David Kotz papers for project 'wifi-measure'

Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

Sat, 01 Jun 2024 00:00:00

José Camacho, Katarzyna Wasielewska, Rasmus Bro, and David Kotz. Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring. IEEE Transactions on Network and Service Management, volume 21, number 3, pages 2926–2943. IEEE, June 2024. doi:10.1109/TNSM.2024.3368501.

Abstract:

There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows us to detect and diagnose disparate network anomalies, with a data-analysis workflow that combines the advantages of interpretable and interactive models with the power of parallel processing. We apply the extended MBDA to two case studies: UGR’16, a benchmark flow-based real-traffic dataset for anomaly detection, and Dartmouth’18, the longest and largest Wi-Fi trace known to date.

Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring

Sat, 01 Apr 2023 00:00:00

José Camacho, Rasmus Bro, and David Kotz. Interpretable Learning in Multivariate Big Data Analysis for Network Monitoring. Technical Report number 1907.02677, arXiv, April 2023.

Abstract:

There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows us to detect and diagnose disparate network anomalies, with a data-analysis workflow that combines the advantages of interpretable and interactive models with the power of parallel processing. We apply the extended MBDA to two case studies: UGR'16, a benchmark flow-based real-traffic dataset for anomaly detection, and Dartmouth'18, the longest and largest Wi-Fi trace known to date.

Mining social interactions in connection traces of a campus Wi-Fi network

Sun, 01 Aug 2021 00:00:00

Eduardo Mañas-Martínez, Elena Cabrera, Katarzyna Wasielewska, David Kotz, and José Camacho. Mining social interactions in connection traces of a campus Wi-Fi network. Proceedings of the SIGCOMM Poster and Demo Sessions, 3 pages, pages 6–8. ACM, August 2021. doi:10.1145/3472716.3472844.

Abstract:

Wi-Fi technologies have become one of the most popular means for Internet access. As a result, the use of mobile devices has become ubiquitous and instrumental for society. A device can be identified through its MAC address within an autonomous system. Although some devices attempt to anonymize MAC addresses via randomization, these techniques are not used once the device is associated to the network. As a result, device identification poses a privacy problem in large-scale (e.g., campus-wide) Wi-Fi deployments: if the mobile device can be located, the user who carries that device can also be located. In turn, location information leads to the possibility to extract private knowledge from Wi-Fi users, like social interactions, movement habits, and so forth.

In this poster we report preliminary work in which we infer social interactions of individuals from Wi-Fi connection traces in the campus network at Dartmouth College. We make the following contributions: (i) we propose several definitions of a pseudocorrelation matrix from Wi-Fi connection traces, which measure similarity between devices or users according to their temporal association profile to the Access Points (APs); (ii) we evaluate the accuracy of these pseudo-correlation variants in a simulation environment; and (iii) we contrast results with those found on a real trace.

An inside vs. outside classification system for Wi-Fi IoT devices

Tue, 01 Jun 2021 00:00:00

Paul Gralla. An inside vs. outside classification system for Wi-Fi IoT devices. June 2021. Undergraduate Thesis.

Abstract:

We are entering an era in which Smart Devices are increasingly integrated into our daily lives. Everyday objects are gaining computational power to interact with their environments and communicate with each other and the world via the Internet. While the integration of such devices offers many potential benefits to their users, it also gives rise to a unique set of challenges. One of those challenges is to detect whether a device belongs to one’s own ecosystem, or to a neighbor – or represents an unexpected adversary. An important part of determining whether a device is friend or adversary is to detect whether a device’s location is within the physical boundaries of one’s space (e.g. office, classroom, home). In this thesis we propose a system that is able to decide with 82% accuracy whether the location of an IoT device is inside or outside of a defined space based on a small number of transmitted Wi- Fi frames. The classification is achieved by leveraging a machine-learning classifier trained and tested on RSSI data of Wi-Fi transmissions recorded by three or more observers. In an initialization phase the classifier is trained by the user on Wi-Fi transmissions of a variety of locations, inside (and outside). The system can be built with off-the-shelf Wi-Fi observing devices that do not require any special hardware modifications. With the exception of the training period, the system can accurately classify the indoor/outdoor state of target devices without any cooperation from the user or from the target devices.

Automatic Learning coupled with Interpretability: MBDA in Action

Mon, 01 Jun 2020 00:00:00

José Camacho, Rasmus Bro, and David Kotz. Automatic Learning coupled with Interpretability: MBDA in Action. Proceedings of the Network Traffic Measurement and Analysis Conference (TMA). IFIP, June 2020. ISBN13: 978-3-903176-27-0.

Abstract:

In this paper, we illustrate the application of Multivariate Big Data Analysis (MBDA), a recently proposed interpretable machine-learning method with application to Big Data sets. We apply MBDA for the first time for the detection and troubleshooting of network problems in a campus-wide Wi-Fi network. Data includes a seven-year trace (from 2012 to 2018) of the network’s most recent activity, with approximately 3,000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations. This is the longest and largest Wi-Fi trace known to date. Furthermore, we propose a new feature-learning procedure that solves an inherent limitation in MBDA: the manual definition of the features. The extended MBDA results in a methodology that allows network analysts to identify problems and diagnose them, which are principal tasks to troubleshoot the network and optimize its performance. In the paper, we go through the entire workflow of the approach, illustrating its application in detail and discussing processing times.

