Characterizing Flows in Large Wireless Data Networks Export

@inproceedings{meng:flows, abstract = {Several studies have recently been performed on wireless university campus networks, corporate and public networks. Yet little is known about the flow-level characterization in such networks. In this paper, we statistically characterize wireless network using a recently-collected trace. For static flows, we take a two-tier approach to characterizing the flow arrivals, which results a Weibull regression model. We further discover that the static flow arrivals in spatial proximity show strong similarity. As for roaming flows, they can also be well characterized statistically. We explain the results by user behaviors and application demands, and further crossvalidate the modeling results by three other traces. Finally, we use two examples to illustrate how to apply our models for performance evaluation in the wireless context.}, author = {Meng, Xiaoqiao G. and Wong, Starsky and Yuan, Yuan and Lu, Songwu}, booktitle = {Proceedings of the Tenth Annual International Conference on Mobile Computing and Networking}, citeulike-article-id = {2820699}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1023720.1023738}, keywords = {crawdad, dartmouth\_campus, ibm\_watson, measurement, stanford\_gates, ucsd\_sigcomm2001, wireless}, month = {September}, posted-at = {2008-05-21 17:27:06}, priority = {2}, publisher = {ACM Press}, title = {Characterizing Flows in Large Wireless Data Networks}, url = {http://portal.acm.org/citation.cfm?id=1023720.1023738}, year = {2004} }

TY - CONF ID - meng:flows L3 - citeulike-article-id:2820699 N2 - Several studies have recently been performed on wireless university campus networks, corporate and public networks. Yet little is known about the flow-level characterization in such networks. In this paper, we statistically characterize wireless network using a recently-collected trace. For static flows, we take a two-tier approach to characterizing the flow arrivals, which results a Weibull regression model. We further discover that the static flow arrivals in spatial proximity show strong similarity. As for roaming flows, they can also be well characterized statistically. We explain the results by user behaviors and application demands, and further crossvalidate the modeling results by three other traces. Finally, we use two examples to illustrate how to apply our models for performance evaluation in the wireless context. TI - Characterizing Flows in Large Wireless Data Networks PB - ACM Press T2 - Proceedings of the Tenth Annual International Conference on Mobile Computing and Networking KW - crawdad KW - dartmouth_campus KW - ibm_watson KW - measurement KW - stanford_gates KW - ucsd_sigcomm2001 KW - wireless AU - Meng, Xiaoqiao AU - Wong, Starsky AU - Yuan, Yuan AU - Lu, Songwu PY - 2004/September// UR - http://portal.acm.org/citation.cfm?id=1023720.1023738 ER -