Accepted Manuscript

Cloud-assisted Industrial Internet of Things (IIoT)- enabledframework for health monitoring

M.Shamim Hossain, Ghulam Muhammad

PII: S1389-1286(16)30001-9DOI: 10.1016/j.comnet.2016.01.009Reference: COMPNW 5803

To appear in: Computer Networks

Received date: 2 August 2015Revised date: 6 January 2016Accepted date: 8 January 2016

Please cite this article as: M.Shamim Hossain, Ghulam Muhammad, Cloud-assisted Industrial In-ternet of Things (IIoT)- enabled framework for health monitoring, Computer Networks (2016), doi:10.1016/j.comnet.2016.01.009

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Cloud-assisted Industrial Internet of Things (IIoT)- enabled framework for healthmonitoring

M Shamim Hossaina,∗, Ghulam Muhammadb

aSoftware Engineering Dept, College of Computer and Information Sciences, Riyadh, Saudi ArabiabComputer Engineering Dept, College of Computer and Information Sciences, Riyadh, Saudi Arabia

Abstract

The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devicesand sensors has played an important role in the next-generation healthcare industry for quality patient care. Becauseof the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoringinfrastructure for analyzing patients’ healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthI-IoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communicationtechnologies, interconnected apps, Things (devices and sensors), and people that would function together as onesmart system to monitor, track, and store patients’ healthcare information for ongoing care. This paper presents aHealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices andsensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, water-marking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals.The suitability of this approach has been validated through both experimental evaluation, and simulation by deployingan IoT-driven ECG-based health monitoring service in the cloud.

Keywords: ECG monitoring, IoT-driven healthcare, cloud-assisted system, signal watermarking, HealthcareIndustrial Internet of Things (HealthIIoT)

1. Introduction

Today we are witnessing the increased use of smartdevices and communication apps in healthcare monitor-ing, and their influence on the activities of healthcareprofessionals (doctors, nurses, and hospital administra-tors), patients, and the healthcare industry. Accordingto Gartner and Forbes, it is estimated that by 2020, theInternet of Things (IoT) will contribute $1.9 trillion tothe global economy and $117 billion to the IoT-basedhealthcare industry [1]. Based on this estimate, it is ex-pected that the Healthcare Industrial IoT (HealthIIoT)will be one of the main players in the Industrial Internetof Things (IIoT)-driven healthcare industry. IIoT hashad a remarkable influence across many large and smallhealthcare industries . As a result, an increasing num-ber of wearable IoT devices, tools, and apps are beingused for different monitoring applications (e.g., glucose

∗Corresponding authorEmail addresses: mshossain@ksu.edu.sa (M Shamim

Hossain ), ghulam@ksu.edu.sa (Ghulam Muhammad)

monitors, ECG monitors, and blood pressure monitors)to avoid preventable death due to hospital or other re-lated errors. The errors may occur before, during, orafter hospitalization.

Currently, HealthIIoT is still in its preliminary stageswith regards to design, development, and deployment;however, IoT-based solutions are presently displaying aremarkable impact, and carving out a growing marketin today’s healthcare industry and tomorrow’s emerg-ing IIoT-based healthcare monitoring solutions. IIoThas the potential to save 50,000 people each year in theUS by avoiding preventable deaths due to hospital error[2]. It promises patient well-being and safety by coor-dinating critical patient information and synchronizingrelated resources (e.g., healthcare staff, facilities, wear-able smart devices to capture real-time patient data suchas vital signs, and patient-related electronic informa-tion) instantly through interconnected devices and sen-sors. Research reveals that IoT in the healthcare indus-try can facilitate better care with reduced costs, reduceddirect patient-healthcare staff interaction, and ubiqui-tous access to quality care [3]. Mohammed et al. de-

Preprint submitted to Computer Networks February 4, 2016

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veloped a remote patient monitoring system using webservices and cloud computing [4]. Hassanalieragh etal. discussed the opportunities and challenges of healthmonitoring and management using IoT [5]. To date,however, no comprehensive study has been publishedabout cloud-assisted IIoT-driven health monitoring.

Safe and high-quality healthcare service is ofparamount importance to patients. Accordingly, health-care data security and patients’ privacy are important is-sues that will have a great impact on the future successof HealthIIoT [17]. One of the major issues in the IIoT-based healthcare system is the protection of privacy. Ingeneral, a healthcare service provider receives data fromits users (such as patients) and shares them with regis-tered clinics or healthcare professionals. The providermay also distribute the data to health insurance compa-nies and pharmaceutical companies. Moreover, patientdata can be vulnerable to hackers during cloud transferor synchronization with interconnected devices.

