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Healthcare Solution based on Machine Learning Applications in

IOT and Edge Computing

Dr S. Mohan Kumar1 and Darpan Majumder

2

1 Associate Professor, Dept of ISE, NHCE, Bangalore

2 Research Scholar, Dept of ISE, NHCE Research Centre, VTU

ABSTRACT

Cloud computing and Internet of Things (IOT) are two technologies which though not directly related

have a significant role in our day to day living. These two technologies can be merged together to solve

problems in domains of healthcare, surveillance, assisted living, agriculture, asset tracking. However,

Cloud computing is not an ideal choice for applications that require real time responses due to high

network latency. Hence a new technique “edge computing” was introduced that would push the

computation to the “edge of the network” thereby reducing network latencies. Edge computing can

address concerns involving real time responses, battery power consumption, bandwidth cost as well as

data safety and privacy. In this paper we shall consider the applications of edge computing and IOT in the

field of healthcare. In this research especially, we are exploring the possibilities of integration of

cloud/edge computing and Machine Learning paradigms into a Distributed computing based IOT

Framework. The target is to able to extract relevant information of interest among the huge data that is

typically generated by the front-end Sensor frameworks in IOT devices. Some intelligence can be

included in the front-end module itself to enable the front-end to take a decision on data priority.

Guidance regarding how to achieve this can be provided by a backend IOT server. It is proposed that the

backend server has Machine Learning based implementations to be able to automatically learn data

signatures of interest based on the data it has already received

KEYWORDS: Edge Computing, Cloud Computing, IOT, Healthcare, Body Sensor Network, Big Data

Analysis, Machine Learning, Data Preprocessing, Scheduling Algorithms, Real Time Systems, Task

Level Parallelism, Context Aware Computations, Data Load Prediction Modeling, Neural Networks,

Heuristic Algorithms, SPO2, ECG, EMG, MQTT, CoAP, MEC, QOS, NFV, DSVRG, ETSI, SDN, QOR,

HRV, CTLDA, Assisted Living, Agriculture, Asset Tracking, Battery Power Consumption, Data Safety

and Privacy

INTRODUCTION

IoT (Internet of Things) is a framework that uses technologies like sensors, network communication,

artificial intelligence and bigdata to provide real life solutions. These solutions and systems are designed

for optimal control and performance.

Internet of Things (IOT) is a happening Technology given the advancements in allied technologies like

Sensor, Communication and Computing. With these advancements, any leaf node device of today is

capable of “sensing” its surroundings, can perform computation and is addressable by a network address

over a wireless network. This enables solutions to be developed that can map “real life” entities to a

corresponding virtual object. These virtual objects can communicate with each using available

communication technologies and keep the “real life” entity informed about the state of “things”. A control

mechanism between the “real life” entities and the virtual objects is also included as part of this

framework/solution.

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Cloud computing comprises topics related to providing computing services and utilities like servers,

storage, databases, networking, software, analytics etc over the internet. Depending on the requirements

of the end user, various services can be provided from a remote location.

Edge computing is a subset of cloud computing where multitude of these services are provided from a

location that is geographically closure to the end user and can thereby serve the purpose of eliminating

network latency.

A typical IOT model comprises an end node device that can communicate with a back-end

computation/data center over a communication medium (Generally Wireless). The communication

channel mostly uses IOT protocols like MQTT/CoAP. Data and Control Messages can be seamlessly

exchanged across the IOT endpoint and a Data Center Server. The Endpoint device can provide ambient

condition information to a Data Centre using various Sensors depending on the domain of interest and get

back information/instructions from backend to perform actions. The Back-End Servers are generally

powerful computing resources and can use computation intensive algorithms to process the data gathered

from the End point devices.

The main challenges around IOT solutions are

The amount of data generated by the sensors are huge. Extraction of relevant information from

the captured data is a challenge. This effort requires development of an algorithm that can extract

abnormalities in captured data for body sensor networks. There have major research scopes in

field of machine learning and sampling algorithms

Given the fact that computation intensive operations are pushed to back end, optimization of Real

Time Response is an area of improvement. Optimizing the amount of data transfer is an area of

interest.

Decentralization of computation. With more and more devices being IOT capable, computation at

one point will create bottleneck in network resources. The computation needs to be distributed

and Task Level Parallelism needs to be achieved. Computation and resource distribution

algorithms are areas of major research interest in this field

Security of the IOT devices.

