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The Cricket Location-Support System

This paper outlines a decentralized location support system designed by Min Chen to prioritize user privacy and reduce costs. Utilizing a combination of RF and ultrasound technologies, the system enables users to infer their current location through a network of beacons that disseminate spatial information. Key challenges addressed involve collision issues from multiple RF transmissions and proper beacon signal correlation. The paper explores various listener inference algorithms to optimize location accuracy and discusses experimental implementations, including performance tests in both static and mobile scenarios.

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The Cricket Location-Support System

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  1. The Cricket Location-Support System By: Min Chen 10/28/03

  2. Goal • User Privacy • Decentralized administration • Network heterogeneity • Low cost • Room-sized granularity

  3. System Architecture • Beacon: Disseminate the string of space information about a geographic space to listeners. • Listener: Infer its current location from the set of beacons . Approach Use combination of RF and ultrasound to provide a location-support service to users.

  4. Related Problem and Method • Collision of RF transmissions from different beacons ------------Randomization • Wrong correlation of the RF data of one beacon with the ultrasonic signal of another ------ System parameters ------ Listener Inference Algorithms

  5. use a relatively sluggish RF data transmission rate. System Parameters Selection

  6. Interference Scenarios • RF-A:US-RA • Align the beacons • RF-A:US-I • Using RF signal with long range • RF-A:US-RI • Ensure less than 5 beacons within range of each other

  7. Listener Inference Algorithm • Majority • Picks the beacon with highest frequency of occurrence • MinMean • Selects the beacon with the minimum mean value • MinMode • Compute the per-beacon statistical mode over the past n samples.

  8. Implementation :Beacon Positioning and Configuration

  9. Implementation :Ultrasound Deployment

  10. Experiment: Boundary Performance

  11. Experiment: Static Performance

  12. Experiment: Static Performance (Cont)

  13. Experiment: Mobile Performance

  14. Comments Concerns when apply to sensor network 2D Constrain Power Consideration Calibration On the Transmitter. Kalman Filter for the Mobile Algorithm

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