Fine-Grained Ad-Hoc Localization in Wireless Sensor Networks

1. Fine-Grained Ad-Hoc Localization in Wireless Sensor Networks Andreas Savvides Center for Embedded Networked Sensing (CENS) & Networked and Embedded Systems Lab (NESL) http://nesl.ee.ucla.edu/projects/ahlos http://nesl.ee.ucla.edu/projects/smartkg

2. Location Awareness in Sensor Networks Ad-Hoc Localization Random Deployment Operate in the presence of obstacles Rapid Infrastructure Setup • Multihop networks • may span of over large • geographical regions • Not always easy to provision • for the proper placement of • landmarks • Form a multihop network • to avoid obstacles • Landmarks may not always • be within range of all nodes • Reduce the cost • and time overhead of • installing new systems

3. Localization in Smart Kindergarten • Derive locations of students and objects • Track head motion patterns • Use ultrasonic Time-of-Flight • Requirements • Unobtrusive operation • Low power consumption • High degree of accuracy • Ease of deployment • Smart beacon calibration • Communicate the locations back to the infrastructure

4. Platforms: Medusa MK-2 • Medusa MK-2 Node • For localization experiments • 40MHz ARM THUMB • 1MB FLASH, 136KB RAM • 0.9MIPS/MHz • 480MIPS/mW (ATMega 242MIPS/mW) • can run eCos, uCLinux • RS-485 bus • Out of band data collection • Formation of arrays • 3 current monitors (Radio, Thumb, rest of the system) • 540mAh Rechargeable Li-Ion battery

5. RF TX Start 4ms Start Symbol Detected (start timer) RF Signal 4ms RF Reception Complete 15ms Ultrasound Signal (for max range) Ultrasonic Ranging Latency USND TX Start Ultrasound Detected Transmitter Receiver

6. Ranging Characterization • Lab characterization of ranging module, at 25 pulses (temperature 21.4 C)

7. Localization Algorithms • Platforms are computationally constrained • Incomplete beacon node information • Nodes need to collaborate to jointly estimate their locations -> collaborative multilateration • Need to avoid error propagation • Distributed operation to avoid node failures • Lightweight processing and efficient communication to preserve power

8. Collaborative Multilateration • Utilize measurement information over multiple hops • Solve the problem in a fully distributed manner beacon nodes

9. Centralized Collaborative Multilateration 1 5 4 3 6 2 The objective function is Can be solved using iterative least squares utilizing the initial Estimates from phase 2 - solve with an Extended Kalman Filter

10. Distributed Collaborative Multilateration • Instead, we propose a simple approximation • Each node performs a multilateration using only next-hop neighbor information in the context of a collaborative subtree • If multilaterations follow a consistent pattern then a global gradient with respect to the whole collaborative subtree is established (driven using Distributed Depth First Search) • Much less computation, similar result, fully distributed operation with desirable side effects

11. Distributed Collaborative Multilateration 2 5 3 4 1 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

12. Distributed Collaborative Multilateration 2 5 3 Error is reduced at each iteration, because we are operating in an over-constrained setup 4 1 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

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

14. 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

15. 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

16. Error Behavior of Multihop Localization • Many sources of error • Channel error, algorithmic and computation error and setup error • Setup error is associated with design-time and deployment time parameters • Deployment geometry • Network density • Beacon concentration • Measurement technology accuracy • Certainty in beacon locations • Cramer-Bound Analysis to show how the setup error behaves as the network scales

17. Node and Beacon Density Effects RMS Error(m) RMS Error(m) Node density (nodes/m2) Number of beacons 100 Node network, 4 – 20 beacons 200 Node network, 10% beacons

18. Smart Beacon Calibration

19. Host PC Controller Software 3D GUI Client(s) • Manager • Packet based • One to many switching • Facilitate online • processing of incoming • data • Allows direct use of • MATLAB code TCP Server Other SW Localizer Calibration SW Gateway Node Serial I/O

20. Conclusions • Collaborative Multilateration is a feasible solution • Works with binary obstacles • Distributed localization is feasible in some scenarios • An initial testbed is there, working on completion. • Geometry is a problem • Ready to generate larger traces of measurement data for further study • More experiments using the testbed • Moving from advance hardware testing to a more complete system that provides localization and tracking services