Sensing Uncertainty and the Role of Constrained Actuation

1. Sensing Uncertainty and the Role of Constrained Actuation Aman Kansal

2. Overview Sensor network performance: quality of information returned by it

3. Contributions • Develop models for realistic sensing • Going beyond the circular disc model • Develop platforms for evaluating sensing coverage with real sensors and real world sensing media • Use actuation based system reconfiguration for estimating/improving sensor network coverage and uncertainty • plan additional resources • provide decision confidence

4. Fidelity of data depends on multiple factors Modeling the Sensing Process Anisotropic Environmental Attenuation Phenomenon of Interest Noise in Transducer Electronics Compression Loss

5. Measuring Sensing Uncertainty • Model reality closely • Existing work assumes artificial sensing models • Circular range model • Consider resolution of coverage • Assess coverage due to multiple sensors • Existing work considers degree of coverage: may not model application requirement Coverage degree 2 Coverage degree 1 Sensing radius

6. Measuring Sensing Uncertainty Sensors 1 • Sensing Uncertainty: distortion in reconstructed phenomenon • Raw sensor reading not of interest • With multiple sensors: distortion in joint reconstruction Joint Reconstruction X i … Phenomenon Fusion Center L Chen et al, IEEE JSAC’04

7. Measuring Sensing Uncertainty • Model the sensor field as a stochastic process with autocorrelation function R(x1,y1,x2,y2) = Rx • Model sensing noise as another stochastic process with autocorrelation function RN. • Sensing medium anisotropies and attenuation affect sensing SNR

8. Propagation Matrix N1 X1 h1j Y1 + N2 Estimation [X1,…,XL] X2 h2j Y2 + … N1 XL hLj Y1 + Sensor Readings Phenomenon • Denote H to be the propagation matrix

9. Information Theoretic Bound Rate High Noise Low Noise Distortion • Expression derived for actual H and RN Optimal Rate-Distortion relationship

10. Reduce Sensing Uncertainty Transducer noise, sN • Higher Costs: cannot deploy densely • Cannot handle occlusions • Precision requires higher energy, bandwidth Higher precision transducers CONFLICTING • May need very high density to guarantee coverage in arbitrary environment • Finite communication bandwidth shared by more sensors: per sensor share falls • Intrusive: interferes with phenomenon Higher density deployment Medium Anisotropies, H

11. Use Actuation • Need better quality information instead of more bits • Actuation can achieve: • Higher fidelity without high density • Move towards phenomenon to enhance sensing SNR • Adapt to specific deployment scenario • Adaptation to run time dynamics • Growth of foliage, movement of phenomenon, presence of mobile occlusions (animals)

12. Use Actuation • Challenges: • Accurate localization and navigation is resource and power intensive • Uncertainty due to changing sensor position • Energy overhead • Solution: Low Complexity Actuation • Small motion on assisted tracks • Pan, tilt, zoom capabilities • Virtual Mobility: changing active and inactive nodes Sensor Node Track Traction Platform

13. Intuition: Small Actuation Helps Sensor Covered Area Uncovered area Uncovered Area • Coverage area increases • Multiple perspectives feasible • Adapt to medium and phenomenon changes

14. Intuition: Small Actuation Helps 3000 Reduction in occluded Area, % 2500 2000 1500 l l 1000 x 500 20 35 30 25 2l Distance to obstacle, x

15. Simulating Multiple Obstacles • Assume multiple small aspect ratio obstacles • Single camera moves a small multiple of mean obstacle diameter • Obstacles distributed uniformly randomly Obstacles Sensor

16. Simulation Results Changing Obstacle Size Changing Obstacle Density Percentage Gain due to mobility Mobile (2l) 1 Mobile (l) 500 Coverage Fraction 300 100 static 5 10 15 2 20 laverage 1 Obstacle Density Dmove/laverage Results averaged over 20 random topologies

17. Laboratory Experiments with Image Sensors • Constructed a system of four cameras and a square field with obstacles • Image processing used on noisy camera output to detect target • Constant lighting conditions • Measured detection probability by moving a target around the field

18. Laboratory Set-up • Obstacle placement models tree locations in an example forest (WindRiver Canopy Research Facility) Camera Movement

19. Experiment Results 100 Static Probability of Mis-detection 10-1 Target Mobile 3 4 1 2 Number of Cameras

20. Real World Experiments • In woods near UCLA (near Sunset Rec.) • Arbitrary obstacle shape and size • Lighting conditions no longer constant • Sensor noise increased • Sensor not designed for outdoor usage and imaging in sunlit conditions • Detection measured in 12’x12’ region, Motion range = 2’ from mean position

21. Real World Experiments Constrained motion (small multiple of mean obstacle size) helps reduce sensing uncertainty

22. Pan-Tilt-Zoom Volume Coverage • Evaluating coverage gain in volume for a commercial sensor (Sony SNCrz30N)

23. Virtual Mobility • Node ID insignificant: deactivating one node and activating another is same as relocation of a sensor • Higher node deployment density required to enable migration to sufficient locations for coverage • Motion delay can be made very small • Multiple simultaneous nodes can be activated for special events

