Safe and Dependable Bio-Sensor Networking for Pervasive Healthcare

1
Safe and Dependable Bio-Sensor Networking for
Pervasive Healthcare

• SANDEEP K. S. GUPTA
• Department of Computer Science and Engineering
• (Affiliated with BMI, BME, EE)
• School of Computing and Informatics
• Ira A. Fulton School of Engineering
• Arizona State University
• Tempe, Arizona
• sandeep.gupta_at_asu.edu

2
IMPACT (Intelligent Mobile Pervasive Autonomic
Computing Technologies) LAB

• MISSION
• Creating Humane Technologies for an Ever
  Changing World.
• La Famiglia
• Capo di Tutti Capi Sandeep Gupta
• Consigliere Georgios Varsamopoulos (Post Doc)
• Soldatto
• Ayan Bannerjee (PhD)
• Ken Bannister (MS)
• Guofeng Deng (PhD)
• Gianni Giorgetti (PhD)
• Michael Jonas (PhD)
• Su-Jin Kim (PhD)
• Tridib Mukherjee (PhD)
• Qinghui Tang (PhD)
• Krishna Venkatasubramanian (PhD)
• Picciotto

http//impact.asu.edu
3
IMPACT Research

• Use-inspired research in pervasive computing
  wireless sensor networking

• Goal
• Pervasive Health monitoring
• Evaluation of medical applications
• Features
• Secure, Dependable and Reliable data collection,
  storage and communication
• Sponsor

• Goal
• Evaluation of crisis response management
• Features
• Theoretical model
• Performance evaluation
• Access control for crisis management
• Sponsor

Medical Devices, Mobile Pervasive Embedded Sensor
Networks
BOOK Fundamentals of Mobile and Pervasive
Computing, Publisher McGraw-Hill  Dec. 2004
4
Pervasive Healthcare
5
Holy Grail of Pervasive Healthcare
cheap enough technology will enable early
detection and a lot less folks will have a heart
attack, stroke or cancer going forward. .. A
nanochip will search your blood for cancer five
years before they grow uncontrollably . Your
doctors may not be certain about what is going on
inside your body but technology will.
6
Healthcare Today
Source Wikipedia

• Definition
• Healthcare, is the prevention, treatment, and
  management of illness and the preservation of
  mental and physical well being through the
  services offered by the medical, nursing, and
  allied health professions Wikipedia.
• Provision
• In most places healthcare is provided in
  organized manner through a system of medical
  facilities
• Provided through public sector, private or both

Presence of Universal Healthcare
Primary Delivery Model
7
An Aging World
Region-wise Percentage of population over 60

• Department of Health projects by 2050 over 20 of
  the population in U.S. will be above 65.
• Consequences
• Acute shortage of trained medical professionals.
• Bureau of Labor Statistics estimates 18,000 job
  openings for physicians created annually by
  physician retirements.
• Reduced healthcare delivery coverage .
• Increase in the medical costs.
• Solution
• Extend traditional delivery model, with
• Communication infrastructure
• Devices facilitating remote checkup, surgery
• Visualization and imaging tools
• Result Remote Monitoring models
• Telemedicine model
• Centralized monitoring model

Source World Population Prospects, The 1998
Revision, United Nations Secretariat
Region-wise World population by 2050
Extended Delivery Model
Source International Energy Agency (IEA)
8
Pervasive Healthcare
Use Pervasive Computing for day-to-day healthcare
management to enable real-time, continuous
patient monitoring
Body Area Network
Applications
Home-based Care
Sports Health Management
Disaster Relief Management
Medical Facility Management
GOAL Enable independent living, general
wellness and disease management.
9
Differences Advantages

• Pervasive Healthcare
• Continuous Patient Monitoring.
• Automated diagnosis and treatment.
• Utilizing medical facilities only if condition
  very serious.

• Current Healthcare
• Detect symptoms
• Go to medical facilities (professionals)
• Medical professional performs diagnosis and
  treatment.

