Research at NESL and Ad-Hoc Localization

1. Research at NESL and Ad-Hoc Localization Andreas Savvides Networked and Embedded Systems Lab asavvide@ee.ucla.edu http://nesl.ee.ucla.edu

2. Introduction to NESL • Personnel • 1 faculty, 9 Ph.D. students, 5 M.S. students, 2 Undergrads • Activities in following fields in EE • Embedded Computing Systems (Graduate) • Communications (Graduate) • Computer Engineering Option (Undergraduate) • Active participation in two new wireless-related centers at UCLA • CENS: Center of Embedded Networked Systems (NSF S&TC) • CAINS: Center of Autonomous Intelligent Networks & Systems (ONR) • Strong collaborations • CS: LECS (Estrin), WAML (Gerla), Multimedia Lab (Muntz), and ER Lab (Sarrafzadeh), • EE: SPAPL (Alwan), ICL (Villasenor) • External: USC/ISI-East • Research and teaching sponsors • Projects from DARPA, NSF, ONR, SRC • Intel equipment grants

3. Research Activities • Energy-aware wireless communications & computation: design and tools[DARPA/PACC, ONR, SRC, NSF/CENS] • Energy-aware radio management and protocols • Dynamic power management in real-time OS and wireless SoCs • Novel wireless sensor and multimedia node architectures • Algorithms & protocols for wireless multimedia and ad hoc sensor/actuator nets [DARPA/SensIT, DARPA/PACC, DARPA/NEST, ONR, NSF/ITR, NSF/CENS] • Adaptive protocols for MAC, ad hoc routing, self-configuration, topology management, mobility management • Software services & infrastructure [ONR, DARPA/SensIT, NSF/ITR] • Agent-based programming of ad hoc wireless networks • Fine-grained localization, resource discovery, tracking, and timing synchronization • Middleware for sensor networks • Algorithms and protocols for ad hoc OFDM networks [ONR] • Efficient OFDM modeling • Algorithms for radio adaptation for energy & QoS • Channel and sub-carrier allocation • System applications of wireless technologies [NSF/ITR, NSF/CENS, ONR] • Technologies for sensor-enhanced deeply instrumented smart spaces, ecosystem monitoring, battlefield networks

4. Prototype Tools and Platforms SensorViz SensorWare Medusa MK-2 SensorSim PALOS iBadge

5. Smart Kindergarten Project Middleware Framework NetworkManagement SensorManagement SensorFusion Speech Recognizer Database & Data Miner Wired Network Collaboration: Muntz (CS) Alwan (EE) Potkonjak (CS) Baker (Education) WLAN Access Point High-speed Wireless LAN (WLAN) WLAN-Piconet Bridge WLAN-Piconet Bridge Piconet Piconet SensorsModules Sensor Badge Networked Toys

6. Ad-Hoc Node Localization Techniques Locate nodes deployed in a sensor field Rapid installation and self-calibration of indoor localization systems Indoor localization in the presence of Obstacles (e.g SmartKG) Ad-Hoc Node Localization • Many Applications • Pervasive computing, sensor networks, geographic aware protocols, location based services, etc

7. Localization Challenges • Algorithmic Challenges • Solve a large non-linear optimization problem using resource constrained microprocessors • Computation and communication challenges and energy tradeoffs (distributed, efficient and robust) • Need to operate in a multi-hop setup, deal with error propagation • Physical Effects on Ranging Measurements • Interference - transmission coordination • Multipath and shadowing effects • Other systematic error sources • Practical Challenges • Robustness, mobility support • Protocol architectures and integration, low power design • System integration challenge

8. Problem Setup 7 1 3 8 • Assumptions • Nodes within radio range can also measure their inter node distances, measurement error is white gaussian • Some nodes are initially aware of their locations • Nodes trust each other and can collaborate with each other • Nodes do not have any angular information 4 10 9 5 14 12 2 13 15 11 6

9. Algorithms Based On System Parameters Medusa MK-2 (A. Savvides) Ultrasonic RX/TX (Y.C. Kuan, A. Savvides) iBadge (I. Locher, S. Park)

10. Collaborative Multilateration • Considers constraints over the whole network • Ensure that a unique position solution for each node exists before trying to solve the problem. • Need a set of initial estimates to start the estimation process • Start the position refinement – iterative least squares • Two computation models: centralized and fully distributed • Localization process overview • Nodes organize themselves into groups • Some perimeter nodes become beacons • Nodes share information about locations and measurements • Information is combined to estimate locations (AB sin a, AB cos a) (AC,0) (0,0)