Therefore, we need to protect this information fromunauthorized access, which may result in the postingof personal information in the public domain, or in in-terference with essential medical equipment, such as apacemaker. A security breach of a patient’s monitoringdevices and data may cause the patient social embar-rassment, mental disorders, or adverse physical effectssuch as a fatal heart attack. Hence, data protection in theform of watermarking and authentication is very impor-tant in an IIoT-based healthcare system.

To this end, this paper describes an IIoT-based healthmonitoring framework, where health monitoring signalsare authenticated. As a case study, we have used electro-cardiogram (ECG) monitoring, as ECG is an importantassessment tool. By continuously monitoring an ECGsignal, a healthcare professional can diagnose diseaseand prescribe medications to avoid preventable death .ECG signals are recorded via portable ECG recordingdevices at home or outdoors, and sent to smartphones ordesktops via Bluetooth technology . On the client side, asmartphone app or desktop software removes unwantednoise from the recorded signal, and embeds a water-mark for security and authentication purposes; heartbeatis also monitored using a simple algorithm . The water-marked ECG signal is then transmitted to a cloud server,where temporal and spectral features are extracted andclassified using a one-class support vector machine clas-sifier. The classification decision, along with the water-marked ECG, is passed to the desired healthcare profes-sional for analysis. The professional then sends back adecision and prescription to the cloud server. The cloudthen notifies the patient . The contributions of the paperare as follows:

• the introduction of a watermark into the ECG sig-nal on the client side, to avoid a security breach inthe cloud

• the introduction of a user identification code to pro-vide customized protection of data

• the introduction of a one-class support vector ma-chine classifier in the cloud

The rest of this paper is organized as follows. Sec-tion 2 reports some related studies. Section 3 outlinesthe HealthIIoT ecosystem, followed by a high-level dataflow architecture for the HealthIIoT monitoring valuechain, and the details of a cloud-supported HealthIIoT-enabled monitoring framework. Section 4 describes aproposed health monitoring approach by consideringECG as healthcare data. Section 5 presents the exper-imental results and evaluations. Section 6 concludes thepaper.

2. Related Studies

The IIoT is an innovative technology, directly inter-connecting a set of sensors and devices (such as smart-phones) to collect, record, transmit, and share data forpossible analysis. The IoT has a wide range of emerg-ing applications [4]-[16]. Among them, the most revo-lutionary potential application is healthcare monitoring,where patient healthcare data are collected from a num-ber of sensors, analyzed, delivered through a networkand shared with healthcare professionals for evaluationof patient care [4], [5], [6], [10], [11], [13], [23]. Amore comprehensive survey of IoT for healthcare ap-plications can be found in [7]. IoT-enabled healthcareapplications, including IoT-driven ECG monitoring, arediscussed in the following studies [6], [10]. Li et al.[10]. presents a health monitoring service as a plat-form for ECG monitoring using adaptive learning anal-ysis model to detect abnormalities.

Mohammed et al. developed a remote patient moni-toring system using web services and cloud computing[4]. In particular, they designed an Android applica-tion for ECG data monitoring and analysis. Data canbe further analyzed by third-party software if needed;however, there is no option for the cloud server to ex-tract features and classify the signal to assist the healthprofessional at the time the signal is received. In ourproposed framework, the cloud server extracts featuresand classifies the signal, so that a preliminary analysisdecision from the cloud can be sent to the healthcareprofessionals to facilitate good patient care.

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Hassanalieragh et al. discussed the opportunities andchallenges of health monitoring and management us-ing IoT [5]. Some challenges include slow processing,handling big data, presence of too much heterogeneousdata, and data integrity. In our proposed framework,the ECG signal is watermarked on the client side beforetransmitting through the Internet to authenticate againstany attacks. Data processing is also distributed betweenthe client side and the cloud side to make the overallprocessing faster.

Jara et al. [8] present a remote monitoring frameworkusing IoT by proposing a protocol, called YOAPY, tocreate a secure and scalable fusion of multi-modal sen-sors to record vital signs. A cloud-based speech andface recognition framework was developed to monitor apatient’s state remotely [27]. Xu et al. [9] developed aubiquitous data accessing method in an IoT-based sys-tem for emergency medical scenarios. They proposed asemantic data model to store data, and a resource-baseddata access method to gain control of the data ubiqui-tously, concluding that their method could be significantto assist decision-making in emergency medical situa-tions.

Zhang et al. [12] introduced an architecture of mobilehealthcare networks, incorporating privacy-preservingdata collection and secure transmission. The privacy-preserving data collection was achieved using cryptog-raphy with secret keys and private keys. Secure trans-mission was gained using attribute-based encryption,where only authorized users would have access to thedata. These methods are generally worthwhile; how-ever, the main problem is computation complexity.

