost effective and manageable cloud storage. Predictive analytics such as predictive modelling and machine learning can be used to analyze the latest and past data from the cloud to forecast the near and far future. The predictive analytics will immediately send notifications and the appropriate actions to be taken to the concerned system so that the residents can get equable life. Waste management such as sewage management and trash management are one of the major exertions in a smart city. In this paper, predictive analytics is applied on the cloud data collected from various sensors attached on the waste management resources such as trash bins and sewage pipes. Firstly, group of trash bins and sewage pipes are attached with radio wave sensors which in turn connected to an IoT enabled receiver to provide cost effective data collection. Secondly, the data from the receivers are stored in a cloud. Thirdly, predictive analytics using K-means clustering are proposed for applying on the data in the cloud. Finally, actions like redirecting the trash collection truck and sewage clearing robot based on the priority can be done automatically and long-term actions can be directed to the concerned authorities with notifications and alerts to take necessary actions. In this paper, designing of an immaculate smart city is proposed which can be achieved by developing IoT based trash collection and sewage cleaning system. For analysis purpose synthetic data is considered where levels for garbage cleaning bins at different time and liquid levels of drainage pipes at different time are represented with different numbers.

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“…Any exposure of such sensitive data by unauthorized parties is considered a violation of federal health insurance portability and accountability act rules. Moreover, any tampering or modification of this data can lead to fatal outcomes [10]. This makes trust in the received data and its integrity from the IoT sensors very critical.…”

Section: Introductionmentioning

confidence: 99%

An FPGA Implementation of Privacy Preserving Data Provenance Model Based on PUF for Secure Internet of Things

Hamadeh

Tyagi

2021

SN COMPUT. SCI.

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Data provenance to maintain data integrity and authenticity is a significant challenge in the Internet of Things (IoT) environments. Data provenance protocols must communicate provenance metadata while preserving its privacy. This enables trust in IoT systems expanding its acceptance within society. In this paper, we present a scheme to combine data provenance and privacy-preserving solutions. Our scheme utilizes Physical Unclonable Functions (PUFs) with non-interactive zero knowledge proof to provide trustworthy and dependable IoT systems. An IoT device can anonymously send data to the corresponding server associated with the proof of ownership. We propose a privacy-preserving data provenance protocol. This protocol was synthesized with Altera Quartus. It was implemented on an Altera Cyclone IV FPGA to demonstrate its practicality and feasibility. Most of the protocol steps take time of the order of 40 μ s establishing its practicality. Keywords Data provenance • Privacy-preserving • Internet of thing • PUFThis article is part of the topical collection "Technologies and Components for Smart Cities" guest edited by Himanshu Thapliyal, Saraju P. Mohanty, Srinivas Katkoori and Kailash Chandra Ray.

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The Internet of Things (IoT) Augmentation in Healthcare: An Application Analytics

Cited by 22 publications

References 18 publications

IoT Wearable Devices for Health Issue Monitoring Using 5G Networks’ Opportunities and Challenges

IoT Wearable Devices for Health Issue Monitoring Using 5G Networks’ Opportunities and Challenges

Designing of an Immaculate Smart City with Cloud Based Predictive Analytics

An FPGA Implementation of Privacy Preserving Data Provenance Model Based on PUF for Secure Internet of Things

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