Participatory sensing
Participatory sensing ( Engl. Participatory acquisition) (also known as Urban, Citizen, Human-Centered, People-Centric or opportunistic sensing called) is a concept for measuring data , contributing in which groups of people sensor information to knowledge to generate. While classic sensor networks are based on statically placed sensor nodes, the mobility of the participants plays a central role here.
definition
The term participatory sensing is defined differently depending on the area of application. The concept is based on assumptions that result implicitly from the specific applications.
A general definition can be found in:
“In participatory sensing, a large number of people collect data or information in a data collection campaign with the help of widespread mobile devices, often time-related, measured values, data or information that can be recorded with the built-in mechanisms of the mobile device. The data collectors forward their information to a central instance (server), which processes it for specific purposes. The type of information to be collected is predefined in the scenario. In complex scenarios, the central authority can take over the coordination of the potential collectors. "
Comparison with classic sensor networks
A central component of participatory sensing is the mobility of the sensors compared to the location-relatedness in classic sensor networks. The advantages are that a higher spatial resolution can be achieved, since the measurement is not limited to individual static sensors. In particular, the data is recorded wherever people are and there is therefore a need for information. In addition, those affected by the data-based applications can be directly involved in the data collection. Especially for services that benefit society, this approach can lead to greater acceptance.
A disadvantage of participatory sensing is that the quality of the information can only be guaranteed to a limited extent. The reasons for this are, on the one hand, that the devices are not operated by professional staff but by laypeople. On the other hand, sensors that are as inexpensive and compact as possible are usually used in order to enable mobile use or to achieve widespread use. In order to be able to guarantee a sufficient quality of the data despite these challenges, approaches for the automatic calibration of the sensors exist .
Furthermore, questions arise regarding the effective incentive to participate, data security and data protection .
Usage scenarios
The increasing spread of mobile devices with a large number of sensors , above all smartphones with GPS receivers, microphones and cameras, makes the use of participatory sensing on a large scale possible.
Concrete usage scenarios are:
- Control of environmental pollution (e.g. fine dust , noise , hazardous substances )
- Recording of road damage
- Recording and evaluation of the distribution of plants and animals
- Recording of damage after storms or other weather events
See also
Individual evidence
- ↑ Jeffrey Burke et al .: Participatory sensing. Retrieved November 2, 2014 . (English).
- ↑ a b Dr. Andreas Abecker, Dr. Wassilios Kazakos, Julio Melo de Borges, Dr. Valentin Zacharias: Contributions to a technology for participatory sensing applications. Retrieved November 2, 2014 .
- ↑ Matthias Wetter, ETH Zurich: Sensor-based data acquisition in the service of society. Retrieved November 2, 2014 .
- ↑ Vladimir Bychkovskiy, Seapahn Megerian, Deborah Estrin, Miodrag Potkonjak: A Collaborative Approach to In-Place Sensor Calibration In: Information Processing in Sensor Networks, Lecture Notes in Computer Science , Vol. 2634, Springer-Verlag, 2003, pp. 301– 316.
- ^ Thomas Ludwig, Simon Scholl: Participatory Sensing in the context of empirical research. (No longer available online.) Archived from the original on November 2, 2014 ; Retrieved November 2, 2014 . Info: The archive link was inserted automatically and has not yet been checked. Please check the original and archive link according to the instructions and then remove this notice.
- ↑ Delphine Christin, Andreas Reinhardt, Salil S Kanhere, Matthias Hollick : A Survey on Privacy in Mobile Participatory Sensing Applications In: The Journal of Systems and Software , Vol. 84, Issue 11, Elsevier, 2011, pp. 1928-1946.
- ↑ Karlsruhe Institute of Technology: Measure particulate matter pollution using a smartphone. Retrieved November 2, 2014 .
- ↑ Dr. Immanuel Schweizer, Technical University Darmstadt: da_sense: Noise measurement by mobile phone. Retrieved November 2, 2014 .
- ^ Roman Kernchen, Laborpraxis: Participatory Sensing of Chemical Hazardous Substances in the Air. Retrieved November 2, 2014 .
