1. CS 546: Intelligent Embedded Systems Gaurav S. Sukhatme Robotic Embedded Systems Lab Center for Robotics and Embedded Systems Computer Science Department University of Southern California gaurav@usc.edu http://robotics.usc.edu/~gaurav/CS546

2. Last time –HW3 –Time synchronization basics –Firefly paper –RBS paper Today –Aevena updates (Richard) –HW4 (Karthik) –Localization overview –Localization papers

3. Today Memory Processor InterfaceSensor and actuator suite Energy supply External communication Platform OS and SW architecture Tools User interface Application Figure adapted from [Pottie and Kaiser 2005] 1/24, 4/18: Cyclops 1/31: Networking 2/21: Energy management 4/18: Cyclops 2/7,14 and 28: Time synch, localization and data management 3/21,28 and 4/11: Environmental monitoring

4. What is localization ? The process of identifying the absolute or relative spatial location of nodes Why is it difficult ? Sensor measurements are noisy, delayed, intermittent, and often unavailable

5. Why Localize? To location-stamp sensor measurements To locate and track objects in the environment To monitor the spatial evolution of a diffuse phenomenon To determine the extent and quality of sensing coverage To load balance in topology control mechanims To form clusters for hierarchical processing To perform routing To facilitate efficient spatial querying

6. Key Issues: What to localize ? One model in networked localization is to assume that some nodes are already localized (anchors or references) How many anchors ? Can (should) the anchors move ? Are the non-anchored nodes cooperative ?

7. Key Issues: When to localize ? Often: all nodes need to be localized at the beginning of operation One shot vs. repeated

8. Key Issues: How well to localize ? Absolute vs. relative Symbolic (topological) Accuracy, reliability and computation efficiency tradeoff

9. Key Issues: Where to localize ? Central vs. distributed If distributed – in anchor nodes or in the non-anchor nodes Security considerations may play a role

10. Key Issues: How to localize ? Measurements –Relative: Signal strength, range (acoustic, RF, laser, IR etc.), angular dispersion of neighbors –Absolute: GPS, comparison to a global map Techniques –Proximity, centroids, constraints, ranging, angulation, pattern recognition, filtering, scaling, potential fields….

11. Two broad approaches in networked systems Coarse-grained –Small set of discrete measurements (e.g. binary proximity) Fine-grained –Large numbers of finer measurements (RF ranging, signal waveform, angulation, timing etc.) In addition if nodes move and rate information is available then filtering techniques from robotics can be used

12. Coarse-grained Node Localization: Binary Proximity Set of anchors placed in the environment Either anchors emit beacon signals or a node beacons when it needs to be localized Nodes can localize themselves coarsely to the beacon location (or resolve conflicts) Canonical example is the Active Badge system (indoor office environment) –Badges beacon periodically –Infrastructure responds with localization information –Room level localization Modern variants using RFID tags

13. Coarse-grained Node Localization: Centroids Higher density set of anchors If unknown node hears n beacons it localizes itself to their centroid Simple calculation, distributed across all nodes Extensions: weighted centroids, stochastic combinations

14. Coarse-grained Node Localization: Geometric constraints If geometric extent of (say) radio signal is r; R min < r < R max When multiple anchor beacons are heard at a node, it must solve an area intersection problem Area is determined by range boundaries and angular boundaries (if any) Size and shape of intersection regions provides an error bound on location accuracy

15. Coarse-grained Node Localization: Identifying codes Careful anchor placement to guarantee that each point is covered by a unique set of anchors General problem of a minimal ID code is NP-complete, but there are decent polynomial time heuristics

16. Fine-grained Node Localization: RSS Radio signal strength diminishes with distance Received power should be an indicator of distance from transmitter Path loss exponent – can be experimentally determined for some environments Noise – often high variance

17. Fine-grained Node Localization: TDoA Combine acoustic and RF techniques Simultaneously transmit radio and acoustic signals; measure respective arrival times T s and T r at receiver Distance is (T s – T r )/V s Issues –Works well in close proximity –Acoustic signals show multipath –Higher cost

18. Fine-grained Node Localization: Triangulation Location of unknown node determined by n measurements of distance to reference nodes Formulate as a least squares problem Correct distance between node o and anchor i Difference between measured and actual distance Determine (x o,y o ) that minimizes

19. Fine-grained Node Localization: Angle of Arrival Location of unknown node determined by n measurements of angle to reference nodes Can (again) formulate as a least squares problem

20. Fine-grained Node Localization: Pattern Matching Develop signal strength map of signal coverage from multiple transmitters and perform pattern matching on measurements at run time

21. Network-wide Localization Ideally, simply decompose this into individual node localization techniques In practice, this is often hard (why ?) –Either collect all data centrally –Or attempt a distributed technique

22. Constraint-based Express geometric constraints as inequalities (range, annulus, angular range) Select an objective function (minimize total error say) Solve using standard semi-definite programming techniques (some of which have distributed iterative formulations) One can also pose this problem stochastically and solve for the maximum likelihood location estimate

23. Mobile node-based constraints

24. Iterative Multilateration Assume inter-node distances are available between all neighboring nodes Pick node with most anchor neighbors and perform triangulation to locate it Add this node to the list of anchors, pick next node with largest number of anchor neighbors, and repeat In a distributed version, any node with a sufficient number of anchor neighbors can start the process

25. Iterative Multilateration: Issues Nodes w/o sufficient anchor density in neighborhood remain unlocalized –One solution is collaborative multilateration (Savvides paper) The order of the selected nodes affects accuracy

26. Collaborative Multilateration Determine collaborative subgraphs within the network that contain reference and unknown nodes such that there is a unique solution for the location of the unknown nodes Combine with iterative multilateration

27. Multi-hop Distance Estimation Used if anchors are several hops away DV-hop: distance from anchor is obtained by multiplying number of hops on shortest path by an (estimated) average distance per hop DV distance: If inter-node distances are known then use those instead of the above estimate Euclidean propagation on quadrilaterals

28. Force/Potential Approaches Use difference between actual and estimated position to generate a restoring ‘force’ that change the position estimate along the gradient of the force

29. Next week Energy management HW 3 is due a week from today (deadline extended) HW 4 is also due a week from today