Granados et al. [14] proposed web-enabled gatewaysfor IoT-based eHealth with an option for wired or wire-less services. To take advantage of wired gatewaysin terms of power-efficiency and low cost, the authorsused the wired gateways in a small room or building,where movement is restricted. Radio frequency iden-tification (RFID)-based eHealthcare systems were pro-posed in [15] and [16]. In [15], the authors proposeda system that would capture the patient’s environmentalconditions, such as temperature and humidity, by RFID,and transmit them to the cloud for a more detailed un-derstanding of ambient conditions. Catarinucci et al.[16] proposed an IoT-aware architecture to monitor andassess a patient’s situation automatically by integratingultra-high-frequency RFID functionality.

Sawand et al. [18] identified three types of threats inan eHealthcare monitoring system. These are identitythreat, where the identity of the patient is lost or stolen,access threat, where an intruder can access the systemillegally, and disclosure threat, where confidential med-

ical data are opened via malware or file sharing toolsintentionally or unintentionally. They offered some so-lutions to these threats, including biometric cryptogra-phy and an advanced signal processing scheme; how-ever, the authors did not implement these solutions intheir paper.

Patient with IoTdevices and sensors

Healthcareprofessionals

Smart medical device and ‘Things’ industries

Emergencyresponse service

Social media and family

Environmental and home control service (e.g. temp, air, light)

Drug industries, and pharmacies (e.g. smart

pills, and medicines)

Analysis ECGverification

Storage

Replicadata center

Research center

Figure 1: Conceptual illustration and scenario for HealthIIoT ecosys-tem.

In [19] and [21], emotion-aware or affective mobilecomputing frameworks have been proposed, and theauthors investigated an architecture named “emotion-aware mobile cloud” (EMC) for mobile computing. Au-thors in [20] proposed another framework, affective in-teraction through wearable computing and cloud tech-nology (AIWAC). Recently, Hu et al. [6] introduced theHealthcare Internet of Things (Health-IoT), attemptinga bridge between intelligent health monitoring and emo-tional care of the patient.

To date, we have found no comprehensive study oncloud-assisted IIoT-driven ECG monitoring, where i)the ECG signal is watermarked on the client side beforetransmission through the Internet to the cloud, and ii)the cloud server extracts features and classifies the sig-nal to assist healthcare professionals in providing qual-ity patient care.

3. Proposed cloud-assisted HealthIIoT-enabledmonitoring framework

HealthIIoT can revolutionize today’s healthcare in-dustry with affordable and quality patient care by adopt-ing a large number of interconnected machines, wear-able things (devices and sensors), and cloud-computing

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Figure 2: Data flow architecture for HealthIIoT monitoring value chain.

technologies to collect patient data in a seamless man-ner. This HealthIIoT technology will play an impor-tant role in a number of health monitoring applications,to form a Healthcare Industrial IoT ecosystem. Fig-ure 1 describes a comprehensive IIoT-driven health-care ecosystem. As shown in Figure 1, one type ofstakeholder (e.g., patient with IoT devices and sen-sors, healthcare professional, hospital or medical re-search center, social media and family) is connected toanother type of stakeholder (e.g., emergency responseservices, drug industries and pharmacies, smart medi-cal devices and ’Things’ industries, environmental andhome control services) to form a complex HealthIIoTecosystem. It also dispatches emergency services tothe patient when needed, and orders pharmacy refills.In this ecosystem, interconnected ‘Things’ (medical de-vices and sensors) are coordinated. It allows fast trans-fer of patient information among the stakeholders in asecured manner, such that specific patient data are avail-able only to a designated authorized healthcare team.Finally, cloud-based big data analytic enables analyz-ing, storing, closely monitoring, and securely sharingthe data for further review and medical recommenda-tions, aimed towards fulfilling the promise of IndustrialIoT in regard to quality patient care, real-time patientmonitoring, and avoiding hospital error.

The Industrial IoT is the combination of big data,IoT, Machine to Machine (M2M) communication, cloudcomputing, and real-time analysis of data from inter-

connected sensor devices [22]. The success of HealthI-IoT largely depends on the advancement of the cloud-computing technology and big data analytics. It cre-ates a platform for interconnected smart medical de-vices to operate with large amounts of data (big data)from anywhere at any time. The data are actually gener-ated by a myriad of interconnected smart devices, com-munication apps, and their usage in healthcare monitor-ing applications. Data are gathered and analyzed frome-health records, imaging equipment, medical sensors,devices, and smartphones over the cloud. This analy-sis augments the decision-making power of healthcareprofessionals, and helps patients have an active role inmanaging their personal health.

