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# The OpenSense Measurement Box
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The OpenSense measurement platform is based on the prototype platform developed within the [Nano-Tera](http://www.nano-tera.ch/) project [X-Sense](http://www.nano-tera.ch/projects/414.php) (as part of the cooperation between the projects) and further extended for monitoring air pollution. The core of the prototype measurement station is a [Gumstix](http://www.gumstix.com/) embedded computer running the Linux operating system. The station supports GPRS/UMTS and WLAN for communication and data transfer. A GPS receiver supplies the station with precise geospatial information. Localization in cities is a challenging task due to street canyons, multi-path effects, and often low number of directly visible satellites. Thus, the measurement station is equipped with an accelerometer and receives the door release signal once installed on a tram to assist recognition of halts and tram stops to minimize the positioning uncertainty. The weight of the developed OpenSense station is approximately 4.5kg and energy consumption is 40W. The station is supplied with power from the tram.
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![](img/misc/box_closed.jpg)
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![](img/misc/box_side.jpg)
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![](img/misc/box_open.jpg)
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*The OpenSense measurement box*
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Every station is equipped with an O3, CO, NO2, and a ultrafine particle (UFP) sensor. The ozone sensor-a metal oxide semiconductor gas sensor-performs measurements by heating up the surface of a small microchip with a thin layer of a semiconducting metal oxide to several 100°C. When ozone gas is present, the electric conductivity of the semiconductor is altered. The CO and NO2 sensors are electrochemical gas sensors that measure the concentration of a target gas by oxidizing and reducing the target gas at the electrode. The mounting points of all gas sensors are covered with a thin Teflon layer to minimize interference of the target gases with the dust cover of the measurement station.
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The lifetime of all gas sensors is up to 3 years. As part of our cooperation, the University of Applied Sciences Nordwestschweiz ([Fachhochschule Nordwestschweiz](http://www.fhnw.ch/personen/martin-fierz)) provides ultrafine particle sensors and the necessary expertise. Furthermore, we monitor temperature and humidity in the enclosure.
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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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![](img/misc/box_installation.jpg)
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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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![](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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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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2. A wearable measurement device in the form of smartwatch. Two metaloxide gas sensors are used to measure volatile organic compounds (VOC) while the user is indoors and ozone (O3) while the user is outdoors.
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Due to potential interference from the user on the gas sensor devices we also apply a tailor-made neural network based calibration process which is running on the users smartphone. |