• Automated
• Real-time
• Inexpensive
• Very efficient

• Manual
• Slow
• Costly
• In-efficient

Pervasive Healthcare Technology is Necessary to
Meet Future Needs
10
Pervasive Healthcare - Conceptual Overview
Feedback for Adaptation
Medical Sensor Plane
Management Plane
Knowledge Generation Plane
Doctor
Actuation
Patient
Data Collection
Knowledge

• Collect Medical contextual data
• Local Processing
• Medical Actuation

Generate Knowledge

• Important Issues
• Accuracy of knowledge generation, minimize false
  positives
• Ensure data does not overwhelm
• Focus doctors attention on important data
• Summarize data carefully

• Storage Management
• Sensor Management
• Generate Context

11
Issues in Pervasive Healthcare Systems

• Hardware
• Low power
• Minimal form factor
• Energy scavenging
• Packaging
• Material Constraints
• Long-term operation

• Software
• Reliability
• Energy-efficiency
• Security
• Fault-tolerance
• Context-awareness
• Actuation

• Organization Reimbursement
• Who will deploy and control such systems
  doctor/hospitals/third-party ?
• Who will cover the costs of installation,
  management and repair ?
• Will insurance cover such systems ?

• A good pervasive healthcare solution is the one
  which takes into account
• The limitations/requirements imposed by the
  hardware software components
• Regulatory constraints
• Personalized requirements of each patient
• in its design.

12
Talk Overview
Minimize heat generated by health monitoring
sensors
Improve patient privacy by using efficient data
security protocols.
Provide automated emergency handling capability
Improve energy-efficiency of the medical sensors
used.
Context awareness for adapting to change in the
patients health
Provide feedback to patients based on their
health data trends
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Ayushman
14
Ayushman A Pervasive Healthcare System
Sanskrit for long life
Environmental Sensors (Temperature etc)

• Project _at_ IMPACT Lab, Arizona State University
• To provide a dependable, non-intrusive, secure,
  real-time automated health monitoring.
• Should be scalable and flexible enough to be used
  in diverse scenarios from home based monitoring
  to disaster relief, with minimal customization.

Internet
Stargate Gateway
External Gateway
Central Server
Medical Sensors (EKG, BP) controlled By Mica2
motes
Medical Professional
Home/Ward Based Intelligence
Body Based Intelligence
Medical Facility Based Intelligence
Vision

• To provide a realistic environment (test-bed) for
  testing communication
• protocols and systems for medical
  applications. 

K. Venkatasubramanian, G. Deng, T. Mukherjee, J.
Quintero, V Annamalai and S. K. S.
Gupta, "Ayushman A Wireless Sensor Network Based
Health Monitoring Infrastructure and Testbed",
In Proc. of IEEE DCOSS June 2005
15
Medical Data Management Architecture
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Current Setup
database
RS232
Oximeter
Base Station
802.11
ZigBee
Central Server
Blood Pressure
Environmental Data (accelerometer, Temperature,
humidity, Light)
Bluetooth
Internet
Body Area Network

• Properties
• Hardware and software based architecture
• Multi-tiered organization
• Real-time, continuous data collection
• Query support (past, current data)
• Remote monitoring capability through the Internet
• Simple alarm generation

Remote Clients
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Enabling Technologies

Commercially available sensor boards
Open source OS with support for ad hoc networking
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Phone to WSN Interface

• Design Principles
• To minimize the changes to the existing WSN
  architecture (required to maintain backward
  compatibility with previous apps.)
• To leverage COTS hardware and existing software
  solutions (to minimize the development time).
• Issues to address
• Phone to sensors interface
• Data handling on the cell phone

Monitoring and Control Software
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Phone to Sensor Interface

• We evaluated three options
• Direct Connection
• WAN Connection
• Bluetooth

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Standard Data Flow
TinyOS Pkt Data
TinyOS Pkt Data
0101101100110
RS232 / USB
Processing Data Storage
WSN Gateway
TinyOS Cmd
TinyOS Pkt Cmd
0101101100110
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Bluetooth Gateway
RS232 / USB
Data
Data
MCU
Radio
UART
Cmd
Cmd
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Software on the Phone
Bluetooth
RFCOMM

• Phone Software
• Establishes the connection to the BT gateway
• Handles the TinyOS packet structure
• Coverts the raw sensor data in engineering units
• Provides the users with a GUI
• Dispatch control commands to the WSN

23
The Personal Sensor Network
24
Desktop Screen Shot
Patient Details
Current Sensor Value
Sensor Values Trend
Query Result Archived Data
Location of Server
25
VIDEO DEMO Using CellPhonefor Sensor Monitoring
26
Other Similar Projects
http//www.intel.com/research/prohealth/

