A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system

Transcription

1 A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system Presented by: Wei Tu Written by: Gunasekaran Manogaran, R. Varatharajan, Daphne Lopez, Priyan Malarvizhi Kumar, Revathi Sundarasekar, Chandu Thota

2 Outline Introduction Background Related work MF-R and GC architecture Result and Analysis Performance evaluation Comparisons with other models Conclusion PAGE 2

3 Introduction The development of mobile sensing: - A number of body sensors devices have been developed for continuous monitoring of healthcare, personal fitness and physical activity awareness. The importance of big data: - Getting meaningful patterns from patient diagnostic data is an essential problem. The challenges that traditional data storage techniques and platforms face: - The volume, velocity, and variety of data is growing PAGE 3

4 Background Big data Ø Big Data originally meant the volume, velocity, and variety of data that becomes difficult to process when using traditional data processing platforms and techniques. Ø Especially, big data plays a vital role in healthcare applications. Nowadays, modern healthcare systems are adopting quickly clinical data, which is used for increasing clinical record sizes available online. Ø As a result, a number of options are identified to use Big Data for reducing the cost of healthcare and to diagnose diseases. PAGE 4

5 Background Cloud and fog computing Ø Cloud computing is the practice of using a network of remote servers hosted on the Internet to manage, store and process data, rather than a personal computer and a local server. Ø But traditional cloud computing architectures move the entire data from the network edge to the data center, causing more latency. Ø Fog computing is used for improving the efficiency and to reduce the volume of data that needs to be transferred from the physical devices to the cloud. PAGE 5

6 Background Internet of Things Ø The Internet of Things (IoT) is a connection of physical objects with network connectivity that used to collect and exchange data. Ø Thing refers to a device which is connected to the internet and transfers the device information to other devices. PAGE 6

7 Related work Nowadays, many researchers are using IoT in healthcare application which includes monitoring of continuous heartbeat, blood pressure, blood sugar level and body temperature. CodeBule was devloped by Harvard Sensor Network Lab to monitor individual health. This project uses the following sensors namely EKG, pulse oximeter, EMG, and SpO2 sensor to monitor individual health condition. PAGE 7

8 Related work Alarm-Net infrastructure was developed by University of Virginia to monitor patient health in an assisted living environment. Various sensors and actuators are used in the Alarm-net framework for observing the health condition of the patient. UbiMon is the healthcare project originally developed for observing the patients health condition in a continuous manner. Wireless ad hoc network and mobile communication such as 4G networks are used in this project for monitoring the patients health in a continuous manner This framework is used in the decision-making process when the patient health condition is bad. PAGE 8

9 Related work MobiCare is another project developed on the basis UbiMon for monitoring the individuals health condition on a continuous basis. Offline healthcare services are available in the MobiCare project for preventing the issues when the network failure occurs or an emergency situation arises. Personal ambient monitoring (PAM) is also developed for healthcare services for monitoring the individual s health. PAGE 9

10 Related work We do we need now? Ø A tool that are required to process the huge data generated form the various IoT devices. Ø Transmission cost should be reduced for transfer of clinical data between the sensors and the server. Ø The need for efficient optimization techniques between the consignment and the header information. Ø The requirement of effective fault tolerance system to manage network failure and emergency situations Ø The requirement of authentication and authorization schemes to protect the clinical data against the intruders and unauthorized access. Ø So on PAGE 10

11 A new architecture PAGE 11

12 MF-R architecture Data collection phase Ø Data collection phase is used for continuous monitoring of the patient s health. Ø Whenever the respiratory rate, heart rate, blood pressure, body temperature and blood sugar exceed the normal values, the devices send an alert message with clinical value to the doctor using wireless network through fog computing. Ø Sensor data collected from fog computing are connected to a cloud for transfer and storage into the database. PAGE 12

13 MF-R architecture Data transfer phase Ø The data collected from fog computing are moved to Amazon S3, where Amazon EMR can access it for further processing. Ø The proposed architecture uses s3cmd utility method for moving data into Amazon S3. S3cmd is responsible for uploading, retrieving and managing data in cloud storage service providers such as Amazon S3, Google Cloud Storage. It is a command line program suitable for batch scripts and automated backup to S3. PAGE 13

