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Location Estimation in ZigBee Network Based on Fingerprinting. Authors : Qingming Yao, Fei-Yue Wang, Hui Gao, Kunfeng Wang and Hongxia Zhao Publisher : Vehicular Electronics and Safety, 2007. ICVES Present : Yu-Tso Chen Date : November, 5, 2008.

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Location Estimation in ZigBee Network Based on Fingerprinting


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    1. Location Estimation in ZigBee Network Based on Fingerprinting Authors: Qingming Yao, Fei-Yue Wang, Hui Gao, Kunfeng Wang and Hongxia Zhao Publisher:Vehicular Electronics and Safety, 2007. ICVES Present:Yu-Tso Chen Date:November, 5, 2008 Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

    2. Outline • 1. Introduction • 2. Research Methodology • 3. Implement and Results • 4. Conclusions and Future Work

    3. Introduction • Transform ordinary environments into intelligent spaces • Context refers to the physical (position, time, weather) and social situation (work or leisure place) • As location becomes one of the most import contexts, location-aware computing is a recent interesting research area

    4. WSN Location Estimation System • Authors implement the location estimation system adopting ZigBee based network • Advantage :short range, low data rate, low power consumption and low cost network technology • easily constructing ad-hoc, mesh networks

    5. ZigBee Network • RSSI (Received Signal Strength Indication) is the basic function we use to form fingerprinting and measure data only the Local Location cluster is used

    6. System Methodology (1/2) • Beacons are fixed in several points evenly to make sure that mobile station (MS) can receive n (3 to 5 as usual) points’ radio signal at each location • MS records and processes the RSS vector and then searches the fingerprinting database to find some fingerprinting which makes the algorithm criterion maximum

    7. System Methodology (2/2) • Oxy =(o1xy, o2xy, ..., onxy )T is the observed RSS vector from beacons at location Lxy • RSSI = -( 10n d + A) 10 log • Fij =(f1ij , f2ij , ..., fnij )T is average RSS of location Fingerprinting database is constructed by a process of offline training

    8. Build the Histogram • Beacon k at location Lij is L =[lk0ij , ..., lkM−1ij ] • L enables computing the histogram hkij of signal strengths for each beacon indexed k

    9. Location Estimation Algorithm • Practical environment, the radio channel is of noisy characteristics • The observation Oxy deviates significantly from Fij • Map the online observed data Oxy to some physical Lxy • We applied a probabilistic approach using Bayesian inference

    10. Estimation algorithm’s target • The estimation algorithm’s target is to find a location Lij that makes the probability P(Lij |Oxy) maximized

    11. Estimation algorithm’s target (cont.) • Conditional probability P(Oxy|Lij) is the likelihood of Oxy occurring in thetraining phase of Lij • P(Lij) is the prior probability of location Lij being the correct position (uniformly distributed) • CSMA/CA mechanism ensures the signal from different beacons independent from each other • joint probability distribution => marginal probability distributions ,where

    12. IMPLEMENT AND RESULTS • ZigBee module • TI’s single-chip 2.4 GHz IEEE 802.14.5 compliant RF transceiver CC2420 Fxed on ceiling usually (Beacon)

    13. Layout of Experimentation • Office room dimensions of 7.2m×9m×2.6m • All the beacons are fixed on the ceiling • Calibration points where the mobile stations’ signal strength was collected are denoted by the gray square

    14. Signal Statistical Character • Multi-path fading and people’s activities lead the RSSI fluctuating

    15. Two error distances to evaluate the accuracy • Physical space’s Euclidian distance: • Singal space’s Euclidian distance between Oxy and Lxy:

    16. Short-term Measurement • Experimentation was designed as collecting RSSI of beacon3 at location L1 and L2

    17. Long-term Measurement

    18. Two Clusters with Frequency of RSSI with Two Beacons • Investigate how the pattern of fingerprinting at different locations effects the location separation frequency of occurrence of each sample pattern

    19. Mean RSSI in Calibration Points • Fluctuate frequently

    20. Conclusion • System can triangulate the location within 70% accuracy with the tolerance of 0.5 meters that is quite encouraging.

    21. Thanks

    22. Existed Location Systems • AT&T’s Active Badge • diffuse infrared technology • Disadvantage - difficulty with fluorescent lighting or direct sunlight • Active Bats • Ultrasound time-of-flight lateration technique • Disadvantage - requires large scale deployment and high cost

    23. Existed Location Systems (cont.) • Cricket • MIT complemented the Active Bats by using a radio frequency control signal • Disadvantage - centralized management, and mobile receivers have heavy computational and power burden • All the three systems is that they only provide light-of-sight (LOS) location estimation

    24. Offline Training Phase • Offline Training Phase • The location fingerprinting is collected at each point of the 30 calibration points • The probabilistic distributions of four directions are obtained by (1)

    25. Online Estimation Phase • Online Estimation Phase • measure and average the RSSI from beacons • The average RSSI forms the observation tuple • Oxy = (o1xy,o2xy, ..., onxy )T and be applied in (4) (5) (6) to triangulate location Lij • Search the fingerprinting database which stores the prior probability hkij (ζ) to find the (i, j) which makes P(Lij |Oxy) maximized

    26. Conclusion • System can triangulate the location within 70% accuracy with the tolerance of 0.5 meters that is quite encouraging.