• Proactive Health Project _at_ Intel
• Developing sensor network based pervasive
  computing systems
• Managing daily health and wellness of people at
  homes
• Proactively anticipate patients need and improve
  quality of life.
• Code Blue Project Sensor network based health
  monitoring
• _at_ Harvard
• Developing sensor network based medical
  applications for
• Emergency Care
• Disaster Management
• Stroke patient rehabilitation
• AMON Project _at_ ETH, Zurich
• Developing multi-functional wearable health
  monitor
• E.g. BP, pulse, SpO2, ECG, Temperature
• Aware Project _at_ the Center Pervasive Healthcare,
• University of Aarhus, Denmark.
• Applying context aware computing to hospital
  scenarios

http//www.eecs.harvard.edu/mdw/proj/codeblue/
http//www2.wearable.ethz.ch/amon.0.html
http//www.pervasive-interaction.org/Aware/
http//www.csail.mit.edu/events/news/2006/smart.ht
ml
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Third International Conference on Body Area
Networks Tempe, Arizona, USA March 13 March
15, 2008

• Synopsis
• Recent advances in the field of wireless sensor
  networks have moved them beyond their traditional
  areas of application in monitoring of remote and
  mobile environments. Sensor networks are
  increasingly being deployed within and at the
  surface of the human body to form Body Area
  Networks (BodyNets). They can be utilized in
  diverse applications such as
• Physiological monitoring
• Human computer interactions
• Education and entertainment through interactive
  games.
• BodyNets 2008 aims to establish a forum to bring
  together research professionals from diverse
  fields including computer science, biomedical
  engineering and medicine in both academia and
  industry to address the technical, social,
  systems and application issues related to
  BodyNets.

Organizing Committee
General Chair Sethuraman (Panch) Panchanathan,
ASU, USA TPC Chair Sandeep K. S. Gupta, ASU,
USA TPC Co-Chairs Daniel Siewiorek, CMU,
USA Timothy Buchman, WUSTL, USA Loren
Schwiebert, WSU, USA Industry Track
Co-Chairs Carlos Cordeiro, Intel Research,
USA Mary-Murphy Hoye, Intel Corp.,
USA Conference Coordinator Zita Rozsa, ICST,
Europe
Publicity Chair Webmaster Krishna
Venkatasubramanian, ASU, USA Local Arrangement
Chair Georgios Varsamopoulos, ASU,
USA Publications Chair Leif Hanlen, NICTA,
Australia Sponsorship Chair Gianni Giorgetti,
ASU, USA Steering Committee Chair Imrich
Chlamtac, Create-Net, Italy General Vice-Chair
David Tacconi, Create-Net, Italy
Address Life Sciences A-wing, Room 190 Arizona
State University (Tempe Campus) Tempe, AZ
85287USA
http//www.bodynets.org/
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BodyNets 2008
Keynote Speakers Title Artificial Intelligence
on the Body, in the Home, and Beyond Speaker Dr.
Diane Cook, Washington State University Title
On Innovation, Quality of Life and Technology of
BodyNets Speaker Dr. Sundaresan Jayaraman,
Georgia Institute of Technology

• Tutorial Speakers
• Title
• Body Sensor Networks for Health-care Monitoring
  Premises, Challenges and Prospective
• Speaker
• Dr. Roozbeh Jafari, University of Texas at Dallas
  
• Title
• Energy-efficient Design for Mobile Phone-Centered
  Wireless Body Area Networks
• Speaker
• Dr. Lin Zhong, Rice University