Figure 2 outlines how the flow of a patient’s health-care data (e.g., ECG signals) is captured securely; how itis transferred seamlessly through a connection gatewayto the cloud data centers for further analysis and pro-cessing, such as feature extraction, classification, veri-fication, workload measurement, and big data manage-ment. After being processed and securely stored in thecloud, the chain of collected data is either accessed byhealthcare professionals, or delivered to external sys-tems for further industry-specific healthcare IoT solu-tions.

Figure 3 shows the cloud-assisted IIoT-enabledhealth monitoring framework. First, the patient’s ECGsignal is recorded through the connected devices andsensors, and then after possible signal reconstruction,

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enhancement, and watermarking, it is sent to the cloud-based system using network connections. The cloudsystem validates this information to check that the pa-tient’s information is correct, and then extracts somefeatures, classifies the signal, and redirects it to the as-signed healthcare professionals and providers for possi-ble patient care.

The major components of the framework are de-scribed below.

Healthcare staff and other related stakeholders:Patients upload their ECG readings through an ECGinterface, which is connected to the Internet. Aftersome processing, the ECG reading is stored in the clouddatabase, where healthcare professionals can access itand review it for possible action based on the uploadedECG readings.

ECG signal capturing and recording service: Thisservice is used to record and store the ECG signal fromdifferent devices and smartphones.

Secure transmission service: The service enables asecure and authenticated transmission of the ECG signalthrough Internet. To accomplish this, watermarking isembedded into the signal, and later, is extracted to verifythe authenticity.

Resource allocation manager: Manages virtual ma-chine (VM) resources and web services.

Cloud system manager: Controls all VMs and allo-cates suitable resources through the resource allocationmanager for each service, such as ECG signal and thingscollection and record service manager, ECG monitoringsession manager, feature extraction and classificationmanager, and finally, signal reconstruction, enhance-ment and watermarking manager.

1. ECG signal and things collection and record ser-vice management: This web service is respon-sible for managing the users’ data and their re-lated health information, and storing them in thedatabase.

2. ECG monitoring (analysis and communicationalerts) session management: Responsible for man-aging and controlling the sessions, in addition tolocating, tracking, and evaluating the activities.

3. Feature extraction and classification management:This web service extracts collected data upon run-ning the ECG apps on smartphones, and storesthem in a MySQL database before sending themto the HealthIIoT cloud.

4. Signal reconstruction, enhancement, and water-marking: This web service generates, records, andtracks the performance of the monitoring function.

HealthIIoT manager: Manages all health-relatedIIoT data by assigning data to different replicated datacenters, after verifying the authenticity of the data.

Monitoring and analytics: Analyzes the data by ex-tracting features and applying classification techniques.Calculates and monitors the workload of the framework,such as storage and bandwidth.

Replica service: Because of the growing demandfor interconnected medical devices with heterogeneousconnectivity, HealthIIoT systems handle large numbersof data requests for accessing patient healthcare. There-fore, datasets need to be replicated in multiple sites anddata centers to offer faster data access times. If one ormore sites (data centers) are down, healthcare data canbe accessed from other nearby sites. Generating repli-cas also enables the healthcare professionals’ ECG filerequests to distribute the workload through the replicaservers, and avoid performance degradation due to net-work congestion. Moreover, this ensures faster access,scalability, and a reduction in response time.

IIoT-driven healthcare service directory: Recordsand stores data from the ECG capturing devices. It alsoregisters and publishes different participating services.It facilitates continuous care for the patients by record-ing the ECG signal in portable ECG recording devicesat home or outdoors, and sends them to smartphonesor desktops. Some major elements in this directory arethe ECG capturing service, feature extraction and ECGclassification service, secure transmission service, andpayment service. Healthcare professionals can get ac-cess to the ECG data from this directory without visitingthe patient.

4. Proposed health monitoring approach

Our proposed health monitoring approach consists ofsome processing steps, which are signal enhancement,watermarking, feature extraction, ECG analysis, andsignal reconstruction. Signal enhancement and water-marking are done on the client side. The work flow ofthe proposed framework is shown in Figure 4.

4.1. Signal enhancementAn ECG monitoring system based on the cloud was

proposed by Pandeya et al. [24]. In their system, ECGdata were collected by mobile devices and were sent tothe cloud for analysis. The system was just a proto-type, and therefore, problems remained in its fully prac-tical usage in term of data collection and transmission.The first issue is to ensure the effectiveness of ECG datacollection through mobile devices. Physiological arti-facts can be caused by muscular activities that result

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Figure 3: The proposed HealthIIoT architecture for an ECG monitor-ing.

in small spikes, and by human motion that results inlarge swings in the recorded data. Non-physiologicalartifacts can be produced by electrical interference andelectrode malfunction. Electrode malfunction is initi-ated by loose connections, electrode misplacement, lowamount of electrode gel, wrong filter setting, fracturedwires, etc. Of them, electrode misplacement is a majorsource of malfunction of ECG data acquisition [25]. Ca-ble misplacement can even result in ECG that resemblescardiac abnormalities like ectopic rhythm [26].

