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Enhancing Telehealth Big Data Through Fog Computing Harishchandra Dubey University of Rhode Island DigitalCommons@URI Department of Electrical, Computer, and Department of Electrical, Computer, and Biomedical Engineering Faculty Publications Biomedical Engineering 2015 Fog Data: Enhancing Telehealth Big Data Through Fog Computing Harishchandra Dubey Jing Yang See next page for additional authors Follow this and additional works at: https://digitalcommons.uri.edu/ele_facpubs The University of Rhode Island Faculty have made this article openly available. Please let us know how Open Access to this research benefits oy u. This is a pre-publication author manuscript of the final, published article. Terms of Use This article is made available under the terms and conditions applicable towards Open Access Policy Articles, as set forth in our Terms of Use. Authors Harishchandra Dubey, Jing Yang, Nick Constant, Amir Mohammad Amiri, Qing Yang, and Kunal Mankodiya Fog Data: Enhancing Telehealth Big Data Through Fog Computing Harishchandra Dubey#, Jing Yang, Nick Constant, Amir Mohammad Amiri, Qing Yang+, Kunal Makodiya* Department of Electrical, Computer, and Biomedical Engineering University of Rhode Island, Kingston, RI 02881, USA #[email protected], [email protected],*[email protected] ABSTRACT decision-making [1]. For example, in the healthcare domain, The size of multi-modal, heterogeneous data collected through various telehealth enables the use of sensors within or on the human sensors is growing exponentially. It demands intelligent data body. Wearable sensors such as ECG and activity monitors are reduction, data mining and analytics at edge devices. Data a specific type of medical sensors placed on the human body compression can reduce the network bandwidth and transmission allowing non-invasive, unobtrusive, 24/7 data collection for power consumed by edge devices. This paper proposes, validates and health monitoring. The increasing number of ambient devices evaluates Fog Data, a service-oriented architecture for Fog computing. installed in homes that surround the human body provide The center piece of the proposed architecture is a low power embedded telehealth interventions to citizens seeking affordable computer that carries out data mining and data analytics on raw data collected from various wearable sensors used for telehealth healthcare while remaining in touch with medical practitioners applications. The embedded computer collects the sensed data as time remotely. Such telehealth application is a typical example of series, analyzes it, and finds similar patterns present. Patterns are big data application that collects a large volume of data with a stored, and unique patterns are transmited. Also, the embedded variety of information requiring real time and fast processing to computer extracts clinically relevant information that is sent to the provide the best healthcare. There are several challenges in cloud. A working prototype of the proposed architecture was built and deploying telehealth applications. Firstly, designing used to carry out case studies on telehealth big data applications. information sensing nodes in body sensor networks (BSNs) is Specifically, our case studies used the data from the sensors worn by a challenge that is eased by using wearable sensors, e.g., patients with either speech motor disorders or cardiovascular smartwatches. The second challenge is a collection, storage and problems. We implemented and evaluated both generic and application specific data mining techniques to show orders of analysis of large amount of heterogeneous, multi-modal, magnitude data reduction and hence transmission power savings. distributed, and scalable data sets, known as big medical data. Quantitative evaluations were conducted for comparing various data It is inefficient and impractical in several applications to use mining techniques and standard data compression techniques. The traditional architectures and algorithms for storing and obtained results showed substantial improvement in system efficiency processing such data. The third challenge is energy efficiency using the Fog Data architecture. of wearable and portable edge devices used in a telehealth CCS Concepts solution. Since BSNs are powered by batteries, to provide •Networks ➝ Cloud computing •Social and professional topics ➝ uninterrupted monitoring of patients, these batteries should not Remote medicine Hardware ➝ Signal processing systems be frequently recharged. Therefore, low power consumption is Hardware➝Sensors and actuators •Applied computing ➝ Health critical for BSNs. Typically, data storage and data transmission care information systems consume a significant amount of energy suggesting the benefits of quick and preliminary data analytics to reduce the amount of Keywords: Big Data; Body Area Network; Cyber-physical necessary data to be stored and transmitted. Systems; Edge Computing; Fog Computing; Internet of Things; Wearable Devices. 