Mobile Body Sensor Networks for Health Applications

sweetlipscasteSecurity

Nov 2, 2013 (3 years and 7 months ago)

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Mobile Body Sensor Networks for
Health Applications

Yuan Xue, Vanderbilt

Posu Yan, UC Berkeley

A collaborative work of

Vanderbilt (Sztipanovits,
Xue
, Werner, Mathe, Jiang)

Berkeley (Bajcsy,
Sastry’s

group)

Cornell (Wicker group)

2

Topics


Introduction


Monitoring congestive heart failure (CHF)
patients


System overview


Security support


Experiments


WAVE and Berkeley Fit

Introduction


The cost of health care has become a national concern.


Medicare was 35 million for 2003 and 35.4 million for 2004


Health care expenditures in the United States will project to rise
to 15.9% of the GDP ($2.6 trillion) by 2010
.


Impact of Information Technology


Electronic Patient Records


Remote Patient Monitoring


Integration of wireless communication, networking and
information technology


large amount of medical information can be collected to help
determine the most effective strategies for treating chronic
illness, reducing disability and secondary conditions


improving health outcomes, and reducing the healthcare
expenses by more efficient use of clinical resources.

3

4

Remote Patient Monitoring


Needs to be part of the overall chronic disease
management process.


Requires fully integration of


IT Technologies


wireless communication, sensor platform,
networking, and database


Clinical enterprise practice


Explicitly incorporates security and privacy
policies to protect the end
-
to
-
end
communication and access of sensitive
medical information.



System Overview

5

Execution
Engines

BPEL

Engine

EMR

EMR
Services

Monitor
Services

Monitor
Services

Service Oriented Architecture


Protocol
models

Workflow
models

Monitor models

Sensor network

Patient
management

Decision

Support


Remote Patient Management

Computing and Network Infrastructure


Clinical Information System

Homecare System

Execution
Engines

Clinical Foundation

Technology Foundation

End
-
to
-
end
Security models

6

Monitoring CHF Patients


Provide unobtrusive and persistent monitoring


Weight


Blood pressure


Heart rate


Energy expenditure


Data analysis and feedback


Automated
-

based on thresholds (i.e. cannot allow
rapid weight fluctuation, etc.)


Doctor intervention

7

System Architecture

Medical
Database

Automated
Evaluation

Doctor
Evaluation

8

System Components


Hardware


Nokia N810 Internet Tablet


External 802.15.4 basestation


Motion sensor (802.15.4)


Weight scale (Bluetooth)


Blood pressure monitor (Bluetooth)


Software


SPINE (Signal Processing In Node
Environment)


Bluetooth daemon


Apache Axis2 WSDL client

Nokia N810

Motion sensor

Weight scale

Blood pressure monitor

Remote Monitoring Software Architecture

9

Data sampling

Data analysis

Sensor control

Data analysis

Sensor control

Data aggregation

Web service

Buffer Management

Secure Comm.

Sensor Auth.

Secure Communication

Sensor Authentication

Service Layer

TinyOS

Telos Mote

TinyOS

Telos Mote

Comm Layer

Media Access Control

Media Access Ctr

Maemo Linux

Nokia N10

USB

Data analysis

Data aggregation

Web service

TinyOS

Workstation

OS/hardware

platform

Sensor

Healthcare Gateway

Clinical System

SPINE

Integration With Clinical Information System

10

11

SPINE


Open
-
source framework for managing
wireless sensor networks


Discovery


1 motion sensor node


Configuration


Energy expenditure feature @ 1 Hz


Data processing


Calculate kilocalories per minute


SPINEController


Main application which runs a SPINE server,
communicates with Bluetooth daemon, runs
networking thread (WSDL Client)

12

Bluetooth Daemon


Communicates with weight scale and blood
pressure monitor


SDP (Service Discovery Protocol) and SPP (Serial
Port Profile) protocols


Hardware configured to send last measurement
automatically after measurement is taken


Communicates with SPINEController through
text files

13

Apache Axis2 WSDL Client


Runs in thread in SPINEController


Queues data


Sends data in queue to medical database


Automatically retries to send data if unsuccessful
(no wireless connectivity)


