DPNM, POSTECH Master Thesis Defense 1/22 Efficient Energy Scheduling of WBAN Sensors for...

DPNM, POSTECH Master Thesis Defense 1/22

Efficient Energy Scheduling of WBAN Sensors for U-Healthcare

Hyeok Soo Choi

Co-Supervisors: James Won-Ki Hong & Nazim Agoulmine

DPNM Lab.

Department of Computer Science and EngineeringPOSTECH, Korea

[email protected]

June 20, 2011


Outline

IntroductionProblem StatementGeneral Description of the ApproachDetails of the Solution

Mutual Information

Criteria of Sensor Selection

Implementation IssuesSimulation & ResultsConcluding Remarks


Introduction(1/2)

Image source: http://mediax.stanford.edu

Percentage of the population over the age of 65

Aging society and health care problem• Unsustainable health care cost• Health-care cost of elderly is very expensive• Early detection of disease treat disease earlier less expen-

sive

Advancement in low-power electronics, sensor technologies and wireless communication technologies

• Possibility to development small-sized biomedical sensors to monitor more efficiently elderly remotely.

Motivation

(year)

(%)

U-Health Smart HomeWireless Body Area

Network


Introduction(2/2) Wireless Body Area Network (WBAN)

This enable wireless communication between several medical sensor on the human’s body

In WBAN, sensors aims at monitoring human’s health status, activity, motion pattern, etc.

EEG

ECG

CoordinatorSpO2

Temperature

Motion Sensor

WBAN


Problem Statement(1/3)

SizeSensors needs to be as small as possible to be accepted by elderly

Idea of nano sensors !

EnergySmall size small battery

Small battery small life time

Example5mW

1.5V


Problem Statement(2/3)

High energy consumption

Existing WBANs use a Communi-cation based schema

Sensors are configured according to the communication needs (e.g., duty cycle)

Sensed data is regularly sent to the coordinator even though the data is not needed (because there is no anomaly)

1 time/ min

1 time / sec

1 time/ hour

10 times/ min

1 time/ hour


Problem Statement(3/3)Research question

Is it possible to define an alternative communication reducing the energy consumption while not missing important information that are necessary to detect health anomalies?

Define an WBAN communication to detect health anomaly (disease)

With reducing the energy consumption

With not missing important information

Research Goal


General Description of the Solution(1/2)

1

2

Idea: Inspiration from doctors method-ology Disease or

no Disease ?

1. Doctors do not try to check all symp-toms but only the most important ones (heart beat, pressure, tempera-ture)

2. If they’re ok, no further investigation3. Otherwise investigate more symp-

toms to detect a disease


General Description of the Solution(2/2) Information based schema

What is the relation between the symptoms and the diseases ?

Doctors should provide the data

What symptoms to monitor ?

We propose to use the concept of mutual information to identify the symptoms that provide the most information gain to detect particular diseases

Which sensors to activate ?

Identify the sensors that can de-tect these symptoms and ONLY activate them when necessary

What next in case of anomaly ?

Add more sensors (symptoms to detect) to increase the informa-tion gain

What is the sensor that has the highest impact on the coor-

dinator’s knowl-edge?


Mutual Information

Definition of mutual information

Measures the mutual dependence of the two variables

ExampleTwo variables, X and Y have high mutual information if you can predict a lot about one from the other.

If X and Y are independent, then knowing X does not give any information about Y, so their mutual informa-tion is zero


Entropy Linked to Healthcare Disease

An abnormal condition affecting the body of an organism

Construed to be a medical condition associated with spe-cific symptoms and signs

SymptomA departure from normal function or feeling

Indicating the presence of disease or abnormalityInformation Gain


Criteria of Sensor Selection(1/2)

H(D|S) H(S|D)I(D; S)

H(D) H(S)

H(D, S)


Criteria of Sensor Selection(2/2)

IG(ei | ej) =

H(D) H(S1)H(S3) H(S2)


Coordinator-Sensors CommunicationBased on the IEEE 802.15.4

Beacon enabled modeContention Access Period (CAP)

CoordinatorCalculate information gain per every cycleID Pending Address Fields (PAF)Beacon frame broadcast

Medical sensorsDoes PAF contain medical sensor’s ID?