Figure 4: Overall work flow of the ECG monitoring system in thecloud.

Therefore, in the proposed framework, the recordedECG signals are enhanced before processing to get ridof some of the common artifacts. In the enhancementstage, the recorded ECG signal is passed through a low-pass filter to suppress the high frequency componentsthat are referred to noise. A 25-point moving averagefilter is then applied to the output of the low-pass fil-ter to smooth the signal. Figure 5 shows the effect

of low-pass filtering and moving average filtering of arecorded ECG signal. From the figure, we see that thesignal looks ’clear’ after applying these two filters. Thispreprocessing step is necessary to correctly detect elec-trical waves in the ECG signal, which is critical for sub-sequent online analysis and monitoring.

Figure 5: Effect of low-pass filtering (b) and moving average filtering(c) of the recorded ECG signal (a).

4.2. Peak R detection

To process the ECG signal, determining peak R is re-quired. These two attributes in ECG signal are very im-portant for ECG signal analysis. To detect R, we useanalytical wavelet transform (AWT) [29]. A complexAWT can be expressed by Eq. 1.

φ(ω) = ωne−ω, ω ≥ 0= 0 , ω < 0

(1)

The above equation is obtained by combining aHilbert transform and a wavelet transform. In Eq. 1, n isthe derivative order. The advantage of using the aboveequation is that it combines the time-frequency locationof a wavelet transform with the local slope informationof the Hilbert transform.

After applying complex AWT, we apply thresholdingand look for persistent lines. The local maximums areidentified in the wavelet transform signal to find R (seeFigure 6).

4.3. ECG watermarking

The next stage is to watermark the signal to protect itfrom forgery. Watermarking is a procedure to embedsome information in a signal without distorting the visi-bility or credibility of the signal for ownership claim. Ithas mainly two parts: watermark embedding and water-mark extraction for verification. Embedding watermarkin the ECG signal will ensure the authenticity of the

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Figure 6: Detected peak R.

ECG signal transmitted over the cloud. We adopt a sim-ple yet efficient strategy of watermarking that has rarelybeen used for ECG signals. The watermarking is basedon discrete wavelet transform (DWT)-singular value de-composition (SVD) [26] [28]. The study in [26] [28]did not use DWT-SVD watermarking scheme in ECGsignals, e-healthcare or cloud-based systems. DWT isa multi-resolution technique that decomposes a signalinto different levels of time and frequency. In the pro-posed step, we use a two-level DWT that decomposesthe signal into three subbands: approximation subbandL2, second-level detail subband H2, and first-level de-tail subband H1. SVD is a matrix factorization tech-nique that decomposes a matrix into three matrices. If arectangular matrix A of size I × J is the input, the out-put will be two orthogonal matrices and one diagonalmatrix as follows (Eq. 2):

AI×J = UI×IS I×JVTJ×J (2)

where UT U = II×Iand VT V = IJ×J , which meansthat U and V are orthogonal. S is a diagonal matrix,whose diagonal entries are singular values and arrangedin descending order. These singular values are alwaysreal numbers. The computation of SVD is stable againstround-off errors. The fact that a slight variation in thevalues of S matrix does not affect the perception of anECG signal, watermark bits can be added to the singularvalues of S to get a robust watermarking.

The watermark is an image consisting of the client’sregistered ID in image format of size I × J, where I > J..The detailed procedure is described as follows.

Step 1. Normalize the watermark image matrix by 255.

Imi, j ={watermarki, j/255; 0 ≤ i ≤ I, 0 ≤ j ≤ J

}

Step 2. Apply SVD on the normalized matrix. The re-sultant Sw is a square matrix of size I × I.

Im = Uw · S w · VTw

Step 3. Multiply Sw by a watermark intensity factor, α.

S wα = α · S w

Step 4. Store Uw, VwT , and α for watermark extraction.

Use Swα for watermark embedding.Step 5. Divide the ECG signal into N number of beats.

The beat duration is 0.6 seconds and peak R islocated at 40% of the duration.

Step 6. Step 6: Apply two-level DWT on each beat.Take H2 (detail coefficients at level 2) for wa-termark embedding. Store L2 (approximationcoefficient) and H1 (detail coefficients at level1) for reconstruction of watermarked ECG sig-nal.

Step 7. Form a matrix G using H2 of all the frames.The number of rows corresponds to the numberof beats of the signal.

Step 8. Apply SVD on matrix G. The resultant Ss is asquare matrix of size N × N.