1. INTRODUCTION With the increasing use of wearable sensors in healthcare and biomedical applications, we are living in the data-driven world. As a result, we are presented with many challenges in dealing with big data. "Big Data" is characterized by high-volume, high-variety, and high-velocity information that demands efficient and innovative processing for enhanced insight and ThisPermission material tois makepresented digital to orensure hard copiestimely disseminationof all or part of of this scholarly work for personal Figure 1. Fog Data, a service-oriented architecture to reduce andor classroom technical work.use is Copyright granted withoutand all rightsfee provided therein arethat retained copies areby not made or storage requirements and to increase the overall efficiency of the authors or by the respective copyright holders. distributed for profit or commercial advantage and that copies bear this notice telehealth big data solutions. Theand original the full citation citation of on FogData the first paper page. is: Copyrights for components of this work Harishchandraowned by others Dubey, than Jing ACM Yang, must Nick Constant,be honored. Amir Abstr Mohammadacting with credit is Amiri,permitted. Qing Yang,To copy and otherwise, Kunal Makodiya. or republish, 2015. to Fog post Data: on servers Enhancing or to redistribute The authors are grateful to the anonymous reviewers for providing Telehealth Big Data Through Fog Computing. In Proceedings of the to lists, requires prior specific permission and/or a fee. Request permissions comments and suggestions that improved the quality of the paper. This ASE BigData & SocialInformatics 2015 (ASE BD&SI '15). ACM, New from [email protected]. research is supported in part by NSF grants CCF-1439011 and CCF-1421823. York, NY, USA, , Article 14 , 6 pages. Any opinions, findings, and conclusions or recommendations expressed in this DOI=http://dx.doi.org/10.1145/2818869.2818889ASE BD&SI 2015, October 07-09, 2015, Kaohsiung, Taiwan © 2015 ACM. ISBN 978-1-4503-3735-9/15/10 $15.00 material are those of the author(s) and do not necessarily reflect the views of DOI:http://dx.doi.org/10.1145/2818869.2818889 the National Science Foundation. Figure 2. A system architecture of Fog Data. Fog Data, a new system architecture based on Fog computing 2.1. TeleHealth and Medical Big Data concept, is presented in this paper as a means to tackle these An increasing population with chronic diseases and old age challenges. The proposed architecture is a service-oriented Fog along with the rise in medical costs has created a demand to computing architecture interfacing in-home telehealth devices extend the healthcare services from hospital to home with a facilitating the collection, storage, and analysis of large amount focus on efficiently managing disease and overall wellbeing of of heterogeneous, multimodal, distributed and scalable data sets patients. In the last decade, this scenario has given rise to a new for person-centered health monitoring. The unique feature of Fog Data architecture is its ability to carry out onsite data paradigm called "telehealth" that allows patient health analytics to reduce the amount of data to be stored and monitoring and disease management in non-clinical settings transmitted to the cloud. As shown in Figure 1, wearable such as private homes, nursing homes, and assisted living. sensors and ambient devices at home collect the necessary Telehealth infrastructure consists of Body Sensor Network information as raw data. The raw data is generally in the form (BSN) combining wearable sensors and personal area network of time series signals that are transmitted to energy efficient [2]. To further illustrate telehealth here are some of the notable embedded computer, referred to as Fog computer. The Fog wearable telehealth systems: 1) VitalConnect is a band-aid style computer handles transmitting necessary data to the cloud after healthpatch to collect continuous vital signs in remote settings preliminary analysis and filtering. [3]. 2) Philips has launched an adhesive patch to monitor To validate the proposed architecture, we have designed and Chronic Obstructive Pulmonary Disease (COPD) [4]. 3) Fitbit, implemented a system prototype based on Intel® Edison a wristworn sensor contains a motion sensor for estimating embedded processor connected to the wearable telehealth health indices such as step counting, calories, and sleep quality systems. Using the working prototype, we have carried out [5]. extensive experiments using real data sets collected from Due to sensor-rich infrastructure, telehealth generates medical patients
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