29
BodyNets 2008 Technical Program
Communication Techniques Effect of quantization
on beamforming in binaural hearing aids Sriram
Srinivasan , Ashish Pandharipande , Kees
Janse Investigation of Wireless Data
Transmission between Hearing Aids Crista L.
Malick, Steven J. Franke, Qi Xie, Jennifer T.
Bernhard, Mitesh Parikh, Douglas L. Jones, and
Francois Callias. Body-Coupled Communication
for Body Sensor Networks Adam Barth, Stephen
Wilson, Mark Hanson, Harry Powell , Dincer
Unluer, John Lach Analysis of Body Sensor
Network Using Human Body as the Channel Jerald
Yoo, Namjun Cho, Hoi-Jun Yoo
Software Technology Platforms Distributed
Pervasive Services using Group Service
communication supporting Body Area Networks
Christopher Foley, Sasitharan Balasubramaniam,
Dimitri Botvich, WilliamDonnelly, Stefan
Michaelis, Jens Schmutzer, Thomas Stair Service
Discovery and Composition in Body Area Networks
Matteo Coloberti, Clemens Lombriser , Daniel
Roggen, Gerhard Troester, Renata Guarneri,
Daniele Riboni A Framework for Creating
Healthcare Monitoring Applications Using Wireless
Body Sensor Networks Sameer Iyengar , Filippo
Tempia , Raffaele Gravina , Antonio Guerrieri ,
Giancarlo Fortino , Alberto Sangiovanni-Vincentell
i CareNet An Integrated Wireless Sensor
Networking Environment for Remote Healthcare
Yuan Xue , Stephen Wicker , Philip J Kuryloski ,
Shanshan Jiang, Roozbeh Jafari, Ruzena Bajcsy ,
Yanchuan Cao , Sameer Iyengar
Communication Protocols On the performance of
Bluetooth and IEEE 802.15.4 radios in a body area
network Rahul C. Shah, Lama Nachman and
Chieh-yih Wan IEEE body area network and medical
implant communication Bin Zhen, Huan-Bang Li,
Ryuji Kohno ZigBee-Based Wireless Sensor Network
for Real-Time Transmission of Wavelet Compression
of ECG Signals Shuo-Jen Hsu, Shih-Wei Chen,
Wan-Ya Chen, Hsin-Hsien Wu, You-Yin Chen Novel
QoS Scheduling and Energy-saving MAC protocol for
Body Sensor Networks Optimization Begonya Otal,
Luis Alonso, Christos Verikoukis
Powering and Energy Adapting Radio Transmit
Power in Wireless Body Area Sensor Networks Shuo
Xiao, Vijay Sivaraman, and Alison
Burdett Approaches to Self-Powered Biochemical
Sensors for In-Vivo Application Eric M. Yeatman,
Danny OHare, Cate Dobson, Eleni Bitziou Joint
Encryption/Multiple Access for Body Area Sensor
Networks Walter D. Leon-Salas, Deep Medhi, Yugi
Lee
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BodyNets 2008 Technical Program
HCI/Wearable Computing SMASH A Distributed
Sensing and Processing Garment for the
Classification of Upper Body Postures Holger
Harms, Oliver Amft, Daniel Roggen, Gerhard
Troester Modeling of EOG and Electrode Position
Optimization for Human-Computer Interface Niina
Nojd, Jari Hyttinen An Architecture for Smart
Textiles Mark T Jones, Thomas Martin, Braden
Sawyer
Activity and Signal Classification ECG
Segmentation in a Body Sensor Network Using
Adaptive Hidden Markov Models Huaming Li, Jindong
Tan Body Posture Identification using Hidden
Markov Model with Wearable Sensor
Networks Muhannad Quwaider, Subir
Biswas Classifying Wheelchair Propulsion
Patterns with a Wrist Mounted Accelerometer
Brian French, Asim Smailagic, Dan Siewiorek,
Vishnu Ambur, Divya Tyamagundlu Analysis of
human performance using physiological data
streams Gaurav Pradhan, Balakrishnan Prabhakaran
Medical Applications The SmartCane System An
Assistive Device for Geriatrics Winston Wu,
Lawrence Au, Brett Jordan, Thanos Stathopoulos,
Maxim Batalin, William Kaiser, Alireza
Vahdatpour, Majid Sarrafzadeh, Meika Fang, Joshua
Chodosh Preliminary Studies for the development
of a Ubiquitous Computing and Health-monitoring
System for Wheelchair Users Jongbae Kim A
wireless platform for fall and mobility
monitoring in health care Pepijn W J Van de Ven,
Alan Bourke, John Nelson, Gearaid
OLaighin Physiological Signal Monitoring in the
Waiting Areas of an Emergency Room Dorothy
Curtis, Jason Waterman, Jacob Bailey, Eugene
Shih, Thomas Stair, John Guttag, Robert Greenes,
Lucila Ohno-Machado
Non-Medical Applications The Speckled Golfer D K
Arvind A Wearable Wireless RFID System for
Accessible Shopping Environments Sreekar
Krishna, Vineeth Balasubramanian , Narayanan
Chatapuram Krishnan , Terri Hedgpeth SerPens --
A Tool for Semantically Enriched Location
Information on Personal Devices Sourav
Bhattacharya, Joonas Kukkonen, Petteri Nurmi,
Patrik Floreen
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Medical Sensor Safety
32
Tissue Heating

