Measurement and analysis of the error characteristics of an in-building wireless network
Conference proceedings on Applications, technologies, architectures, and protocols for computer communications
Wireless Andrew: experience building a high speed, campus-wide wireless data network
MobiCom '97 Proceedings of the 3rd annual ACM/IEEE international conference on Mobile computing and networking
Analysis of a metropolitan-area wireless network
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Analysis of a local-area wireless network
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Characterizing user behavior and network performance in a public wireless LAN
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Analysis of a campus-wide wireless network
Proceedings of the 8th annual international conference on Mobile computing and networking
Characterizing flows in large wireless data networks
Proceedings of the 10th annual international conference on Mobile computing and networking
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
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Modeling and analysis of wireless traffic is fundamental to traffic engineering and resource management. The majority of existing work in this arena has focused on modeling/analyzing aggregate traffic, and little has been done in modeling/analyzing wireless traffic at the per-user level. In this paper, we bridge the gap and perform a detailed analysis and modeling on the traffic generated by individual wireless users, leveraging the data traces collected in a period of 4 months (November 2003 - February 2004) on the Dartmouth campus-wide 802.11 WLANs. Our study indicates that several parameters that characterize the wireless traffic generated by individual users are predictable, such as the traffic volume originating from a user and destined for a specific IP address, the set of destination IP addresses (for which connections initiated by a user are destined), and the patterns by which a wireless user connects to applications. We also model the per-connection traffic volume, the duration, and the interarrival time, of connections issued by a user using Weibull or Pareto distributions, and show that the burstiness of aggregate wireless traffic (which has been reported in the literature) is a direct consequence of the burstiness of the burstiness of traffic generated by individual users. These findings can be used to optimize traffic and resource management and provide better QoS (in terms of availability, resiliency, and performance).