11. Computing Nodes Levels of Computation • Centralized • Only one node computes 2. Locally Centralized Some of unknown nodes compute 3. (Fully) Distributed Every unknown node computes • Each approach may be appropriate for a different application • Centralized approaches require routing and leader election • Fully distributed approach does not have this requirement

12. PHASE 1 PHASE 2 Find nodes with unique position solutions Compute Initial Position Estimates For all nodes PHASE 3 PHASE 3 Centralized Computation Distributed Computation Communicate results to central point Communicate Compute estimate at each node Compute location estimates Criteria met? Refine estimates of under-constrained nodes NO YES Transmit estimates back to each unknown node Done Done

13. Collaborative Subtrees (Phase 1) • Consider the single hop case: • 3 non-collinear beacons are required • Extend to multihop case: • Need at least 3 neighbors to act as anchors • Additional conditions need to be imposed • Consider the case where 3 beacons are at most 2 hops away • Derive a new set of constraints • Extend to multiple hops

14. Initial Estimates (Phase 2) • Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box • Use the distance to a beacon as bounds on the x and y coordinates U a a a x

15. Initial Estimates (Phase 2) • Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box • Use the distance to a beacon as bounds on the x and y coordinates • Do the same for beacons that are multiple hops away • Select the most constraining bounds Y b+c b+c c b U a X U is between [Y-(b+c)] and [X+a]

16. Initial Estimates (Phase 2) • Use the accurate distance measurements to impose constraints in the x and y coordinates – bounding box • Use the distance to a beacon as bounds on the x and y coordinates • Do the same for beacons that are multiple hops away • Select the most constraining bounds • Set the center of the bounding box as the initial estimate Y b+c b+c c b U a a a X

17. Initial Estimates (Phase 2) • Example: • 4 beacons • 16 unknowns • To get good initial estimates, some beacons should be placed on the perimeter of the network

18. Computing at a Central Point beacon 1 5 4 3 6 2 Unknown location The objective function is Can be solved using iterative least squares utilizing the initial Estimates from phase 2 - we use a Kalman Filter

19. Distributed Computation • Use an approximation method • Estimate node positions iteratively inside the network • Each node computes is location based on the currently estimated positions of its neighbors • If multilaterations follow a consistent pattern then a gradient with respect to the whole collaborative subtree is established (driven using Distributed Depth First Search) • Much less computation, similar result

20. Distributed Computation 2 1. Obtain initial estimates 2. for each unknown 2.1: Perform Atomic Multilateration if the neighbor is beacon use beacon location else use current position estimate 2.2: Broadcast new location estimate 3. Repeat step 2 every time a new position estimate is received until the convergence criteria are met 5 3 Uncertainty of estimate after first iteration 4 Iteration 1 Uncertainty of estimate after second iteration 1 Iteration 2 The unknown nodes need to perform their atomic multilateration in the same order, driven by a Distributed Depth First Search algorithm => local computations, follow a global gradient

21. Convergence Process • From SensorSim simulation • 40 nodes, 4 beacons • IEEE 802.11 MAC • 10Kbps radio • 40MHz processor • Average 6 neighbors per node

22. Gains in Computation Overhead • Computation cost based on MATLAB FLOPS outputs • Result difference between centralized and distributed is very small • Mean = 0.015 mm, Standard Deviation = 0.0054mm • A group of nodes can collectively solve a non-linear optimization problem than none of the nodes can solve individually. • Distributed computation cost between 3-4 MFLOPS per node

23. Localization Accuracy • Results obtained on a suite of 200 networks 10-200 nodes in each network • Average error over all networks was 27.7 millimeters, with a std 16mm

24. Communication Cost and Latency • Convergence time increases with group size • Similar trend in the communication cost • Communication cost evenly distributed across all nodes • Communication cost can be further reduced by reducing group size

25. Conclusions • Collaborative Multilateration • Reduces error propagation • It can go around obstacles – does not consider multipath effects though • Distributed version • Allows a group of nodes to solve a problem that they could not solve individually • Robust to node failures, even distribution of power consumption • Many interesting algorithms and applications • First we need to verify the test-bed • Aiming for deployment in the Smart Kindergarten

26. Thank you! More details on NESL research http://nesl.ee.ucla.edu or visit our lab @ 1762 Boelter Hall Simulation & Visualization Hardware Platforms Algorithms Power Awareness Networked Embedded Systems