uu.seUppsala University Publications
Change search
ReferencesLink to record
Permanent link

Direct link
Adaptive Sampling in Wireless Sensor Networks for Air Monitoring System
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In wireless sensor networks (WSN), it is important to use resources efficiently because sensors have limited resources such as battery life and computational power. In this thesis, we study the method which can save energy of air-monitoring sensor networks with respect of QoS (quality of service). From historical data, we observe that during certain time of the day, concentration of air pollutants has no radical change, from which we can conclude that applying high sampling rate uniformly all the time is not necessarily required. Our approach uses Kalman filter technique to eliminate the noise from the sensor measurements, and adjust the sampling interval based on the difference between the present and previous measurements. If the sampling interval is within the sampling interval range, we use the new sampling interval for the next measurement and if not, a central server assigns a new sampling interval and sampling interval range to the requesting sensor. This way, we can achieve adaptive sampling based on input characteristics so as to save energy of the sensor net- work and also to obtain proper accuracy of sensor measurements. We simulated our method with real measurement data with Matlab and finally implemented our method in the GreenIoT project to demonstrate the energy- efficiency and sensing-quality of our technique.

Place, publisher, year, edition, pages
2016. , 44 p.
IT, 16029
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-295995OAI: oai:DiVA.org:uu-295995DiVA: diva2:935757
Educational program
Masters Programme in Embedded Systems
Available from: 2016-06-12 Created: 2016-06-12 Last updated: 2016-06-12Bibliographically approved

Open Access in DiVA

fulltext(6937 kB)58 downloads
File information
File name FULLTEXT01.pdfFile size 6937 kBChecksum SHA-512
Type fulltextMimetype application/pdf

By organisation
Department of Information Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 58 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

Total: 76 hits
ReferencesLink to record
Permanent link

Direct link