Context Fusion: Dealing with Sensor Reliability Christos Anagnostopoulos Odysseas Sekkas Stathes Hadjiefthymiades Pervasive Computing Research Group, Department of Informatics and Telecommunications University of Athens, Greece SensorFusion07, , Pisa, Italy

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.

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

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.)

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))

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 …

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

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

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

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

Reliability of Sources Probability distribution for the sensor AP1 P(AP1=v 1 |L=L 1 )=0.5 L1L1 L2 L2 … v1v … v2v … v3v … ………… Reliability (h) for each sensor (A=Access Point (AP), B = IR Beacon) ID A1A2A3A4B1B2B3B4B5 h 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

Mean Confidence Probability

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