G = Us · S s · VTs

Step 9. A new matrix, Snew, of size N × N is formed byusing matrices Swα and Ss.

S new =

{S wα(n, n) + S s(n, n), 1 ≤ n ≤ IS s(n, n), (I + 1) ≤ n ≤ N

Step 10. Using Us, V sT , and Snew, perform inverse SVD

to get matrix G’.

G′ = Us · S new · VTs

Step 11. Using L2, G’, and H1, perform inverse DWT toget watermarked ECG signal.

The watermark image can be reconstructed by usingthe following steps.

Step 1. Subtract Ss from Snew to get Sim. Sim should beequal to Sw if the watermarked speech signal isnot under attack.

S im = S new(n, n) − S s(n, n), 1 ≤ n ≤ I

Step 2. Apply inverse SVD to get the normalized wa-termark.

Im′ = Uw · S im · VTw

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Step 3. Get the watermark image by multiplying thevalues by 255 and dividing byα.

Im′i, j ={Im′i, j × 255/α; 0 ≤ i ≤ I, 0 ≤ j ≤ J

}

If there is no attack, Im’i, j will be the same asthe watermark image.

The enhancement stage and the watermark stage areperformed on the client side. Once the ECG signal iswatermarked signal is transmitted to the cloud, where itis processed to extract features and to analyze.

4.4. Feature extraction

Several features are extracted from the ECG signal inthe cloud server. The features include heartbeat rate(HBR), durations of P wave, PR interval, QRS com-plex, and QT interval, and the shape (inverted or not,and peaked or not) of T wave. These are total of sevenfeatures.The HBR is determined by the inverse of the time dif-ference between RR intervals and expressed as beats perminutes (bpm) as expressed by Eq. 3.

HBR(bpm) =60

RR intervals (sec.)(3)

An unusual p-wave may represent ectopic atrial pace-maker. p-wave longer than 80 millisecond (ms) can in-dicate atrial enlargement. A PR interval smaller than120 ms may be a cause of Wolf-Parkinson-White syn-drome, while larger than 200 ms may indicate a firstdegree of atrioventricular block. QRS complex widerthan 120 ms suggests a disruption of the heart’s conduc-tion system, or sever hyperkalemia. A prolonged cor-rected QT interval (> 440 ms) risks for ventricular tach-yarrhythmia, while an unusual short interval may indi-cate severe hypercalcemia. For a corrected QT interval,QT interval should be normalized by the square root ofthe RR interval. Inverted T waves may be syndromesof myocardial ischemia, or high intracranial pressure,while a peaked (determined by the variance of the wave)T wave may indicate hyperkalemia or early myocardialinfarction.

Spectral features are also calculated by applying theFast Fourier Transform (FFT) to each beat. A 512-pointFFT is used and first half of the magnitude output is re-tained. The 256 bins are linearly resampled to F numberof bins, where F is varied to 10, 20, 30, and 40. These Fnumber of features are appended to previously mentionseven features for classification.

4.5. One-class support vector machine (OCSVM) clas-sification

OCSVM is a one-class classification technique, wherethe feature space is mapped to a higher dimensionalspace so that a hyperplane maximizes the distance be-tween the hyperplane and the origin. In a typical SVM,there are two classes, while in OCSVM, there is onlyone class in the training. OCSVM is used in this frame-work, because it can detect the personal ECG as abnor-mal or not. For the experiments, MIT-BIH database wasused [30]. The first group of 48 records are divided intotwo sets: first set comprises of first 3 minutes of datain each record for training, while second set consists ofremaining 27 minutes of data in each record for testing.

Figure 7 shows the separation of the modules in theclient side and the cloud side.

Figure 7: ECG signal modules on the client side and the cloud side.

5. Experimental results and evaluation

Several experiments were performed to validate theproposed IoT-enabled ECG signal monitoring. They aredescribed in details in the followings subsections.

5.1. Watermarking performance

The performance of the ECG watermarking was mea-sured in terms of imperceptibility, and robustnessagainst attacks [31]. Imperceptibility is a measureof how much the signal is distorted perceivably. Tomeasure imperceptibility, we used signal-to-noise ratio(SNR), which is an objective measurement. SNR is de-fined by Eq. 4.

S NRdB = 10 log10Ps

Ps − P′s(4)

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Figure 8: PSNR (dB) and SNR (dB) using the proposed DWT-SVDbased watermarking, which is compared with DWT-DCT based wa-termarking.

Figure 9: Correlation factor after different types of attack.

where, Ps and Ps are the power of original ECG sig-nal and the watermarked ECG signal, respectively. An-other closely related metric is peak SNR or PSNR. InPSNR, the numerator of logarithm in Eq. 4 is replacedby the square of the maximum value of the pixel in theoriginal watermark image.