Data log files


All data


Queued data

Security and Privacy Overview


Security Requirements


Data confidentiality


Data integrity


Device authentication


User authentication and access control


Service availability






14

Vertical View Across Different Network Layers


Network security


involves the security issues from link to transport layer
security.


provides communication platform security service, including
data confidentiality, integrity, source authentication, service
availability (e.g., resilience to DoS/jamming attacks)


independent of application semantics


Application security


Web security/ Web service security.(e.g., resilience to SQL
injection, cross
-
site scripting)


User authentication and access control


Data access policy


Ensures the consistency between the privacy policy and
workflow

15

Security Mechanisms


Existing security mechanisms and solutions to
leverage


Web security solutions


SSL


TinySec


New security service to implement


Device authentication


Sensor
-
to
-
gateway secure communication


Resilience to jamming attack
--

channel reallocation


Privacy policy enforcement


All above security mechanisms need to be integrated
in the system

16

Challenge: How to ensure the end
-
to
-
end system security


Network Security Architecture

17

Data sampling

Data analysis

Sensor control

Data analysis

Sensor control

Data aggregation

Web service

Secure Comm.

Sensor Auth.

Secure Communication

Sensor Authentication

Service Layer

TinyOS

Telos Mote

TinyOS

Telos Mote

Comm Layer

Channel reallocation

Channel reallocation

Maemo Linux

Nokia N10

USB

Data analysis

Data aggregation

Web service

TinyOS

Workstation

OS/hardware

platform

Sensor

Healthcare Gateway

Clinical System

SSL

Horizontal
--

along the message
communication path


Stage 1: between sensors and mobile gateway


IEEE 802.15.4 communication standard


Pre
-
key distribution


Sensor device authentication


Encryption and MAC generation based on SkipJack in TinySec


Computation: 5.3 ms


Verification 1.3~1.4ms


Bluetooth


Stage 2: between sensor fusion center and the
Vanderbilt web server.


SSL


Client device (or user) authentication


Data encryption and integration protection


Stage 3: Within Vanderbilt Clinical Information System


Integration of user authentication and access control policy with
workflow model



18

Application
-
Layer Security Architecture

Monitoring Screen

Web Service Layer

Alert Processing
Workflow

Data archive
workflow

Raw Data

(only a
lert

related)



data(
i
)



data(
i+x
)



Alerts



alert(k)



Pending Alerts



Patient(x)



Alert Validating
Screen

Raw Data



data(
i
)



data(
i+x
)



Detail

Alert

Sensor

collection

Policy Layer

Policy

Enforcement

Policy

Enforcement

20

Experiment on CHF Patient


5 hour experiment


Nokia N810 battery life approximately 4 hours


required battery change


Energy expenditure every minute


Weight, blood pressure, heart rate
measurement at beginning and end of
experiment


Hardware malfunction at end of experiment


Failed CRC checks on incoming serial packets

21

Experimental Results

Time (min)

Energy Expenditure (kCal / min)

raw data

moving avg.

22

Experimental Results

Time (min)

raw data

moving avg.

car

Energy Expenditure (kCal / min)

23

WAVE and Berkeley Fit


Social networking in mobile BSNs for health
applications


WAVE


API for Android OS


Sensor setup through SPINE framework


Data processing


Action recognition


Energy expenditure estimation


GPS functions


Berkeley Fit


Showcase application for WAVE


Encourages exercise through social interaction

24

Social Interaction


Compete to see who expends the most
energy each day


Users will see leaderboard with rankings


Exercise teams


Users exposed to both encouragement and
competition


Other features


1 mile, 5 mile, etc. competition runs for time


25

Planned Experiments


Study of 30 college students


Monitor energy expenditure


Phase 1


Control group with no social feedback


Phase 2


Add social feedback


Change in energy expenditure with social feedback
enabled?

26

Summary and Future Work


Our system is consistent with the existing clinical
enterprise practice, and thus have the capability to
scale and become part of the overall patient
management process.


Future Work


Full migration to Android


Current Android release has no support for Bluetooth


no
external sensors


Android 2.0 will have Bluetooth API


Distributed action recognition


Experiments on obese children


Extension of security models to sensor networking
system and integration with application
-
level
security models