efficient energy scheduling of wban sensors for u-healthcaredpnm.postech.ac.kr › thesis › 11 ›...

DPNM, POSTECH Master Thesis Defense 1/22

Efficient Energy Scheduling of WBAN Sensors for U-Healthcare

Hyeok Soo ChoiCo-Supervisors: James Won-Ki Hong & Nazim Agoulmine

DPNM Lab.

Department of Computer Science and EngineeringPOSTECH, Korea

[email protected]

June 20, 2011


OutlineIntroductionProblem StatementGeneral Description of the ApproachDetails of the Solution

Mutual InformationCriteria of Sensor SelectionImplementation Issues

Simulation & 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 expensive

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

EEG

ECG

CoordinatorSpO2

Temperature

Motion Sensor

WBAN


Problem Statement(1/3)Size

Sensors needs to be as small as possible to be accepted by elderlyIdea of nano sensors !

EnergySmall size small batterySmall battery small life time

Example5mW

1.5V


Problem Statement(2/3)

High energy consumption

Existing WBANs use a Communication 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 consumptionWith not missing important information

Research Goal


General Description of the Solution(1/2)

1

2

Idea: Inspiration from doctors methodology

Disease or no Disease ?

1. Doctors do not try to check all symptoms but only the most important ones (heart beat, pressure, temperature)

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

symptoms 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 dataWhat 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 detect these symptoms and ONLY activate them when necessary

What next in case of anomaly ?Add more sensors (symptoms to detect) to increase the information gain

What is the sensor that has the highest impact

on the coordinator’s knowledge?


Mutual InformationDefinition of mutual information

Measures the mutual dependence of the two variablesExample

Two 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 information is zero


Entropy Linked to HealthcareDisease

An abnormal condition affecting the body of an organismConstrued to be a medical condition associated with specific symptoms and signs

SymptomA departure from normal function or feelingIndicating 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 coordinator

• NO goes to sleep mode


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

Beacon enable modeRouting protocol : NOAH (No Ad-Hoc Routing Agent)The number of sensor nodes: 7The number of diseases: 5The number of symptoms: 7Simulation 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 consumption

CB’s energy consumption

rate is constant

IB’s energy consumption rate changes

according to the user’s health

state

Part1 Part2 Part3


Simulation Results(2/3)Energy consumption per sensors

•Sensors that belong to CB (S8 ~ S14) have constant energy consumption rate•Compact subset (S1, S2) acts like sensors that belong to CB


Simulation Results(2/3)Energy consumption per sensors

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


Simulation Results(3/3)Expiration time vs. Latency

Expiration Time (s)

Lat

ency

(s)


Concluding RemarksWe proposed an information based scheduling schema

Information gain model using mutual informationBy 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 gainDevelops distributed information based communication


Q & A


Simulation Scenario

Disease 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 efficiency

Use of a utility functionCombining the information gain and energy consumptionThe objective is to choose the sensors which reflect the larger dependency on the target disease and which have the lower operational cost (including energy consumption).


Details of the Solution(1/2)The operation cost function is defined as follows

: set of outgoing links at node n: duty cycle of sensor

: Power gain from transmitter of link k to the receiver of link lC : 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 costBased on the objective function, the criterion for selecting 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


IB Details of the Solution Algorithm (2/3)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


IB Details of the Solution Algorithm (3/3)Start

Initialization



Update knowledge


Yes

No

Sensor selection

The coordinatorselects the WBAN sensor which maximizes the information utility based on the knowledge state, p(D|{ei}i∈B)sends a request to the selected sensor to activateupdates the knowledge statee.g. p(D|{ei} i∈B ∪ ej)

Anomaly detection


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

(t) : Diagnosis state at time tEi

(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 measurements at time t

E(t) = {e1(t), e2

(t), … em(t)}


Information Utility MeasureMutual Information

Given two random variables x and y, their mutual information is defined in term of their probabilistic density functions p(x), p(y), and p(x, y)

The information contribution of sensor j with measurement 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 sensor 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 Avoidance

efficient energy scheduling of wban sensors for u-healthcaredpnm.postech.ac.kr › thesis › 11 ›...

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