Figure 10: Classification accuracy of the proposed framework.

With regard to robustness against attack, we con-sidered two common attacks, which are additive whiteGaussian noise (AWGN), and filtering of type low-pass,high-pass, and band-pass. The measurement were ob-tained by using a correlation factor, η, which is com-puted by using Eq. 5.

η(w,w′) =

N∑i=1

wiw′i√

N∑i=1

wi2

√N∑

i=1w′i2

(5)

where, w and w’ are the original and extracted wa-termark, respectively, N is the number of pixels in thewatermark image. η takes the value between 0 (no rela-tion) and 1 (perfect relationship).

Figure 8 shows the obtained SNR and PSNR indB using the proposed DWT-SVD based watermark-ing. The proposed scheme was compared with an-other popular method, which is DWT-DCT (discrete co-sine transform)-based [32]. From the figure, we seethat the proposed watermarking has a very good SNRand PSNR, and it outperforms the DWT-DCT basedmethod.

Figure 9 shows correlation factor, η, after differenttypes of attacks. The attacks were applied in the cloudserver. The attacks included band-pass filtering withpassband between 8 Hz and 40 Hz, high-pass filteringwith cutoff frequencies of 40 Hz, low-pass filtering withcutoff frequencies of 8 Hz, and 4 Hz, and AGWN of 20

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TFigure 11: Time requirement of the proposed framework.

dB, 15 dB, and 10 dB. From the figure, we see that al-most in all the cases the correlation factor was 1, whichindicates the robustness of the proposed watermarkingalgorithm. For a comparison with DWT-DCT basedmethod, the correlation factor of DWT-DCT based wa-termarking is 0.98 for AWGN 20 dB attack.

Table 1: Storage Configurations.

Config. Node Capac-ity (GB)

Total Stor-age (TB)

RelativeStorage(%)

1 [50-1000] 20.75 802 [50-500] 15 603 [50-200] 12 46.754 [20-50] 4.5 185 [10-20] 3.5 15

5.2. Classification performance

Zipf Guassian. Random

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Figure 12: ECG data access time using relative storage capacity 80%.

Two types of classification experiments were per-formed: one with the MIT-BIH database as mentioned

in the classification section, and the other with actualdata recorded through the proposed framework. Figure10 shows the average classification accuracy using thetwo databases. Seven features correspond to featureswithout spectral features. With 37 features, the accu-racy reached up to 87.7% with MIT-BIH database and90.4% with private database. Figure 11 shows the timespent from transmitting the data to the cloud, extractingfeatures, to classifying the data. One instance, three in-stances, and fives instances of servers were used in thecloud. With five instance server, only around two sec-onds were needed while using 37 features.

1 2 3 4 52

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(b) Gaussian

Figure 13: ECG data access time comparison for all storage configu-ration.

5.3. Workload of the IIoT-enabled health monitoringservice

To evaluate the proposed IIoT-based for health mon-itoring, we used a Java-based simulator program. Thesimulation environment includes cloud topology and anECG data access pattern by a healthcare professional.

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To reduce overhead and latency, ECG files are repli-cated so that healthcare professionals can get access tothe desired data for a specific patient from neighboringdata centers. We have used a similar multi-tier cloudtopology structure [33] with multiple data centers usingthe following storage configurations of replica serversas shown in Table 1, where the capacity of relative stor-age ranges from 15% to 80%. While submitting a task,a number of ECG files are requested for access to pa-tient data. The sequence of file request is handled bythree access patterns, such as Zipf distribution, randomdistribution, and Gaussian distribution. The frameworkis evaluated in terms of ECG data access by healthcareprofessionals. The access time refers to the time of com-pletion of all tasks for the ECG file requests. The objec-tive is to reduce ECG data access time. In the simula-tion, constant data access rate is considered. The totaldata access time is a summation of time needed by thedisk to find a replica of an ECG file from the replicadata center disk and network communication latency forreplica transmission. The data access time, Access(R),is calculated using the following model, Eq. 6:

Access(R) =∑

v∈Gf (v, n) · Dlatency(v, r) + Gnetwork(v, r)

(6)where, G = (V, L) is undirected tree structure of the

cloud, R is a set of replica data centers, n is an ECG fileor sample, f (v, n) is data access frequency by a health-care professional (v) for an ECG file (n), r is lowestancestor of v in R, Dlatency is disk access latency, andGnetwork is network communication latency.

