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October 28-31th 2013

IPIN 2013

Single-Channel Versus Multi-Channel Scanning inDevice-Free Indoor Radio

Localization

Montbéliard, Belford, France

P. Cassarà; F. Potortì; P. Barsocchi; P. Nepa

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Outline

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Scenario

The Inverse Problem

Data Acquisition

Numerical Results

Conclusions

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Scenario

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Data Record

Scenario for device-free indoor localization

IRIS Mote

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The Inverse Problem

Variance-Based RTI Model

This kind of algorithm maps the shadowing measured over the links in to the shadowing over the set of pixel.

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Data Acquisition

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Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_nTime Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; RSS_Link_1…RSS_Link_l

Time Stamp; RSS_Link_1…RSS_Link_l

Time Stamp; RSS_Link_1…RSS_Link_l

sliding window for the variance evaluation

The data are acquired as the values of RSS received by the ID_RX from all the other nodes into the sensor network for a given channel…

Data formatting

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Numerical ResultsThe raw comparison between multi-channel and single-channel

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Lengths of the sliding window 3sec and 5 sec

Multi-channel over two and four channels

The figures show the comparison of the algorithm’s performance for measurements achieved in single-channel and multi-channel mode

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Numerical Results

The comparison between multi-channel and single-channel on equal terms

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Lengths of the sliding window 3sec and 5 sec

Multi-channel over two channels

The figures show the comparison of the algorithm’s performance for measurements achieved in single-channel and multi-channel mode but the single-channel is filtered from multi-channel measurements

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Numerical Results

Again comparison between multi-channel and single-channel on equal terms

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Lengths of the sliding window 10 sec

Multi-channel over four channels

The figures show the comparison of the algorithm’s performance for measurements achieved in single-channel and multi-channel mode but the single-channel is filtered from multi-channel measurements

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Numerical Results

The influence of the channel information...

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Lengths of the sliding window 3 and 5 sec

Multi-channel over two and four channels

In this case the data are collected over many channels, but the algorithm works in either single-channel (discarding the channel info) or multi-channel mode

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_nTime Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1

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Numerical Results

Again about the influence of the channel information...

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Lengths of the sliding window 3 and 5 sec

Fake multi-channel over four channels

In this case the data are collected over the single-channel, but the algorithm works in either single-channel or a fake multi-channel

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_nTime Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_Node_1…Ch_Node_n

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1

Time Stamp; ID_Rx; RSS_1…RSS_n; Ch_1…Ch_1

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Conclusions

Develop a multi-channel localization algorithm may be more complex because

to get enough sample you need to increase the acquisition rate of the nodes

the sensor network needs to handle a data traffic that is the data traffic for the single-channel acquisition by the number of channels

the algorithm needs to elaborate a set of data that is the set of data for the single-channel by the number of channels

The measurements shows that apparently the multi-channel doesn't give remarkable advantages on the VRTI-based localization algorithm, to detriment of the system complexity

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Thanks !

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