Power Consumption at End Point Devices. Battery consumption is one of the major concern in

IOT devices as charging these devices may not be an easy affair. This problem is generally solved

by offloading tasks to a back-end server and saving battery power that would have been otherwise

required for in-house computing. This provided a major impetus to research in the domains of

decentralization of computation

Edge computing facilitates network response times, aids decentralization and can also address security

concerns. This is because a large part of the critical computation can now be performed at the “edge

nodes” which would interact with cloud periodically. This provides the facilities of cloud computing sans

its disadvantages. Coupled with Machine learning and Big data tools, high efficacy real time solutions can

be developed.

In this research, we are exploring the possibilities of integration of cloud/edge computing and Machine

Learning paradigms into a Distributed computing based IOT Framework. The target is to able to extract

relevant information of interest among the huge data that is typically generated by the front-end Sensor

frameworks in IOT devices. Some intelligence can be included in the front-end module itself to enable the

front-end to take a decision on data priority. Guidance regarding how to achieve this can be provided by a

backend IOT server. It is proposed that the backend server has Machine Learning based implementations

to be able to automatically learn data signatures of interest based on the data it has already received.

As a use case of the above, we plan to apply the above concepts to Medical applications. There has been a

plethora of medical sensors currently available like

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SPO2 Sensor

ECG Sensor

Airflow Sensor

Temperature Sensor

Sphygmomanometer

Body Position Sensor

Galvanic Skin Response Sensor

Glucometer

EMG Sensor

Determination of whether a data is critical or not should be taken by the leaf device with guidance from

the backend cloud/edge server. The cloud/edge server should be able to learn information from the

current leaf node as well as other nodes it serves and provide guidelines to the endpoint device regarding

priority of decisions. The decision would be based on generic data available as per medical records as

well as personalized data generated.

Real life applications of the above approach would be:

Determine sudden blood pressure fluctuations. Analyze these fluctuations and check whether these

are aberrations or not and alert emergency services accordingly. Such a data is critical and needs real

time attention and therefore should be prioritized over others. Some data patterns might be normal

for some patients but not for others.

Determine body posture movements of the patient and check when the patient is requiring attention

for movement. This can be used for assisted living solutions. [20]

Determine epilepsy seizure based on the analysis from data available from electroencephalography.

[29] Work on algorithms so that the seizure can be detected at a computing resource near the sensor

Work on an algorithm/solution that will prioritize transmission and processing of critical data over

non-critical data

Guide an ambulance to appropriate health center that is the closest, having relevant facilities based

on patient’s health checkup data collected by Body Sensor Networks

LITERATURE SURVEY

In this section, we shall consider the prior arts literature related to edge computing and IOT applications

in healthcare

IOT Applications in Healthcare:

Vikas Vippalapalli et al [16]: Year of Publication: 2016 This proposal is for a low-cost patient healthcare monitoring system model based on lightweight wearable

sensors. These sensing nodes are used for real time detection and analysis of healthcare data of patients.

The devices are designed to be able to collect and share the gathered data among themselves thereby

facilitating information analysis and storage. This also eliminates manual in-efficiencies in the process.

For patient data collection, a Audrino based wearable device with Body Sensor Networks is proposed.

This is integrated with “Labview” to provide remote monitoring capability

Maheswar Rao Kinthada et al [17]: Year of Publication: 2017 This research proposes a method/framework that can be used to monitor patients medicine intake. It

provides a mechanism to dispense prescribed medications as well as track medication history including


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missed dosage. The framework alerts the patient regarding medication consumption using alarms. In case

of failure, medical staff is also made aware of the missed dosage.

UtkarshaniJaimini [18]: Year of Publication: 2017 This research proposes a method/framework that can be used to monitor patients medicine intake. It

provides a mechanism to dispense prescribed medications as well as track medication history including

missed dosage. The framework alerts the patient regarding medication consumption using alarms. In case

of failure, medical staff is also made aware of the missed dosage.

R.N.Kirtana et al [19]: Year of Publication: 2017 Heart Rate Variability (HRV) measures variation in time interval between consecutive heart beats. HRV

analysis can detect Cardiovascular diseases, Diabetic Mellitus, disease states associated with Autonomic

Dysrhythmia like Hypertension and different chronic degenerative medical conditions. Monitoring HRV

data will help detection of such diseases. In this research work, the authors propose a low-cost and easy to

use Remote HRV Monitoring System based on Internet of Things (IoT) technology for Hypertensive

patients. In this proposal, HRV parameters are calculated based on the data retrieved using Wireless

Zigbee based pulse sensor. This Arduino based systems transmits the data retrieved from monitoring the

patient to a backend server using MQTT an IOT protocol. The application server collects HRV data and

plots graphs.