We compared ECG data access time by integrat-ing replication and without considering replication ap-proaches to the proposed framework. Figure 12 com-pares the ECG data access times by considering repli-cation and non-replication strategies for the first stor-age configuration, as shown in Table 1. In the major-ity of cases, a storage configuration with replication re-sulted in a shorter ECG data access time than a con-figuration without replication. Replicated datasets inseveral data center locations decrease the data accesstime because healthcare professionals have access tothe required ECG data for a particular patient from anearby site. Moreover, the storage capacity of the datacenters favors using the replication approach. With areduced storage capacity, the ECG data access timesfor both approaches (i.e., with and without replication)are increased, but by different magnitudes. Figure 13shows the execution times for Zipf and Gaussian distri-butions for all the storage configurations listed in Table

1. Shorter access times for all storage configurations arecorrelated with using the replication approach.

0 15 30 45 60 75 90 105 1200

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(b) ECG Capturing

Figure 14: Workload for ECG Monitoring.

Figure 14 shows the workload of the main servicesused for health monitoring of the proposed HealthIIoTframework. We have concentrated on three key ser-vices: ECG capturing service, transmission service, andextraction or classification service. To understand thefeatures of those workloads as they relate to ECG mon-itoring, the run-time characteristics of those workloadsare collected by running the proposed health monitor-ing prototype on the Amazon Elastic Computing Cloud(EC2). For this purpose, we rented a VM with anIntel R©CoreT M 2 Duo, DDR3 ECC RAM at 2.53 GHZ,1 Gbps bandwidth, and 4.0 GB memory, running Win-dows Server 2010. The performance monitor of Win-dows has been used to record the resource consump-tions of those workloads for memory, CPU, and network

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bandwidth utilizations.

6. Conclusion

IIoT-driven healthcare monitoring is an emerginghealthcare service that may potentially revolutionizethe healthcare industry in terms of improving accessto patient information, and offer quality patient carethrough continuous monitoring from anywhere at anytime, through a multitude of devices. With HealthIIoT,healthcare professionals may be able to access patientinformation, store it, and analyze it in a real-time man-ner to monitor and track the patient. However, inter-connected wearable patient devices and healthcare data(such as ECG signals) are subject to security breaches.To this end, this paper describes a cloud-integratedHealthIIoT monitoring framework, where healthcaredata are watermarked before being sent to the cloud forsecure, safe, and high-quality health monitoring. Fu-ture work will involve testing the proposed HealthIIoTmonitoring framework for data security and notificationfunctions, as well as implementing a test trial with real-world patients and health professionals.

Acknowledgment

The authors extend their appreciation to the Deanshipof Scientific Research at King Saud University, Riyadh,Saudi Arabia for funding this work through the researchgroup project no. RGP 228.

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Biography

M. Shamim Hossain is an Associate Professor ofSWE, CCIS, at King Saud University, Riyadh, KSA.Dr. Shamim Hossain received his Ph.D. degree in Elec-trical and Computer Engineering from the Universityof Ottawa, Canada. His research interests include se-rious games, cloud and multimedia for healthcare, bigdata for multimedia, social media, and biologically in-spired approach for multimedia and software system.He has authored and co-authored around 100 publica-tions including refereed IEEE/ACM/Springer/Elsevierjournals, conference papers, books, and book chapters.He has served as a member of the organizing and tech-nical committees of several international conferencesand workshops. Recently, he received outstanding pa-per award from an IEEE Conference. He has servedas co-chair, general chair, workshop chair, publicationchair, publicity chair, and TPC for over 12 IEEE andACM conferences and workshops. He is on the edito-rial board of Springer Multimedia tools and Applica-tions (MTAP). Currently, he serves as a lead guest ed-itor of IEEE Transactions on Cloud Computing, IEEECommunication Magazine, Elsevier Future GenerationComputer Systems, Elsevier Computers and Electri-cal Engineering, Springer Multimedia tools and Appli-cations (MTAP), Springer Cluster Computing. Previ-ously, he served as a lead guest editor of IEEE Transac-tions on Information Technology in Biomedicine (cur-rently JBHI), Springer Multimedia tools and Applica-tions, and Hindawi International Journal of DistributedSensor Networks. Dr. Shamim is a Senior Member ofIEEE and a member of ACM.

Ghulam Muhammad is anAssociate Professor in the department of Computer En-gineering, College of Computer and Information Sci-ences at the King Saud University, Riyadh, KSA.Dr. Ghulam received his Ph.D. in Electrical andComputer Engineering from Toyohashi University andTechnology, Japan in 2006. His research inter-ests include serious games, cloud and multimedia forhealthcare, resource provisioning for big data process-ing on media clouds and biologically inspired ap-proach for multimedia and software system, image

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and speech processing. He has authored and co- au-thored more than 120 publications including refereedIEEE/ACM/Springer/Elsevier journals, conference pa-pers, books, and book chapters.

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