• Medical sensors implanted/worn by human need to
  be safe.
• Sensor activity causes heating in the tissue.
• Heating caused by RF inductive powering
• Radiation from wireless communication
• Power dissipation of circuitry
• Goal minimize tissue heating.
• Two solutions
• Communication scheduling for
• minimizing thermal effects
• Rotate cluster leader balance energy usage
  distribute heat dissipation
• Thermal aware routing route around thermal
  hotspots

Tissue Blow-up
Heating Zone
Cluster leader
33
Communication Scheduling

• System Model
• Consider only one cluster
• 2D Model
• Rotate cluster head to distribute energy
  consumption reduce heating

• Requirements
• FCC Regulation
• Antenna vs. Freq trade-off

SAR s E2 / ? (W/kg)
E induced Electric Field p tissue density s
electric conductivity of tissue
IEEE Requirement (1g Tissue)
Temperature Rise Pennes Bio-heat Equation
SAR 0.4W/Kg
Whole Body Average
SAR 8W/Kg
Peak Local
CE
SAR .08W/Kg
Whole Body Average
SAR 1.6W/Kg
Peak Local
UCE
Heat by metabolism
Heat by radiation
Heat by power dissipation
Heat accumulated
Heat transfer by convection
Heat transfer by conduction

• Solution
• Random selection may lead to higher temperature
  rise
• Similar to Traveling salesman problem but with
  dynamic metric
• TIP Heuristic Leader selection based on sensor
  location, rotation history

Results
FDTD enumeration
Optimal
720960 hrs (est.)
FDTD Genetic Algorithm
Near Optimal
100 hrs (est.)
TIP enumeration
Optimal
7.6 hrs
Near Optimal
TIP Genetic Algorithm
5 min

• Four Approaches
• FDTD enumeration
• FDTD Genetic Algorithm
• TIP enumeration
• TIP Genetic Algorithm

Temperature
Temp rise in sensor surroundings
Comparative Result
Coordinate y
Coordinate x
Q. Tang, N. Tummala, S. K. S. Gupta, and L.
Schwiebert, Communication scheduling to minimize
thermal effects of implanted biosensor networks
in homogeneous tissue, Proc of IEEE Transactions
of Biomedical Engineering
34
Thermal Aware Routing
Area Hotspot

• In vivo environment maybe sensitive to the
  heating of power dissipation and radiation of
  Implanted sensors
• Energy/load balancing is not equal to heating
  balancing large time scale vs. short time scale

Link Hotspot

• Solution
• Modeling EM radiation and power dissipation of
  sensors
• Identifying hotspot area
• Withdrawal strategy to avoid overheated area
• Averaging power consumption and heat dissipation
• Slight degradation of delay

Temperature distribution of TARA
Q. Tang, N. Tummala, S. K. S. Gupta, and L.
Schwiebert, TARA Thermal-Aware Routing
Algorithm for Implanted Sensor Networks, Intl
Conference on Distributed Computing in Sensor
Systems, 2005
35
Information Security
36
Security in Pervasive Healthcare

• Context
• Patient data is transmitted wirelessly by low
  capability sensors
• Patient data is therefore easy to eavesdrop on
• Security schemes utilized may not be strong
  enough for cryptanalysis
• Patient data is stored in electronic format and
  is available through the Internet
• Makes it easy to access from around the world and
  easy to copy
• Data can be moved across administrative
  boundaries easily bypassing legal issues.
• Electronic health records store more and more
  sensitive information such as psych reports and
  HIV status
• Preserving patients privacy is a legal
  requirement (HIPAA)
• Excruciating Factors
• Wireless connectivity is always on
• No clear understanding of

37
Security Related Issues

• New Attacks
• Fake emergency warnings.
• Legitimate emergency warnings prevented from
  being reported in times.
• Unnecessary communication by malicious entity
  with sensors can cause
• Battery power depletion
• Tissue heating

• Technology
• Efficient cryptographic primitives
• Cheaper encryption, hash functions
• Better sensor hardware design
• Cheap, tamper-resistant sensor hardware
• Better communication protocol design
• Better techniques for controlling access to
  patient EHR