14 MF R architecture PAGE 14

15 MF R architecture Big data storage phase Ø In general, storage of a huge amount of data in the Amazon S3 bucket is not possible. In order to overcome this issue, the proposed architecture uses Apache Pig in data storage phase to transfer huge sensor data from an Amazon S3 bucket to the Apache Hbase, which is a scalable and distributed database that can store a large number of rows and columns in a distributed manner, PAGE 15

16 MF-R architecture PAGE 16

17 GC architecture Grouping and choosing GC architecture is connected with the Meta Fog-Redirection framework for providing security services between Fog and Cloud computing. To be specific, The big data is stored in different cloud data centers based on data classification and functions. For example, the data is in Sensitive, Critical and Normal focus. Hence, the proposed system initially classifies the data according to the type of data. This variety of data is stored in different cloud data centers and retrieved on the basis of the importance. PAGE 17

18 GC architecture The MetaFog-Redirection architecture redirects health data to different fog servers on the basis of the data categorization (sensitive, critical and normal). AWS Cloud Trail is used to process the log files. This log file information is used for classifying data into distinct type. For example, patients clinical parameter is assumed as critical data; patient s personal data, including name, age, sex and address -- all personal data of the patients are presumed as sensitive. Table 3 depicts the normal patient s data that includes patient ID, sex and age. PAGE 18

19 Security integration Public key and private key Encryption and decryption Cryptography identity access management PKI certificates authority PAGE 19

20 MapReduce implementation The proposed IoT health monitoring system uses MapReduce based stochastic gradient descent with logistic regression for developing the prediction model. The prediction model uses the data available in the Apache HBase. PAGE 20

21 Result and analysis Throughput: calculated on the basis of the volume of data sent from the IoT sensor devices to the sensor server in a given time period PAGE 21

22 Result and analysis PAGE 22

23 Result and analysis CPU usage PAGE 23

24 Performance evaluation PAGE 24

25 Performance evaluation PAGE 25

26 Comparison with other models SGD-LR and Multiple Regression(MR) Support Vector Machine(SVM) Random Forest(RF) How to compare? Mean Square Error (MSE), Root Mean Square Error (RMSE), R-squared, Q- squared, Sum of Squared Error (SSE), Total Sum of Squared Error (TSSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Mean Square Deviation (MSD) PAGE 26

27 Comparison with other models PAGE 27

28 Comparison with other models Plotting the pairs of sensitivity and specificities on a scatter plot provides a Receiver Operating Characteristic (ROC) curve. PAGE 28

29 Summary A MF-R with GC architecture for continuous monitoring of the patient s health, consisting of the data collection phase, the data transfer phase and the big data storage phase. Prediction - once the data is stored in the cloud, the system uses the stochastic gradient descent algorithm with logistic regression for developing the prediction model for heart disease. PAGE 29

30 Related papers P. Musiat, L. Hoffmann, U. Schmidt, Personalised computerised feedback in Emental health, J. Mental Health 21 (4) (2012) L. Filipe, F. Fdez-Riverola, N. Costa, A. Pereira, Wireless body area networks for healthcare applications: Protocol stack review, Int. J. Distrib. Sens. Netw. 2015(2015) Khan, Medical applications of wireless body area networks, Int. J. Digital Content Technol. Appl. 3 (3) (2009). PAGE 30

31 Future work The proposed architecture could be applied to more fields. More experiments could be done in different conditions. More environment data(like illumination, humidity) can be taken into consideration. PAGE 31

32 Strengths and Weaknesses Detailedly depicts the architecture of the proposed system Gives good example for some concepts to help understand. The data set used for training is too small. The comparison should be made among different architectures, rather than different machine learning algorithms. PAGE 32

33 Discussion Where else can this architecture be used other than healthcare? How does fog computing help in this architecture? What other functions can be added to this system? PAGE 33

34 Thank you! PAGE 34