Shuang Liu [20]: Year of Publication: 2017 This research explores applications of IoT for surveillance and monitoring that is used in every-day life.

These include applications/solutions like security surveillance, health-care, independent living, etc. To

address the problems seen due to data captured from various viewpoints and heterogenous sensors in IoT,

the author has proposed a novel method of class-constrained transfer linear discriminant analysis. This

method helps in extraction of invariant features from the captured data. The research work focusses on

crossview action recognition of IOT monitoring systems and proposes a model that solves the problem of

“human action” detection due to images captured at different angles using the ability of extraction of

feature invariant metrics. The experimental results have demonstrated that the proposed CTLDA can

achieve better results than the state-of-the-art methods.

S.Pinto et al [21]: Year of Publication: 2017 Due to an increase in the population of the aged across the world there has been a growing requirement to

provide solutions that provide living assistance to the elderly population. In this aspect in might be said

that the Internet of Things can provide a new aspect to modern healthcare by providing a more

personalized, preventive and collaborative form of care. This research work presents a living assistance

based IoT solution for the elderly that can monitor and register patient’s vital information as well as

provide mechanisms to trigger alarms in emergency situations. The research work proposes a solution

comprising a wrist band that can connect to the cloud server to monitor and assist elderly people. It claims

to be low power/cost solution with Wireless communication capabilities

P. Dineshkumar et al [22]: Year of Publication: 2016 This research explores the usage of Big Data methods for analyzing data capture by Health Framework

Sensors with the usage of IOT Cloud based solutions. Hadoop Framework is used for analysis of the

medical data thus captured and utilizing proper alert methods a summary of the critical information is

provided to a physician in Real Time. Methods to extract critical data from a BSN Sensors (Body Sensor

Networks) have been explored here. The physician will be able to get real time patient information over

any connectivity service. This should improve healthcare standards

Sourav Kumar Dhar et al [23]: Year of Publication: 2014 This research proposes a solution so that all the Health care sensors that are used for monitoring can

function together. This is done by removing interference among each other and the consequent distortion


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of healthcare data. Implementation of such a prototype is discussed in this proposal. For any healthcare

monitoring system to function properly, maintaining the sample rate and delay requirement of each sensor

of the monitoring equipment is mandatory. As resources are limited, this proposal provides a way such

that the ideal sampling rate is maintained and the quality of healthcare data is good. It has been shown

that by interleaving the data per the required sampling rate and dividing larger data with maximum

allowed data size the desired sampling for various sensors can be maintained simultaneously ensuring

quality data transfer and thereby making effective usage of Network Bandwidth

Mohammad-Parsa Hosseini et al [29]: Year of Publication: 2017 This research work focusses on determining characteristics from a Brain Computer Interface using

various sensors like electroencephalography (EEG) and resting state-functional magnetic resonance

imaging (rs-fMRI) along with Diffusion Tensor Imaging for data collection from an epileptic brain. The

proposed solution uses edge computing methods to provide a context aware real-time solution using

invasive as well as noninvasive techniques for monitoring, evaluating and regulating an epileptic brain.

This facilitates detection as well as ensures prompt medical attention (surgical or otherwise) in case of

occurrence of an epilepsy seizure. The main goal of this research is to be able to predict an “ictal onset”

Edge Computing Survey Papers:

Tarik Taleb et al [7]: Year of Publication: 2017 This research work is a survey on MEC (Mobile Edge Computing) that discusses the major enabling

technologies in this domain. It explores MEC deployment considering both the perspectives of individual

services as well as a network of MEC platforms supporting mobility. Different possible MEC deployment

options are also discussed here. It also delves into analysis of a MEC reference architecture and its main

deployment scenarios that can offer multitenancy support for application developers, content providers,

and third parties. This work also details out the current standardization activities and future open research

problems.

Somayya Madakam et al [24]: Year of Publication: 2015

The main objective of this work is to provide an overview of Internet of Things, architectures, and vital

technologies and their usages in our daily life. Major observations made in this document are

a. There is no standard definition of IoT

b. Universal standardizations are required in architectural level

c. Technologies are varying from vendor-vendor and hence there is a need of interoperability.

d. For better global management, there is a need to build standard protocols.