• Legislation
• Health Information Privacy and Accountability Act
  (HIPAA)
• Passed in 1995
• Provides necessary privacy protection for health
  data
• Developed in response to public concern over
  abuse of privacy in health information
• Establishes categories of health information
  which may be used or disclosed

• Requirements
• Integrity - Ensure that information is accurate,
  complete, and has not been altered in any way.
• Confidentiality - Ensure that information is only
  disclosed to those who are authorized to see it.
• Authentication Ensure correctness of claimed
  identity.
• Authorization Ensure permissions granted for
  actions performed by entity.

38
Physiological Value based Security

• Aim
• Use of the physiological values (PV) from the
  body to exchange the keys.
• Possible Examples
• Simple
• Blood Pressure, Heart Rate, Glucose level
• Complex
• Temporal variations in different PVs.
• Combination of multiple PV

• Advantages
• Easier and safer key generation
• Reduced Deployment Costs
• Plug-n-Play like capability with Body Sensor
  Networks

GOOD CHOICES Inter-Pulse-Interval FIND OTHERS
Sriram Cherukuri, Krishna K. Venkatasubramanian,
Sandeep K. S. Gupta, BioSec A Biometric Based
Approach for Securing Communication in Wireless
Networks of Biosensors Implanted in the Human
Body, in Proc of IEEE ICPP Workshops, 2003
39
Time Domain Analysis

• Measurement
• Measure Inter-pulse intervals (IPI) from two
  sources EKG and PPG
• Sampling Frequency 1000Hz, 14 health patients,
  85 older and sick patients
• Quantization
• 67 consecutive IPIs from a person quantized into
  2, 128 bit binary streams (keys)
• Comparison criteria False Rejection (FR) and
  False Acceptance (FA)
• Result
• The value of two keys, but close for same person.
• Comparison criteria Hamming Distance
• A threshold of 40 bits (older patients)
  minimizes FR FA
• Results for two PPG data series based IPI
  collection yielded better result

First Proposed C. C. Y. Poon and Yuan-Ting Zhang
and Shu-Di Bao, A Novel Biometrics Method To
Secure Wireless Body Area Sensor Networks for
Telemedicine And M-Health, IEEE Communications
Magazine, 44(4), 2006, pp 73-81. Has been
verified by us.
40
Frequency Domain Analysis

• Steps
• Collect 256 IPI values (EKG PPG)
• MIT Physio Bank Database
• For both
• Divide them into windows of 16 and perform 16
  point FFT
• Drop latter half coefficients generate FCT
• For each row in FCT
• Quantize into 4 bits
• Yielding 16 blocks, 32 bits each.
• Pair wise compute hamming distance of each of the
  16 blocks for EKG with PPG
• Find the closest blocks and realign them
• Choose first 4 blocks for 128 bit key
• Results
• Same person keys 10 apart, different person
  keys 30-40 apart
• For EKG, keys identical for same person

41
PV Based Data Security Protocol
Measure Pre-defined PV _at_ Sender PVs Receiver PVr
Generate Random Key _at_ sender
KeyRand
Encrypt message with Key Rand
C EKeyRand(Message)
? PVs ? KeyRand
Hide KeyRand using PV
Send encrypted message
Receiver encrypted message
KeyRand PVr ? ?
Unhide KeyRand using PVr
Message DPVr(C)
Decrypt message with Key Rand
K. Venkatasubramanian, and S.K.S. Gupta,
"Security For Pervasive Health Monitoring
Sensor Applications", To Appear in Proc of 4th
Intl. Conf. on Intelligent Sensing and
Information Processing (ICISIP), December 2006.
42
Criticality-Awareness
43
Emergency Management

• Critical Events
• Cannot be responded to, using the routine set of
  capabilities of the subjects.
• Requirements
• Request based context evaluation is inadequate.
• Continuous context monitoring is required.

