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Low-Power Listening Goes Multi-Channel
Swedish Institute of Computer Science.
Swedish Institute of Computer Science.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
2014 (English)In: 2014 IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (IEEE DCOSS 2014), 2014, 2-9 p.Conference paper, Published paper (Refereed)
Abstract [en]

Exploiting multiple radio channels for communicationhas been long known as a practical way to mitigateinterference in wireless settings. In Wireless Sensor Networks,however, multichannel solutions have not reached their fullpotential: the MAC layers included in TinyOS or the ContikiOS for example are mostly single-channel. The literature offersa number of interesting solutions, but experimental results wereoften too few to build confidence. We propose a practical extensionof low-power listening, MiCMAC, that performs channel hopping,operates in a distributed way, and is independent of upper layersof the protocol stack. The above properties make it easy todeploy in a variety of scenarios, without any extra configuration/scheduling/channelselection hassle. We implement our solutionin Contiki and evaluate it in a 97-node testbed while runninga complete, out-of-the-box low-power IPv6 communication stack(UDP/RPL/6LoWPAN). Our experimental results demonstrateincreased resilience to emulated WiFi interference (e.g., data yieldkept above 90% when ContikiMAC drops in the 40% range). In noiseless environments, MiCMAC keeps the overhead low incomparison to ContikiMAC, achieving performance as high as 99% data yield along with sub-percent duty cycle and sub-secondlatency for a 1-minute inter-packet interval data collection.

Place, publisher, year, edition, pages
2014. 2-9 p.
Series
IEEE International Conference on Distributed Computing in Sensor Systems
Keyword [en]
Wireless Sensor Networks
National Category
Computer Science
Research subject
Computer Science with specialization in Computer Communication; Computer Science with specialization in Embedded Systems; Computer Science with specialization in Real Time Systems
Identifiers
URN: urn:nbn:se:uu:diva-237685DOI: 10.1109/DCOSS.2014.33ISI: 000361020100002OAI: oai:DiVA.org:uu-237685DiVA: diva2:768451
Conference
9th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS), MAY 26-28, 2014, Marina Del Rey, CA
Available from: 2014-12-03 Created: 2014-12-03 Last updated: 2015-11-04Bibliographically approved

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Iyer, VenkatramanVoigt, Thiemo

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