Koustabh Dolui et al [34]: Year of Publication: 2017

This work explores the efficacy of different types of Edge computing models namely Fog Computing,

Cloudlet and Mobile Edge computing and compares their feature sets. With lot of attention towards IOT

and applications that need Real Time Reponses, edge computing has become an area of interest for

researchers

Yuyi Mao et al [40]: Year of Publication: 2017

This paper provides a survey on the state of art technologies for Mobile Edge computing with a focus on

optimization of radio (network) and computational resources.

OBSERVATIONS FROM LITERATURE SURVEY

There have been great advancements in computing, connectivity and sensing technologies in recent

years. Low cost Health bands capable of sensing human body conditions (Body Sensor Networks) are

now powered with capabilities of computing and connectivity. [56][16][29]. A lot of healthcare

applications are based now based on IOT paradigm


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For some real-time applications “cloud computing” is an overhead due to its high and unpredictable

network latency and hence the idea of edge computing that brings computation closer to the User

Device is gaining in popularity. [13][14][34][43]. It might be noted that in most cases “edge

computing” is a supplement and not a replacement of “cloud computing”

Context aware computations are becoming important in IOT based solutions. [29][20]

HEALTHCARE SOLUTION MODEL BASED ON EDGE COMPUTING

AND IOT

This section focusses on how to intelligently use and prioritize network resources in a IOT framework

over a secure and trust worthy transmission channel for a healthcare based application. This can be

achieved by efficiently preprocessing the input data received from the sensor frameworks at the leaf

devices. For this the leaf device, might take assistance of the back-end cloud servers that has access to

heavy computing resources. The back end can perform the heavy number crunching and advise the end

point device regarding the specific preprocessing to be done to be able to prioritize incoming data from

sensor frameworks. The backend can use Machine Learning and data mining concepts to extract

signatures from incoming sensor data and accordingly provide medical interpretation based on the

captured data. Using this the frontend device can provide an assessment of the patient health condition.

The implementation can be extended to provide a method so that only prominent fluctuations can be

provided to the back end where a physician can analyze the data and conclude. This will help in remote

diagnosis and provide better rural medication where the doctor is away.

It has been observed that due to network latencies cloud computing does not fit into areas that require real

time low latency responses. This is primarily due to the high latency of decisions. This has led to a new

distributed computing architecture proposal in the form of “edge computing” where some part of the

computations can be done at the IOT device or “edge” devices rather than having everything computed in

the cloud. The primary focus of this research would be to marry the concepts of cloud computing and IOT

thereby focusing around improvements in edge computing techniques mainly for healthcare domain


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An IOT Endpoint Has multiple sensors connected to it.

An IOT endpoint is connected to a back-end server over a Wireless network

The IOT Endpoint can communicate with the IOT Backend via IOT protocols (MQTT, CoAP)

The backend server

Can perform computation intensive tasks as it has access to high end computing resources.

For computation tasks, the backend can employ various techniques like big data analysis,

Machine learning, neural networks

Can be cloud based. However, our focus would be on edge computing

Can send notifications to the IOT Endpoint

An MQTT client would be running on the IOT Endpoint and an MQTT server would be running in the

cloud (Edge Server). This will provide the transport protocol necessary for communication between the

IOT Endpoint and the data server.

As discussed earlier due to high network latencies cloud computing is not considered and optimum

solution for Real Time Data Analysis. For this purpose, Edge Computing architecture, has been

introduced. Our basic idea can be extended to edge computing architecture where the endpoint device can

perform computation based on guidance from an edge point device which in turn can get data from Cloud

servers. MQTT protocol fits the bill here as well. The IOT device can have an MQTT client, the MQTT

server should be in edge device which in turn can request for other network services from the cloud.

COAP can also be used as an alternative IOT protocol

Tools/Methods that can be used for this implementation

MQTT as IOT protocol


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Hadoop as bigdata framework

Tensor Flow for Machine learning

CONCLUSION

Recent advancements in “connectivity” and “sensing” technologies in endpoint devices coupled with

cloud computing has brought in research interests in IOT based solutions for Healthcare, assisted living,

agriculture etc. As a part of this study we will be considered the some of the applications on Edge

Computing in IOT in general, while focusing on Healthcare technologies in the later part of this paper.