24. Managing Actuation • GOALS: • Generate optimal actuation commands and sensor placements to minimize sensing uncertainty • Coordinate the actuation of multiple sensors simultaneously measuring distributed phenomenon to maximize global coverage metrics • Joint optimizations with • Energy usage • Navigation constraints • Communication requirements • Resource scheduling in space and time

25. Two Phase Solution • Learn H in deployment scenario • Actuation can be used to acquire the propagation matrix coefficients at high resolution • Use actuation to optimally place and move sensors • Achieve favorable H, RN • System evolves with phenomenon and environment dynamics

26. Phase 1: Self-Aware Actuation • Learn and improve system coverage and uncertainty • Map environmental obstructions • Estimate sensor noise SENSOR

27. Self-awareness Sensors • Acoustic range sensor to acquire propagation matrix • Alternatives: • Stereo-vision: needs two cameras and heavier processing • Laser Ranging: more accurate but • Very expensive hardware • Higher energy requirements • Large size (more processing electronics) • IR Ranging: • useful for shorter range Beam Pattern of SensComp Acoustic Transducer

28. Feature Extraction Algorithms • Pan the range sensor to measure distances • Build environment model • Estimate positions of environmental features • Move to take further measurements and refine map • Algorithms: • VFH: Vector Field Histogram • SLAM: Simultaneous Localization and Mapping

29. Discritized Medium Model • 3D space divided into voxels • Learn whether a voxel is occupied or not • A slice of the 3D space • White: voxels revealed to be empty by range sensor ray tracing • Green: Unexplored/occupied

30. Phase 2: Coordinated Actuation • Multiple sensors tracking multiple phenomenon • Questions • How should coverage be maximized • How should the sensor move to improve information after it detects a phenomenon • How should other sensors locate themselves to gather additional non-redundant information • How should multiple sensors be shared among multiple phenomenon

31. Analyzing the Information Gain • Information gained from a new observation be z • Bayesian approach to update belief about measured phenomenon, x: • Methods to execute this for multi-variate probabilities and multiple simultaneous observations exist: Bayesian networks • Exploit problem structure and variable dependencies to simplify computation

32. Move to Maximize Information Gain • Expected information gain can be measured as mutual information: • Utility of new observation can thus be measured as: -E{log[P(x|z)]} • For multiple sensors: -E{log[P(x|z1,z2…,zL)]}

33. Motion Control • With above metrics, motion trajectories known for sensor teams in • Occlusion free scenario • Gaussian phenomenon • Unconstrained motion • Need methods to measure information gain in the presence of sensing occlusions using the acquired propagation matrix • Need optimal actuation along constrained paths phenomenon y Sensor trajectory x Grochoslky, 2002

34. Learning Based Approach • Central Dispatcher determines which sensors move • Based on estimated quality of each sensor’s data • Sensors locally determine pose • Obtain central estimate of target trajectory • Orient/Move towards estimated target location • Action reinforced based on achieved target visibility [Ref: U.W Ontario]

35. Distributed Actuation: Example • Simple pan actuation to optimize instantaneous coverage Improved Orientations Random Orientations

36. Distributed Actuation Strategies • Define: Neighbors = {any node within 2*Rs} • Wish to coordinate pan orientations to maximize network coverage Algorithm 1: Obstacle information not available, location available • Each sensor transmits {identity,location} to neighbors • Each sensor sorts received identities in ascending order and waits for message from those with smaller identity than itself • Identity order ensures no two sensors choose orientations simultaneously and hence cover overlapping regions • When all messages received (or this sensor has lowest identity within its neighborhood) • Choose a pan orientation which has minimum overlap with sensors whose pan orientation received • Transmit chosen pan orientation to neighbors

37. Distributed Actuation Strategies Algorithm 2: Environment obstacle sensing capability available (location not used) • Each sensor chooses pan orientation to maximize its line of sight coverage • Overlap with neighbors may causes sub-optimal behaviour

38. Distributed Actuation Strategies Algorithm 3: Utilize environment knowledge and sensor coordination • Follow algorithm 1 except that when choosing orientation: Choose a pan angle where the covered area is maximum after accounting for neighbor overlap and environmental occlusion • Geometric calculations based on obstacle locations and neighbor orientations allow the above decision • Expected to perform better as using more information than previous two algorithms

39. Comparison of Actuation Strategies Coverage Fraction Node Density

40. More on Sensing Uncertainty and Actuation • Outlier Verification:Suppose sensor reading differs significantly from neighboring sensors • Is it due to unexpected phenomenon or sensor error? • Mobile node can be moved to location of exception to compare values for critical decisions

41. More on Sensing Uncertainty and Actuation • In-situ Calibration: Need calibration after deployment • Re-calibrate as part of complete device • Re-calibrate to overcome drift • Hard to provide known stimulus in-situ • Known calibrated mobile sensor can be used as ground truth to calibrate

42. More on Sensing Uncertainty and Actuation • Security Issues: Mobile Sensor Can Carry Trust • Malicious behavior: Sensor not faulty but node is compromised and reports malicious data • Mobile sensor can be used for security patrols to periodically weed out such nodes

43. Conclusions • Actuation can reduce sensing uncertainty where high density or higher precisions sensors alone fail • Actuation can be used in a self-aware setting to reconfigure and adapt the system to run time dynamics • Coordinated actuation can help achieve best sensing performance by efficiently utilizing system resources