Medical Emergency
Fire
Hurricane
Flooding

• Criticality
• Consequences of critical events characterized by
  urgency for taking remedial (response) actions
• Response actions are usually exceptional in
  nature
• Usually happen in groups (earthquake severely
  hurt people)

Exceptional Actions
Normal Situation
Criticality
Criticality Awareness improves System
DEPENDABILITY
44
Important Properties of Criticality

• Window of Opportunity (Wo)
• Time within which all mitigative actions should
  ideally be taken
• Value of Wo is criticality dependent.
• Example
• 90 Sec (Data Center, cooling failure)
• 5 Min (Tornado)
• 1 Hour (Heart Attack)
• 30 Days (Disaster Recovery)

• Responsiveness
• Measures the speed with which the system initiate
  detection of criticalities
• Correctness
• Determines the accuracy and confidence of the
  detection process.

D Ta Wo
Time for Initiating mitigative actions
Time to take mitigative actions
45
Criticality Mitigation Process
Detection (Humans, sensors etc)
Evaluation
Planning/ Scheduling
Enabling Actions
Planning Scheduling
Execution of Actions (Humans, Agents etc)
Control Access to Resources
46
Criticality and Access Control

Hurricane (Natural Disasters)
Destruction and Flooding
Rescue

• FURTHER
• Traditional access to EHR is REACTIVE
• Initiated by medical professional after observing
  the patient
• Slow response
• How to speed it up

• Provide medical information (EHR) automatically
  through
• Patient medical sensors/ PDA /cell -phone
  directly
• Preserve patient privacy as per HIPAA disclose
  EHR only to associated doctors
• Rescue doctors dont get access as per HIPAA
• How to make it work

• Proactive system monitoring.
• Facilitates reaction within a window-of-opportuni
  ty.
• Provides privileges for non traditional accesses
  for criticality mitigation.
• Properties
• Proactive takes access control decisions
  independently of specific user request
• Alternate Privilege Provision provide any
  privileges for mitigation,
• Wo-aware rescind privileges after Wo expires
• Dynamicity not adhere to any assumpitons
  regarding criticality or its behavior
• Non-Repudiability maintain detailed records of
  actions taken during criticality

Criticality Aware Access Control
S.K.S Gupta, T. Mukherjee, K. Venkatasubramanian,
Criticality Aware Access Control Models for
Pervasive Applications, In Proc of IEEE
Pervasive Computing, 2006.
47
Criticality Aware Access Control
Detect Criticality
Plan/Schedule Actions
Execute Actions
Grant Privileges

• Locate people, provide aid, based on ailment
• Refer EHR for informed diagnosis and treatment

For each trapped hurt person , their pervasive
health monitoring system

• Obtain info on doctors in vicinity.
• Check if A1, A2 and A3 allowed for them
• If not present, generate privileges
• P1. View past health info
• P2. View current health info
• P3 . View allergy information
• Assign privileges to doctors simultaneously.
• Record actions, if taken

• People Injured in the aftermath of a natural
  disaster
• Check periodically for new criticalities
• New plan schedule if Wo expire or new
  criticality
• Reset all previous privilege assignment

Proactive

• Obtain health information
• Compute type of ailment, possible treatment, Wo.
• Generate list of actions to facilitate
  treatment
• A1. Provide past health info
• A2. Provide current health info
• A3. Provide allergy information

Alternate Privilege Provision
Wo-aware
Non-Repudiability

• Whole process carried out by Pervasive
  healthcare system
• Actions generated may sometimes contradict, such
  cases may mandate sequential assignment of
  privileges
• Role-Privilege model used for implementation,
  where doctors role changed for assigning
  privileges.
• Privileges provided for actions generated and
  not predetermined for different criticalities

• Detection process done periodically interval
  system dependent

• Carried out by doctors

Dynamicity
Comments
48
Context Awareness
Acutation
Other Issues
Energy-Efficiency
49
Energy Efficiency
Solutions

• Need
• Sensors have very small battery source.
• Sensors need to be active for long time
  durations.
• For implantable sensors, it is not possible to
  replace battery at short intervals.
• Challenge
• Battery power not increasing at same rate as
  processing power.
• Small size (hence less energy) of the batteries
  in sensors.

Better Battery
Solar Energy
Vibration
Body Thermal Power
50
Energy-Efficiency Source Coding Biosensor
Communication

• M Symbols 2k

• Need
• Sensors have
• low data rate.
• Short range of operation.
• Demands low power and low complexity at both
  circuit and system level.