We observed that many healthcare solutions require Real Time decision making capabilities. Such a

solution does not prefer cloud computing because of the network delays and latencies that are associated

with it. A working model of such a solution has also been proposed. Such a solution can guide the

endpoint IOT device using IOT protocols like MQTT and at the same time glean information from cloud

and perform the offloaded operations. As a part of our further research we plan to consider multiple IOT

edge servers, their interactions with each other and the End Point devices as well.

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VentsislavTrifonov, IEEE ICUFN 2017


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53. Mobile Edge Computing-Enabled Channel-Aware Video Streaming for 4G LTE by Chen-Chi Wang,

Zih-Ning Lin,Shun-Ren Yang and Phone Lin, 2017 IEEE

54. Design of a High-Performance System for Secure Image Communication in the Internet of Things by

ELIAS KOUGIANOS, SARAJU P. MOHANTY,GAVIN COELHO, UMAR ALBALAWI, AND

PRABHA SUNDARAVADIVEL, SPECIAL SECTION ON SECURITY AND RELIABILITY

AWARE SYSTEM DESIGN, FOR MOBILE COMPUTING DEVICES, Pages 1222 - 1242, Volume

4, 2016

55. Localization of Health Centre Assets Through an IOT Environment (LOCATE), by T. Dylan

McAllister, Samy El-Tawab and M. Hossain Heydari, 2017 IEEE

56. Overbooking Radio and Computation Resources in mmW-Mobile Edge Computing to Reduce

Vulnerability to Channel Intermittency by Sergio Barbarossa, Elena Ceci, Mattia Merluzzi, 2017

IEEE

57. Datapath scheduling using dynamic frequency clocking, by S. P. Mohanty, N.Ranganathan, and V.

Krishna, in Proc. IEEE Comput. Soc. Annu.Symp. VLSI, Apr. 2002, pages. 58 - 63.

58. Content Centric Peer Data Sharing in Pervasive Edge Computing Environments by Xintong Song,

Yaodong Huang, Qian Zhou, Fan Ye, Yuanyuan Yang and Xiaoming Li, Pages 287 - 297, 2017 IEEE

37th International Conference on Distributed Computing Systems

59. Dr. Mohan Kumar S, Karthikayini, LNW-A System Model For A High Quality Effective E-Learning

Using Cloud Environs, International Journal of Current Research and Review, Volume 7, Issue 23,

21-25. .

60. Dr. Mohan Kumar S, Ayurveda Medicine Roles In Healthcare Medicine, And Ayurveda Towards

Ayurinformatics, International Journal of Computer Science and Mobile Computing, Volume 4, Issue

12, 35-43.

61. Dr.S Mohan Kumar, R.Jaya, A Survey On Medical Data Mining – Health Care Related Research And

Challenges, International Journal of Current Research, Volume 8, Issue 01, 25170-25173.

62. R.Jaya, Dr S Mohan Kumar, A Study On Data Mining Techniques, Methods, Tools And Applications

In Various Industries, International Journal of Current Research & Review, Volume 8, Issue 04, 35-

43.

63. Revathi Y, Dr S Mohan Kumar, Efficient Implementation Using RM Method For Detecting

Sensitive Data Leakage In Public Network, International Journal of Modern Trends in Engineering

and Research, Volume 3, Issue 04,515-518. April 2016, Google Scholar & Other International

Databases.

64. Revathi Y , Dr S Mohan Kumar, Review On Importance And Advancement In Detecting Sensitive

Data Leakage In Public Network, International Journal Of Engineering Research And General

Science, Volume 4, Issue 02,263-265. April 2016, Google Scholar & Other International

Databases.

65. Revathi Y, Dr S Mohan Kumar, A Survey On Detecting The Leakage Of Sensitive Data In Public

Network, International Journal of Emerging Technology and Advanced Engineering, Volume 6, Issue

03,234-236. Jan 2016, Google Scholar & Other International Databases.

66. Mr.Dilish Babu.J, Dr.S Mohan Kumar, A Survey On Secure Communication In Public Network

During Disaster, IJESRTInternational Journal Of Engineering Sciences & Research Technology,

Volume 5, Issue 3,430-434.March 2016, Google Scholar & Other International Databases.

67. Mr.Dilish Babu.J, Dr.S Mohan Kumar, Survey On Routing Algorithms During Emergency Crisis

Based On MANET, IJETAE International Journal of Emerging Technology and Advanced

Engineering, Volume 6, Issue 3,278-281.


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