1. Recalibration Problem • Virtual Sensors (VS’s) Detect Events • E.g., Coffee Level Rescue Kitchen, West-East Traffic light Next to Calit(2). Coffee State: 11:43 Half-Full 11:44 Half-Full 11:45 Half-Full 11:46 Half-Full . . VS-Output Traffic Light State: 11:43 Red 11:44 Red 11:45 Red 11:46 Red . . VS-Output

2. Recalibration Problem (Cont.) • Perturbations in Physical Sensors Can Cause Virtual Sensors to Become Un-calibrated • E.g, camera can move its FOV. Coffee State: 11:43 Empty 11:44 Empty 11:45 Empty 11:46 Empty . . VS-Output Traffic Light State: 11:43 Orange 11:44 Orange 11:45 Orange 11:46 Orange . . VS-Output

3. Recalibration Problem (Cont.) • Recalibration refers to detection of such perturbations & adjustment of parameters used by VS’s to detect correctly.

4. The Recalibration Problem - Approach • Observation: • Events = change in value of attributes. • Approach: • Model the state changes as an automata. • Use the automata to capture semantics.

5. Exploiting Semantics for Re-Calibration • Detection: • Deviation from the Learnt Model • Correction & Calibration: • A Search Over Parameter Space to Find Semantic Model • DEMO: Finding the Correct FOV Based on System Semantics: Coffee Machine & Traffic Light

6. Exploiting Semantics for Scheduling of Resources • Let: • C be a Set of n Cameras. • Plan(t)be a {0,1} vector <b1,..,bn> (Indicating Which Cameras to Probe) • Benefit(Plan(t)) be the expected benefit from executing this plan. • Cost(Plan(t)) be the cost associated with that plan. • Find a plan, such that: • Benefit(Plan(t)) is maximized. • Cost(Plan(t)) is minimized. • Under the constraint: Σbi ≤ k • For example, probe a set of N entry/exit sensors: • To find as manydistinct people entering. • When establishing a new connection to a sensor is 5 time more expensivethan continuing to probe the same sensor. • Only k connections can be established at the same time.

7. Semantics Exploited • Apriority: • What are the cameras that I should probe, when nothing else is known. • Self Correlation: • If we’ve seen someone walking in camera A, how likely is it to see more motion in A. • Cross Correlation: • If this is the state of the system we are aware of: • We’ve seen motion in camera B 5 seconds ago. • And motion in camera C 2 seconds ago. • How likely is it to see motion in camera A.

8. Motion Semantics • Semantics Used: • Motion • Other Types of Semantics • Trajectory • Identity

9. Scheduling Using Semantics • Naïve: RR • Exploiting Sensor Semantics: • Determine Probability of Motion • Use the Motion Estimates to Find the Best Plan

10. Results Almost 90% improvement compared to RR!!

11. Different Benefit Functions • The semantics based algorithm proves to perform the best. • ..But it can do better if we change the plan based on the cost/benefit functions.