Research Article
Distributed cognitive coexistence of 802.15.4 with 802.11
@INPROCEEDINGS{10.1109/CROWNCOM.2006.363456, author={Sofie Pollin and Mustafa Ergen and Michael Timmers and Antoine Dejonghe and Liesbet Van der Perre and Francky Catthoor and Ingrid Moerman and Ahmad Bahai}, title={Distributed cognitive coexistence of 802.15.4 with 802.11}, proceedings={1st International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2007}, month={5}, keywords={Cognition Costs Delay Energy consumption Frequency Interference Radio spectrum management Sensor phenomena and characterization Wireless LAN Wireless sensor networks}, doi={10.1109/CROWNCOM.2006.363456} }
- Sofie Pollin
Mustafa Ergen
Michael Timmers
Antoine Dejonghe
Liesbet Van der Perre
Francky Catthoor
Ingrid Moerman
Ahmad Bahai
Year: 2007
Distributed cognitive coexistence of 802.15.4 with 802.11
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2006.363456
Abstract
Thanks to recent advances in wireless technology, a broad range of standards are currently emerging. Interoperability and coexistence between these heterogeneous networks are becoming key issues, which require new adaptation strategies to avoid harmful interference. In this paper, we focus on the coexistence of 802.11 Wireless LAN and 802.15.4 sensor networks in the ISM band. Those networks have very different transmission characteristics that result in asymmetric interference patterns. We propose distributed adaptation strategies for 802.15.4 nodes, to minimize the impact of the 802.11 interference. This interference varies in time, frequency and space and the sensor nodes adapt by changing their frequency channel selection over time. Different distributed techniques are proposed, based on scanning (with increasing power cost) on the one hand, and based on increased cognition through learning on the other hand. These techniques are evaluated both for performance and energy cost. We show that it is possible to achieve distributed frequency allocation approaches that result only in an increase of 20% of the delay performance compared to ideal frequency allocation. Moreover, it is shown that a factor of two in energy consumption can be saved by adding learning to the system