... | ... | @@ -13,7 +13,9 @@ The lifetime of all gas sensors is up to 3 years. As part of our cooperation, th |
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# OpenSense Deployments
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Between 2012 and 2017 we maintained two installations of measurement stations in Zurich: 10 stations on top of 10 trams in the city of Zurich and one station at the national air pollution monitoring network [NABEL](http://www.bafu.admin.ch/luft/00612/00625/index.html?lang=en) station in Dübendorf. Both deployments are briefly described below.
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Thanks to the great support of VBZ ([Verkehrsbetriebe Zürich](http://www.vbz.ch/)), the first measurement station was installed on top of a tram in the city of Zurich at the end of September 2011. Currently we have 10 measurement stations travelling through the city on top of trams. The measurement schedule turns the stations off during nights when the trams are in their respective depots, hence, no energy is used when the trams are on battery power supply. Since the impact of mobility on the measured concentration was the subject to investigation, the early version of the schedule performed measurements at the stops rather than during the tram drive. Recognition of movement was performed based on accelerometer data. In the current scheduler implementation, the O,,3,,, and CO sensors are sampled every minute. The ultrafine particle sensor is sampled with 20Hz and 5 second averages are transmitted to the server. Additionally, we regularly receive high resolution sensor measurements from fixed [NABEL](http://www.empa.ch/plugin/template/empa/699/*/---/l=2) and [OstLuft](http://www.ostluft.ch/) stations in Zurich to perform reference measurements and sensor calibration.
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### Tram deployment
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Thanks to the great support of VBZ ([Verkehrsbetriebe Zürich](http://www.vbz.ch/)), the first measurement station was installed on top of a tram in the city of Zurich at the end of September 2011. Currently we have 10 measurement stations travelling through the city on top of trams. The measurement schedule turns the stations off during nights when the trams are in their respective depots, hence, no energy is used when the trams are on battery power supply. Since the impact of mobility on the measured concentration was the subject to investigation, the early version of the schedule performed measurements at the stops rather than during the tram drive. Recognition of movement was performed based on accelerometer data. In the current scheduler implementation, the O3 and CO sensors are sampled every minute. The ultrafine particle sensor is sampled with 20Hz and 5 second averages are transmitted to the server. Additionally, we regularly receive high resolution sensor measurements from fixed [NABEL](http://www.empa.ch/plugin/template/empa/699/*/---/l=2) and [OstLuft](http://www.ostluft.ch/) stations in Zurich to perform reference measurements and sensor calibration.
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![](img/misc/box_installation.jpg)
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![](img/misc/box_on_tram_closed.jpg)
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... | ... | @@ -22,7 +24,8 @@ Thanks to the great support of VBZ ([Verkehrsbetriebe Zürich](http://www.vbz.ch |
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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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### Stationary deployment
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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 Dübendorf 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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![](img/misc/db_box.jpg)
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![](img/misc/db_station_outside.jpg)
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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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### Participatory and personal sensing
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![](img/misc/hardware_arch_htc.png)
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![](img/misc/wair.jpeg)
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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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*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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