... | ... | @@ -18,6 +18,7 @@ Thanks to the great support of VBZ ([Verkehrsbetriebe Zürich](http://www.vbz.ch |
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![](img/misc/box_on_tram_closed.jpg)
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![](img/misc/tram_front.jpg)
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![](img/misc/box_on_tram.jpg)
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*Installation of an OpenSense measurement station on top of a VBZ cobra tram.*
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The second station is statically positioned next to the [NABEL](http://www.empa.ch/plugin/template/empa/699/*/---/l=2) station in Dübendrof and used as a long-term deployment in cooperation with [EMPA](http://www.empa.ch/)/[BAFU](http://www.bafu.admin.ch/) who kindly helped us with the installation and provide us with reference data. This deployment is running successfully since April 2011. We use the reference data obtained by this station to calibrate our sensors and to evaluate their performance under a wide range of weather conditions, which is difficult to achieve in a laboratory environment.
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... | ... | @@ -25,16 +26,15 @@ The second station is statically positioned next to the [NABEL](http://www.empa. |
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![](img/misc/db_box.jpg)
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![](img/misc/db_station_outside.jpg)
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![](img/misc/db_station_inside.jpg)
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*The NABEL station in Dübendorf used as reference.*
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For calibrating the sensors, we implemented three calibration schemes for mobile sensor nodes. We investigate single-hop and multi-hop calibration given a reference station which can be reached by the mobile stations from time to time. The first scheme implements a standard way of calibrating gas sensors while the other two approaches show different trade-offs between measurement accuracy and calibration delay. We showed though experiments that when using these calibration schemes for ozone sensors we are able to measure ozone concentrations with an average error of 2ppb compared to the measurements done by the [NABEL](http://www.empa.ch/plugin/template/empa/699/*/---/l=2) station. This is remarkable as the accuracy given in the datasheet of the sensor is 20ppb. Furthermore, we found a linear dependency of the calibration accuracy on the number of calibration hops. The accuracy loss is tolerable as long as the number of calibration hops is rather limited which is the case in public transport networks.
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![](img/misc/hardware_arch_htc.png)
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![](img/misc/mainmenu.png)
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![](img/misc/settings.png)
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![](img/misc/calibration.png)
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![](img/misc/measure.png)
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*Participatory sensing. The user can set the poll interval, adjust calibration parameters, poll sensor measurements, and upload the measurements to a server for further processing.*
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![](img/misc/wair.png)
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*Participatory and personal sensing. Left: Hardware architecture of a ozone sensor connected to a HTC smartphone. Right: Wearable device featuring two metaloxide gas sensors (VOC and O3)*
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Besides the deployments described above, we also have a two prototypical implementations of:
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1. A smartphone-based measurement device. We connected a small-sized, low-cost ozone sensor to an off-the-shelf smartphone running the Android OS. The Android application assists the user with sensor calibration, displays sensor readings, stores them on the memory card, and uploads the stored data to a server for further data processing and visaulization.
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