OpenSense: Open sensor networks for air quality monitoring
Project Leader: Karl Aberer of LSIR-EPFL +41 21 693 4679
Boi Faltings of EPFL, expert in Plan Recognition. distributed algorithms for information gathering and integration
Alcherio Martinoli of EPFL/ENAC/ISTE/DISAL, expert in Modeling-self-organized systems
Lothar Thiele of ETHZ / Computer Engineering & Networks Lab., expert in Resource constrained system optimization. RF communication. Computer engineering
Martin Vetterli of EPFL-Audiovisual Communications Laboratory, expert in Mathematical signal processing-superresolution-image/video processing. signal processing for sensing
Wireless sensor networks and publishing of sensor data on the internet bear the potential to substantially increase public awareness and involvement in environmental sustainability. These technologies enable capturing sensor data by involving public authorities and the general public and making real-time information on environmental conditions available to a wide public. Air pollution monitoring in urban areas is a prime example of such an application as common air pollutants have direct effects on human health, thus becoming an extremely important environmental issue in large areas of the world due to increasing urbanization. However, bringing the vision of public involvement in environmental monitoring to a reality poses substantial technical challenges, to scale up from isolated well controlled systems to an open and scalable infrastructure where many nano-scale sensors generate terabytes of data.
Challenges that are not well addressed today are dealing with the heterogeneity and widely varying characteristics of the sensor equipment, measurements and data analysis, supporting and exploiting mobility of sensors and involving the community in a trusted, fair and transparent manner into the monitoring activity. Air pollution monitoring is particularly suited to study these challenges as they are particularly pronounced in this scenario. A wide variety of sensors (meteorological data, air pollutants and fine particles) is used, normally not integrated with one another, with measurements sharing complex atmospheric chemistry and transport processes. These monitors could be stationary or mobile (public and private vehicles, personal devices, airborne vehicles) providing real-time information and warnings on air pollution that is of great public health importance.
OpenSense will address key research challenges in the domain of information and communication systems related to community-based sensing using wireless sensor network technology in the context of air pollution monitoring. Solutions to these problems affect typically all layers of an information and communication system architecture, with interdependencies and synergies among the different layers. For that reason the research team consists of experts in signal processing, networking, robotics, data management and qualitative reasoning. The project will result in open technology that allows integrating diverse sensors, including mobile sensors, into a single environmental model. The information processing techniques we develop will provide important insights to enable other Nano-Tera application domains dealing with monitoring complex events.
Wireless sensor networks and publishing of sensor data on the internet bear the potential to substantially increase public awareness and involvement in environmental sustainability. These technologies enable capturing sensor data by involving public authorities and the general public and making real-time information on environmental conditions available to a wide public. Air pollution monitoring in urban areas is a prime example of such an application as common air pollutants have direct effects on human health, thus becoming an extremely important environmental issue in large areas of the world due to increasing urbanization. However, bringing the vision of public involvement in environmental monitoring to a reality poses substantial technical challenges, to scale up from isolated well controlled systems to an open and scalable infrastructure where many nano-scale sensors generate terabytes of data.
