An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

doi:10.1080/17517575.2016.1277558

Cited by 58 publications

(33 citation statements)

References 37 publications

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“…Typical examples include sensing systems for facilitating longterm mobility of the elderly [36], detecting early symptoms of dementia in elderly people living alone [37], detecting aging related neurological diseases [38], detecting voice disorders [39], monitoring foot pressure wirelessly [40], detecting vital signals [41], and devices for pregnant women to check their and fetus vitals [42]. In addition to patient monitoring systems, IoT applications are also used in preventive management systems, and detecting diseases such as zika, diabetes, and chikungunya [43] [44].…”

Section: Cluster 3: Applicationsmentioning

confidence: 99%

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Dantu¹,

Dissanayake²,

Nerur³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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The rapid integration of the physical and cyber worlds through the Internet of Things, or IoTs, is transforming our lives in ways that we could not have imagined even five years ago. Although they are still in their infancy, IoTs have already made a significant impact, particularly in the healthcare domain. The purpose of this study is to unravel key themes latent in the sparse but growing academic literature on the application of IoTs in healthcare. Specifically, we performed topic modeling and identified five dominant clusters of research, namely, privacy and security, wireless network technologies, applications, data, and smart health and cloud. Our results show that research in healthcare IoT has mainly focused on the technical aspects with little attention to social concerns. In addition to categorizing and discussing the topics identified, the paper provides directions for future research.

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Section: Cluster 3: Applicationsmentioning

confidence: 99%

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Dantu¹,

Dissanayake²,

Nerur³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Fog computing was coined by Cisco in 2012 [21]. It is a framework that complements the cloud computing for decentralizing the resources in data centers towards users for improving the quality of service and user experience [49]. However in fog computing framework, processing of different services are not only concentrated in cloud data centers [44] [51] [56].…”

Section: Fog Computingmentioning

confidence: 99%

“…It introduces the facility of local processing leading to reduction in data size, lower latency, high throughput and power efficiency of the cloudbased systems. Fog computing technology has been successfully implemented in smart cities [31] and healthcare [23,22,42,49,50,51]. Fog devices are embedded computers such as Intel Edison or Raspberry Pi that acts a gateway between cloud and mobile 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 devices.…”

Section: Fog Computingmentioning

confidence: 99%

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GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Barik

Dubey

Mankodiya

et al. 2018

J Ambient Intell Human Comput

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Spatial Data Infrastructure (SDI) is an important framework for sharing geospatial big data using the web. Integration of SDI with cloud computing lead to emergence of Cloud-SDI as a tool for transmission, processing and analysis of geospatial data. Fog computing is a paradigm where embedded devices are employed to increase the throughput and reduce latency at the edge of network. In this study, we developed and evaluated a Fog-based SDI framework named GeoFog4Health for mining analytics from geo-health big data. We built a prototype using both Intel Edison and Raspberry Pi for performing a comparative study. We performed a case study on Malaria vector borne disease positive maps of Maharastra state in India. The proposed framework had provision of lossless data compression. Also, overlay analysis of geospatial data could be performed. In addition, we discussed energy saving, cost analysis and scalability of proposed framework for efficient data processing. We compared the performance of proposed framework with state of the art Cloud-SDI in terms of analysis time. Results and discussions showed the efficacy of proposed system for enhanced analysis of geo-health big data generated from a variety of sensing frameworks.

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“…A Fog-based zika virus outbreak monitoring system was also established where a fuzzy k-nearest neighbour is used to predict the possible infected users according to their clinical symptoms. Moreover, they implemented a Google [27]. They also proposed another similar model to prevent zika virus by using a naive Bayesian network (NBN) as the classifier [28].…”

Section: Web-based Systems For Healthcare Monitoringmentioning

confidence: 99%

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

et al. 2018

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Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every year in the past decade. But recently, this disease has been witnessed in other regions to a large extent and can become an epidemic very soon. In this paper, a new fog computing based e-Healthcare framework has been proposed to monitor the KFD infected patients in an early phase of infection and control the disease outbreak. For ensuring high prediction rate, a novel Extremal Optimization tuned Neural Network (EO-NN) classification algorithm has been developed using hybridization of the extremal optimization with the feed-forward neural network. Additionally, a location based alert system has also been suggested to provide the global positioning system (GPS)-based location information of each KFD infected user and the risk-prone zones as early as possible to prevent the outbreak. Furthermore, a comparative study of proposed EO-NN with state of art classification algorithms has been carried out and it can be concluded that EO-NN outperforms others with an average accuracy of 91.56%, a sensitivity of 91.53% and a specificity of 97.13% respectively in classification and accurate identification of risk-prone areas.

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An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

Cited by 58 publications

References 37 publications

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

Exploratory Analysis of Internet of Things (IoT) in Healthcare: A Topic Modeling Approach

GeoFog4Health: a fog-based SDI framework for geospatial health big data analysis

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment

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