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Location Estimation in ZigBee Network Based on Fingerprinting

Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

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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Outline

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

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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

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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

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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

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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

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System Methodology (2/2)

Oxy =(o1xy, o2

xy, ..., onxy )T is

the observed RSS vector from beacons at location Lxy

• RSSI = -( 10n d + A) 10 log

Fij =(f1ij , f2

ij , ..., fnij )T is

average RSS of location Fingerprinting database is constructed by a process of offline training

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Build the Histogram

Beacon k at location Lij is L =[lk0ij , ..., lkM−1

ij ]

L enables computing the histogram hkij of

signal strengths for each beacon indexed k

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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

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Estimation algorithm’s target

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

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Estimation algorithm’s target (cont.)

Conditional probability P(Oxy|Lij) is the likelihood of Oxy occurring in the training 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

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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)

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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

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Signal Statistical Character

Multi-path fading and people’s activities lead the RSSI fluctuating

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Two error distances to evaluate the accuracy

Physical space’s Euclidian distance：

Singal space’s Euclidian distance between Oxy and Lxy：

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Short-term Measurement

Experimentation was designed as collecting RSSI of beacon3 at location L1 and L2

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Long-term Measurement

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Two Clusters with Frequency of RSSI with Two Beacons

Investigate how the pattern of fingerprinting at different locations effects the location separation

frequency of occurrenceof each sample pattern

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Mean RSSI in Calibration Points

Fluctuate frequently

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Conclusion

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

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Thanks

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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

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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

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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)

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Online Estimation Phase

Online Estimation Phase• measure and average the RSSI from beacons

• The average RSSI forms the observation tuple

• Oxy = (o1xy,o2

xy, ..., onxy )T and be applied in (4) (5)

(6) to triangulate location Lij

• Search the fingerprinting database which stores the prior probability hk

ij (ζ) to find the (i, j) which makes P(Lij |Oxy) maximized

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Conclusion

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