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Scalable Data Collection for Mobile Wireless Sensor Networks
KTH, School of Electrical Engineering (EES), Communication Networks.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the near future WSNs (wireless sensor networks) which consist of tiny wireless embedded systems will be an inseparable part of our daily lives. Data collection, collecting data from a large number of sources to one or more base stations, is a typical application for WSNs. A substantial number of data collection algorithms have been specifically designed for static scenarios while there are some scenarios in which sensor nodes are attached to intrinsically mobile objects. Generally, in such scenarios delay tolerant networking approaches have been exploited for offline data analysis. However, in-situ dta collection from mobile scenarios has received little attention.

We propose Mobile Collect to address the limitations of static data collection protocols in mobile scenarios. For this purpose, Collection Tree Protocol (CTP), a de facto standard for data collection, which is implemented in Contiki-OS (Contiki Collect), has been optimized to avoid loops and to react quickly to topology changes which occur frequently in mobile scenarios. The MAC (Medium Access Control) layer in WSNs has a decisive impact on the overall performance of mobile networks in terms of power consumption, and packet delivery rate. We have evaluated Mobile Collect protocol with a receiver-initiated (A-MAC that we implemented in Contiki-OS) and a sender-initiated (Contiki-MAC) MAC protocol.

Compared to the Contiki Collect and the recently proposed DYMO (Dynamic MANET On-demand) protocol, MObile Collect with Contiki-MAC shows a significant improvement in reliability while it has a slight increase in power consumption. A-MAC slightly improves reliability for sparse topologies, but has higher power consumption.

Place, publisher, year, edition, pages
2012. , 67 p.
EES Examensarbete / Master Thesis, XR-EE-LCN 2012:001
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-98818OAI: diva2:539203
Educational program
Master of Science - Network Services and Systems
Available from: 2012-07-16 Created: 2012-07-03 Last updated: 2012-07-16Bibliographically approved

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