1. Context Fusion: Dealing with Sensor Reliability Christos Anagnostopoulos Odysseas Sekkas Stathes Hadjiefthymiades Pervasive Computing Research Group, http://p-comp.di.uoa.gr Department of Informatics and Telecommunications University of Athens, Greece SensorFusion07, 08.10.2007, Pisa, Italy

2. Context Fusion Context Estimation is characterized by imprecise knowledge (e.g. missing information and unreliability of sources) Context Fusion is the method of deriving high-level context from low-level, inaccurate sensor data. Context Fusion Engine – Dynamic Bayesian Networks (DBN) and Fuzzy Logic incorporates the reliability of sources more accurate inference on the current user situation i.e., a set of aggregated pieces of context.

3. Reliability of Sources [(a 1 is u 1 ) and conf 1 ]  …  [(a n is u n ) and conf n ]  (p is u) The context-determination rule that concludes a situation p w.r.t, reliability of the sources: a i = contextual ingredient (attribute) u i = value conf i = confidence of sensor readings on measuring u i confidence value

4. Probabilistic Fusion Random variables of the DBN are (i)attributes a (i.e., sensor readings) (ii)situation p (i.e. location of the user, actions, etc.)

5. Probabilistic Fusion The calculation of conditional probabilities determines the value of the situation at time t i.e., p = p(t) Fusion: find the situation p(t) that maximizes P(p(t))

6. Fuzzy Probabilistic Fusion p conf p, v … Probabilistic Fusion results Determination Rule situation Probabilistic Fusion v*v* Fuzzy Probabilistic Fusion result Fuzzy Inference Confidence conf 1, v * 1 conf 2, v * 2 conf N, v * N a1a1 a2a2 aNaN …

7. Fuzzy Sets for Confidence Fuzzy Values for P(p(t)), conf p and confidence probability P*(p(t)) denoted as Linguistic Terms.

8. Fuzzy Inference Rules if P(p(t)) is low then P*(p(t)) is low if P(p(t)) is medium and conf p is low then P * (p(t)) is very low if P(p(t)) is medium and conf p is high then P * (p(t)) is somewhat high if P(p(t)) is high and conf p is low then P * (p(t)) is medium if P(p(t)) is high and conf p is high then P * (p(t)) is high

9. System Evaluation We assume that situation p(t) is the location L of the user at time t, L = {meeting room, entrance,…}

10. System Evaluation Test-bed involving two technologies – WLAN Access Points (4) – Infrared Beacons (5) Test-bed area: UoA, Dept. of Informatics and Telecommunications

11. Reliability of Sources Probability distribution for the sensor AP1 P(AP1=v 1 |L=L 1 )=0.5 L1L1 L2 L2 … v1v1 0.50.0… v2v2 0.30.8… v3v3 0.10.2… ………… Reliability (h) for each sensor (A=Access Point (AP), B = IR Beacon) ID A1A2A3A4B1B2B3B4B5 h0.660.790.770.80.820.890.930.951 IR-Beacons appear more reliable on location estimation than WLAN APs IR-Beacons have shorter range of emission thus improving the accuracy of the estimated location

12. Mean Confidence Probability

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