The changing usage of a mature campus-wide wireless network

By H. Tristan, David F. Kotz, Ilya Abyzov. ACM/IEEE International Conference on Mobile Computing and Networking, 2004

859 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/7683d1336a7bafd92e29c555cfb89696ffe5ce61

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

On dominant characteristics of residential broadband internet traffic (2009, result): “[22] compared these results with newer traces from 2003/2004 of the same network, finding major shifts in the application mix (HTTP 63%→27%, File systems 5%→19%, P2P 5%→22%), and that more traffic stayed on-campus than in 2001 (70%, up from 34%).”
Evaluating Mobility Pattern Space Routing for DTNs (2005, methodology): “At a point in time, t , the node k will have a set of directly connected neighbors, which we write as ∪ { k } is the augmented neighborhood that contains k .”
Performance analysis of mobility-assisted routing (2006, methodology): “As can be seen from its description, this model captures real life mobility characteristics observed in various traces [4, 18–20 , 25 ] considerably better than the Let us denote as π r the probability that a given epoch of node i is a local or a roaming one, respectively.”
Distributed community detection in delay tolerant networks (2007, methodology): “We would also like to acknowledge CRAWDAD project [10] for their hosting and sharing of the mobility data.”
Pocket Switched Networks: Real-world mobility and its consequences for opportunistic forwarding (2005, methodology): “This category includes Balazinska and Castro’s study [3] as well as the data gathered at UCSD [4] and Dartmouth [5] which we analyse in this paper.”
Access and mobility of wireless PDA users (2003, methodology): “[3] repeated the earlier Dartmouth study in Fall 2003 and Winter 2004 after the campus had matured and wireless usage on campus became more prevalent.”
Jigsaw: solving the puzzle of enterprise 802.11 analysis (2006, background): “We are unaware of any production wireless network monitored at similar scale.”
NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems (2011, methodology): “Between 2001 and 2004 data about traffic in the access points was collected through three techniques: syslog events, SNMP polls, and network sniffers [9, 15].”
DTN routing in a mobility pattern space (2005, background): “observed [15] that 50% of users studied spent 62% of their time attached to a single access point and this proportion decreased exponentially.”
Spatio-temporal facility utilization analysis from exhaustive WiFi monitoring (2015, background): “Such studies have been performed both in an university campus settings [8], corporate settings [9] and urban settings [10].”
Study of a bus-based disruption-tolerant network: mobility modeling and impact on routing (2007, methodology): “Hsu and Helmy [11] also used Dartmouth traces [10] to study encounter-based broadcasting.”
Impact of Human Mobility on Opportunistic Forwarding Algorithms (2007, background): “The University of California, San Diego (UCSD) [2] and Dartmouth University [3] traces make use of WiFi networking, with the former including client-based logs of the visibility of access points (APs), while the latter includes SNMP logs from the access points.”
Extracting a Mobility Model from Real User Traces (2006, methodology): “We chose to concentrate on the always-on VoIP device users, as these have been shown to have higher mobility [5].”
Modeling Time-Variant User Mobility in Wireless Mobile Networks (2007, methodology): “We also achieved good matching with the USC[22] or the Dartmouth[11] traces, but do not show it here due to space constraints.”