• Solution
• Minimum Energy Coding
• Sources with unknown statistics.
• Minimum energy codes considered
• More energy efficient.
• Only one bit-1 per code
• Achieves
• Lesser number of bit-1 in the transmitted code
• Safely assign to source symbols of any
  probability of occurrence.
• Code Rate (k / n) (k / 2k-1)

System Model
Y. Prakash, S.K.S Gupta, Energy Efficient Source
Coding and Modulation for Wireless Applications,
IEEE Wireless Communications and Networking
Conference, 2003. WCNC 2003. Volume 1, 16-20
March 2003, Page(s) 212-217.
51
Context Generation

• Medical Context
• Is an aggregate of 4 base contexts.
• Each physiological event has to be characterized
  by all 4 base contexts for accurate
  understanding of patients
• health.
• A contextual template can be created for
  specific physiological events for future
  reference.

Physiological (EKG, Perspiration, Heart Rate)
Context Processor
Spatial (Home, Gym, Office, Hospital, Park)
Knowledge
Aggregate Context
Temporal (Morning, Evening, Night)
Sensor Network

• Challenges
• How to determine the aggregate medical context
  from the four base contexts?
• How to create a contextual template for a
  patient?

Environmental (Humidity, Temp)
Base Context
52
Knowledge-based Actuation
Back-end processing
Raw data
Context Processor
Knowledge
Physiological data
Medical Aid
Environmental data
Diagnosis Treatment
Spatial data
Temporal data
Actuation
take medication X, ambulance arriving
53
Conclusions

• The global e-healthcare and telemedicine market
  is currently valued at 7billion (Cap Gemini
  Ernst Young) and is showing an explosive growth.
• Such systems will become increasingly more useful
  because of the aging world population.
• Next generation medical system are being designed
  to provide pervasive, scalable, cheap,
  non-intrusive heathcare to all.
• Aysuhman - a sensor network based health
  monitoring system that is dependable, secure and
  safe.

Prevention (PHC) is Better (Cheaper) than Cure
(Traditional Healthcare)
54
List of Publications

• L. Schwiebert, S. K. S. Gupta, J. Weinmann et
  al., Research Challenges in Wireless Networks of
  Biomedical Sensors, The Seventh Annual
  International Conference on Mobile Computing and
  Networking, pp 151-165, Rome Italy, July 2001.
• Q. Tang, N. Tummala, S. K. S. Gupta, and L.
  Schwiebert, Communication scheduling to minimize
  thermal effects of implanted biosensor networks
  in homogeneous tissue, Proc of IEEE Transactions
  of Biomedical Engineering.
• Q. Tang, N. Tummala, S. K. S. Gupta, and L.
  Schwiebert, TARA Thermal-Aware Routing
  Algorithm for Implanted Sensor Networks, Intl
  Conference on Distributed Computing in Sensor
  Systems, 2005
• Sriram Cherukuri, Krishna K. Venkatasubramanian,
  Sandeep K. S. Gupta, BioSec A Biometric Based
  Approach for Securing Communication in Wireless
  Networks of Biosensors Implanted in the Human
  Body, in Proc of IEEE ICPP Workshops, 2003
• K. Venkatasubramanian, and S.K.S. Gupta,
  "Security For Pervasive Health Monitoring Sensor
  Applications", To Appear in Proc of 4th Intl.
  Conf. on Intelligent Sensing and Information
  Processing (ICISIP), December 2006.
• S.K.S Gupta, T. Mukherjee, K. Venkatasubramanian,
  Criticality Aware Access Control Models for
  Pervasive Applications, In Proc of IEEE
  Pervasive Computing, 2006.
• S. K. S. Gupta, T. Mukherjee, K.
  Venkatasubramanian, and T. Taylor Proximity-based
  Access Control in Smart ED Environments, In Proc
  of 4th IEEE Conference on Pervasive Computing
  Workshops (Ubicare), Pisa, Italy, 2006.
• Y. Prakash, S.K.S Gupta, Energy Efficient Source
  Coding and Modulation for Wireless Applications,
  IEEE Wireless Communications and Networking
  Conference, 2003. WCNC 2003. Volume 1, 16-20
  March 2003, Page(s) 212-217.
• K. Venkatasubramanian, G. Deng, T. Mukherjee, J.
  Quintero, V Annamalai and S. K. S. Gupta,
  "Ayushman A Wireless Sensor Network Based Health
  Monitoring Infrastructure and Testbed", In Proc.
  of IEEE DCOSS June 2005