Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Sampling and predicting geographic areas using participatory sensing
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Participatory sensing is the concept that people contribute information they retrieved independently from the environment using sensors to build a whole body of knowledge. With the popularity of mobile devices, such as smart phones, which have multiple sensors and wireless interfaces, "participatory sensing" has become feasible in a large-scale. Spatial sampling is a technique using a limited number of geographical samples to achieve high credibility in measurement, and then predicting data values for unsampled areas. In this paper, participatory sensing is combined with spatial sampling and prediction, and evaluated under various scenarios. In this paper, an approach based on participatory sensing, sampling and predicting spatial data and evaluating participatory sensing involving prediction results is designed. A Java system prototype is implemented based on the design. Perlin noise and the ONE simulator are used to implement simulation for spatial sampling with participatory sensing. In the prediction, three different prediction algorithms are applied, Voronoi diagram, Delaunay triangulation with gradient and ordinary Kriging. Evaluation of participatory sensing and spatial sampling is measured by root-mean-square-error between true map and predicted map by pixels. The results of the experiments indicate that generally the Voronoi diagram has larger error value than Delaunay triangulation with gradient when only having a few samples. And ordinary Kriging produces the most accurate results but it has highest time complexity and requires a large number of samples to achieve high accuracy. In addition, more evenly distributed samples contribute to higher accuracy of prediction. Given a proper guide, participants in participatory sensing can improve the spatial sampling quality a lot.

Place, publisher, year, edition, pages
2015. , 83 p.
Series
IT, 15083
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-272194OAI: oai:DiVA.org:uu-272194DiVA: diva2:893390
Supervisors
Examiners
Available from: 2016-01-12 Created: 2016-01-12 Last updated: 2016-01-12Bibliographically approved

Open Access in DiVA

fulltext(1738 kB)76 downloads
File information
File name FULLTEXT01.pdfFile size 1738 kBChecksum SHA-512
3862a404c8ce254af5a1f0991514999b6d680d449dfbe52c6d279ed0aa199adacfa0810d51b1e046b0e8b921b9335f3919c79329dfadf4277d80e2795a728dc6
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 76 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 311 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf