1. 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 Engineering POSTECH, Korea soodac@postech.ac.kr June 20, 2011

2. DPNM, POSTECH Master Thesis Defense 2/22 Outline  Introduction  Problem Statement  General Description of the Approach  Details of the Solution Mutual Information Criteria of Sensor Selection Implementation Issues  Simulation & Results  Concluding Remarks

3. DPNM, POSTECH Master Thesis Defense 3/22 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 Home Wireless Body Area Network

4. DPNM, POSTECH Master Thesis Defense 4/22 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 Coordinator SpO 2 Temperature Motion Sensor WBAN

5. DPNM, POSTECH Master Thesis Defense 5/22 Problem Statement(1/3)  Size Sensors needs to be as small as possible to be accepted by elderly Idea of nano sensors !  Energy Small size small battery Small battery small life time  Example 5mW 1.5V

6. DPNM, POSTECH Master Thesis Defense 6/22 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

7. DPNM, POSTECH Master Thesis Defense 7/22 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

8. DPNM, POSTECH Master Thesis Defense 8/22 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 investigation 3.Otherwise investigate more symptoms to detect a disease

9. DPNM, POSTECH Master Thesis Defense 9/22 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 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?

10. DPNM, POSTECH Master Thesis Defense 10/22 Mutual Information  Definition of mutual information  Measures the mutual dependence of the two variables  Example 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

11. DPNM, POSTECH Master Thesis Defense 11/22 Entropy Linked to Healthcare  Disease An abnormal condition affecting the body of an organism Construed to be a medical condition associated with specific symptoms and signs  Symptom A departure from normal function or feeling Indicating the presence of disease or abnormality Information Gain

12. DPNM, POSTECH Master Thesis Defense 12/22 Criteria of Sensor Selection(1/2) H(D|S)H(S|D) I(D; S) H(D) H(S) H(D, S)

13. DPNM, POSTECH Master Thesis Defense 13/22 Criteria of Sensor Selection(2/2) IG(e i | e j ) = H(D)H(S1)H(S3)H(S2)

14. DPNM, POSTECH Master Thesis Defense 14/22 Coordinator-Sensors Communication  Based on the IEEE 802.15.4 Beacon enabled mode Contention Access Period (CAP)  Coordinator Calculate information gain per every cycle ID  Pending Address Fields (PAF) Beacon frame broadcast  Medical sensors Does PAF contain medical sensor’s ID? YES  senses human body and then transmits sensed data to the coordinator NO  goes to sleep mode

15. DPNM, POSTECH Master Thesis Defense 15/22 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

16. DPNM, POSTECH Master Thesis Defense 16/22 Simulation Environment(2/2) 0123456789101112131415161718192021222324 (h) Part 1Part 2Part 3 Diseases occur Diseases do not happen D2 occurs from 3 AM to 4 AM  Simulation Scenario

17. DPNM, POSTECH Master Thesis Defense 17/22 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 Part1Part2Part3

18. DPNM, POSTECH Master Thesis Defense 18/22 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

19. DPNM, POSTECH Master Thesis Defense 19/22 Simulation Results(2/3)  Energy consumption per sensors Other sensors’ (S3 ~ S7) energy consumption rate changes according to the user’s health state

20. DPNM, POSTECH Master Thesis Defense 20/22 Simulation Results(3/3)  Expiration time vs. Latency Expiration Time (s) Latency(s)

21. DPNM, POSTECH Master Thesis Defense 21/22 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 works Finds compact subset of sensors by defining more feasible information gain Develops distributed information based communication

22. DPNM, POSTECH Master Thesis Defense 22/22 Q & A

23. DPNM, POSTECH Master Thesis Defense 23/22 Simulation Scenario DiseaseSymptom D2 D3 D4 D5 2 2 5 7 4 6 6 1 2 3 5 3  Relationship between disease and symptom 142 D1 p(D 1 | e 1 ) > p(D 1 | e 2 ) > p(D 1 | e 4 )

24. DPNM, POSTECH Master Thesis Defense 24/22 General Description of the Solution  Combining high information gain and energy efficiency Use of a utility function Combining the information gain and energy consumption The 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).

25. DPNM, POSTECH Master Thesis Defense 25/22 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 l C : total amount of initial energy

26. DPNM, POSTECH Master Thesis Defense 26/22 Details of the Solution(2/2)  The objective function is augmented with a weighted cost functions as follows : information utility of including the symptom e j : communication cost : relative weight between the information utility and communication cost  Based on the objective function, the criterion for selecting the sensors has the following form

27. DPNM, POSTECH Master Thesis Defense 27/22 Communication Processes

28. DPNM, POSTECH Master Thesis Defense 28/22 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 sensors Initial energy level Start Initialization Send information query Wait for information Update knowledge Knowledge is good enough? Yes No Sensor selection Anomaly detection

29. DPNM, POSTECH Master Thesis Defense 29/22 IB Details of the Solution Algorithm (2/3) Start Initialization Send information query Wait for information Update knowledge Knowledge is good enough? 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|{e i } i ∈ B ) Anomaly detection

30. DPNM, POSTECH Master Thesis Defense 30/22 IB Details of the Solution Algorithm (3/3) Start Initialization Send information query Wait for information Update knowledge Knowledge is good enough? Yes No Sensor selection  The coordinator selects the WBAN sensor which maximizes the information utility based on the knowledge state, p(D|{e i } i ∈ B ) sends a request to the selected sensor to activate updates the knowledge state e.g. p(D|{e i } i ∈ B ∪ e j ) Anomaly detection

31. DPNM, POSTECH Master Thesis Defense 31/22 Notation  Superscript t : time  Subscript i ∈ {1,..., K} : sensor index  Subscript j ∈ {1,..., N} : diagnosis index  D j (t) : Diagnosis state at time t  E i (t) : Measurement of sensor i at time t  E (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) = {e 1 (t), e 2 (t), … e m (t) }

32. DPNM, POSTECH Master Thesis Defense 32/22 Information Utility Measure  Mutual 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 e j (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

33. DPNM, POSTECH Master Thesis Defense 33/22 Demo Room Light Path for Obstacles avoidance ECG Sensor Wireless Sensor Base  Context – U-Health Medical Smart Home @ Postech Video Control System Remote Air Conditioning Control Environment Sensors (Light, Temperature, Humidity) Remote Window Opening /Closing Control Light Path for Obstacles Avoidance