• YES senses human body and then transmits sensed data to the co-ordinator

• NO goes to sleep mode


Simulation Environment(1/2) Simulation tool : NS-2 (version 2.31) MAC protocol : IEEE 802.15.4

Beacon enable mode

Routing protocol : NOAH (No Ad-Hoc Routing Agent) The number of sensor nodes: 7 The number of diseases: 5 The number of symptoms: 7 Simulation time : 1 day


Simulation Environment(2/2)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

D1

D2

D3

D4

D5

24(h)

Part 1 Part 2 Part 3

Diseases occurDiseases do not happen

D2 occurs from 3 AM to 4 AM

Simulation Scenario


Simulation Results(1/3)

Total energy consumptionCB’s energy consumption rate is con-

stant

IB’s energy consumption rate changes according to the user’s

health state

Part1 Part2 Part3



Energy consumption per sensors

•Sensors that be-long to CB (S8 ~ S14) have con-stant energy con-sumption rate

•Compact subset (S1, S2) acts like sensors that be-long to CB



Energy consumption per sensors

•Other sensors’ (S3 ~ S7) energy consumption rate changes accord-ing to the user’s health state



Expiration time vs. Latency

Expiration Time (s)

Late

ncy(s

)


Concluding Remarks

We proposed an information based scheduling schema

Information gain model using mutual information

By introducing our solution, medical problem can be detected by medical WBAN on a longer period of time

Future worksFinds compact subset of sensors by defining more feasible information gain

Develops distributed information based communica-tion


Q & A


Simulation Scenario

Dis-ease Symptom

D2

D3

D4

D5

2

2

5

7

4

6

6

1

2

3

5

3

Relationship between disease and symptom

1 42D1 p(D1 | e1) > p(D1 | e2) > p(D1 | e4)


General Description of the Solution

Combining high information gain and energy effi-ciency

Use of a utility function

Combining the information gain and energy consumption

The objective is to choose the sensors which re-flect the larger dependency on the target dis-ease and which have the lower operational cost (including energy consumption).


Details of the Solution(1/2)

The operation cost function is defined as fol-lows

: set of outgoing links at node n

: duty cycle of sensor

: Power gain from transmitter of link k to the receiver of link l

C : total amount of initial energy


Details of the Solution(2/2)

The objective function is augmented with a weighted cost functions as follows

: information utility of including the symptom ej

: communication cost

: relative weight between the information utility and communication cost

Based on the objective function, the criterion for se-lecting the sensors has the following form


Communication Processes


IB Details of the Solution Algorithm (1/3)

The mutual information between diseases and symptoms are calculated off-line.

The coordinator detect and register information about:

List of medical sensorsInitial energy level

Start

Initialization

Send information query

Wait for information

Update knowledge

Knowledge is good enough?

Yes

No

Sensor selection

Anomaly detection



Start

Initialization



Update knowledge


Yes

No

Sensor selection

The coordinator Evaluates the knowledge

The knowledge is updated as measurement as received by the coordinator.

The probability of detection of a particular disease depends on the collected information:

p(D|{ei} i∈B)

Anomaly detection



Start

Initialization



Update knowledge


Yes

No

Sensor selection

The coordinator selects the WBAN sensor which maximizes the infor-mation utility based on the knowledge state, p(D|{ei} i∈B)

sends a request to the se-lected sensor to activate

updates the knowledge state

e.g. p(D|{ei} i∈B ∪ ej)

Anomaly detection


Notation

Superscript t : timeSubscript i ∈ {1, . . . , K} : sensor indexSubscript j ∈ {1, . . . , N} : diagnosis

indexDj

(t) : Diagnosis state at time t

Ei(t) : Measurement of sensor i at time

tE(t) : Measurement history up to time t

E(t) = {e(0), e(1), … e(t)}E(t) : Collection of all sensor mea-

surements at time t E(t) = {e1

(t), e2(t), …

em(t)}


Information Utility MeasureMutual Information

Given two random variables x and y, their mutual in-formation is defined in term of their probabilistic den-sity functions p(x), p(y), and p(x, y)

The information contribution of sensor j with mea-surement ej

(t+1) can be given by the sequential Bayesian estimation

The mutual information reflects the expected amount of change in the posterior knowledge brought by sen-sor j


Demo Room

Light Path forObstacles avoidance

ECG SensorWireless

Sensor Base

Context – U-Health Medical Smart Home @ Postech

Video Control System

Remote AirConditioning Control

Environment Sensors(Light, Temperature,

Humidity)

Remote WindowOpening /Closing

Control

Light Path forObstacles Avoid-

ance

DPNM, POSTECH Master Thesis Defense 1/22 Efficient Energy Scheduling of WBAN Sensors for...

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