Longitudinal analysis of a campus Wi-Fi network

Tue, 07 Apr 2020 00:00:00

José Camacho, Chris McDonald, Ron Peterson, Xia Zhou, and David Kotz. Longitudinal analysis of a campus Wi-Fi network. Computer Networks, volume 170, article 107103, 15 pages. Elsevier, April 7, 2020. doi:10.1016/j.comnet.2020.107103. ISSN: 1389-1286.

Abstract:

In this paper we describe and characterize the largest Wi-Fi network trace ever published: spanning seven years, approximately 3000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations. The 7TB of raw data are pre-processed into connection sessions, which are made available for the research community. We describe the methods used to capture and process the traces, and characterize the most prominent trends and changes during the seven-year span of the trace. Furthermore, this Wi-Fi network covers the campus of Dartmouth College, the same campus detailed a decade earlier in seminal papers about that network and its users’ network behavior. We thus are able to comment on changes in patterns of usage, connection, and mobility in Wi-Fi deployments.

Networkmetrics unraveled: MBDA in Action

Mon, 01 Jul 2019 00:00:00

José Camacho, Rasmus Bro, and David Kotz. Networkmetrics unraveled: MBDA in Action. Technical Report number 1907.02677, arXiv, July 2019.

Abstract:

Networkmetrics is a new term that refers to the data-driven approach for monitoring, troubleshooting and understanding communication networks using multivariate analysis. Networkmetric models are powerful machine learning tools to interpret and interact with data collected from a network. In this paper, we illustrate the application of Multivariate Big Data Analysis (MBDA), a recently proposed networkmetric method with application to Big Data sets. We use MBDA for the detection and troubleshooting of network problems in a campus-wide Wi-Fi network. Data includes a seven-year trace (from 2012 to 2018) of the network’s most recent activity, with approximately 3,000 distinct access points, 40,000 authenticated users, and 600,000 distinct Wi-Fi stations. This is the longest and largest Wi-Fi trace known to date. To analyze this data, we propose learning and visualization procedures that extend MBDA. This results in a methodology that allows network analysts to identify problems and diagnose and troubleshoot them, optimizing the network performance. In the paper, we go through the entire workflow of the approach, illustrating its application in detail and discussing processing times in parallel hardware.

CRAWDAD dataset dartmouth/campus (v. 2009-09-09)

Tue, 01 Sep 2009 00:00:00

David Kotz, Tristan Henderson, Ilya Abyzov, and Jihwang Yeo. CRAWDAD dataset dartmouth/campus (v. 2009-09-09). Available for download on IEEE DataPort, September 2009. doi:10.15783/C7F59T.

Abstract:

This dataset includes syslog, SNMP, and tcpdump data for 5 years or more, for over 450 access points and several thousand users at Dartmouth College.

MAP: A scalable measurement infrastructure for securing 802.11 wireless networks

Tue, 01 Jan 2008 00:00:00

Yong Sheng, Keren Tan, Udayan Deshpande, Bennet Vance, Hongda Yin, Chris McDonald, Tristan Henderson, Guanling Chen, David Kotz, Andrew Campbell, and Joshua Wright. MAP: A scalable measurement infrastructure for securing 802.11 wireless networks. Unpublished manuscript, 2008, 9 pages.

Abstract:

The shared medium of 802.11 wireless networks means that they are susceptible to many MAC-layer attacks, such as frame spoofing, denial of service, and greedy misbehavior. To detect such attacks it is necessary to monitor the wireless channel itself and examine the timing and content of the 802.11 frames. Wireless “sniffing” is challenging: sniffers may not hear every frame, nearby sniffers may hear identical frames, sniffers’ clocks may be poorly synchronized, or a sniffer may have its radio listening to a different channel than that on which an attack is occurring. In addition, sniffing a large-scale wireless network at a reasonable cost is also a challenge. We present MAP (Measure, Analyze, Protect), a scalable architecture for extensive wireless measurement. MAP features dedicated sniffers, near-realtime multi-sniffer traffic merging, coordinated channel sampling, full stream capturing with feature extraction, and analysis-driven capture “refocusing.” Using a deployment of 20 sniffers placed over a three-floor building, we demonstrate how these features help to improve detection accuracy in realistic attack scenarios. Performance evaluation shows that MAP is both effective and efficient at scale.

The Changing Usage of a Mature Campus-wide Wireless Network

Wed, 01 Oct 2008 00:00:00

Tristan Henderson, David Kotz, and Ilya Abyzov. The Changing Usage of a Mature Campus-wide Wireless Network. Computer Networks, volume 52, number 14, pages 2690–2712. Elsevier, October 2008. doi:10.1016/j.comnet.2008.05.003.

Abstract:

Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As “Wi-Fi” technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks. This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including syslog messages, telephone records, SNMP polling and tcpdump packet captures. This is the largest WLAN study to date, and the first to look at a mature WLAN. We compare this trace to a trace taken after the network’s initial deployment two years prior.

We found that the applications used on the WLAN changed dramatically, with significant increases in peer-to-peer and streaming multimedia traffic. Despite the introduction of a Voice over IP (VoIP) system that includes wireless handsets, our study indicates that VoIP has been used little on the wireless network thus far, and most VoIP calls are made on the wired network.

We saw greater heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the “session diameter”. We use this metric to show that embedded devices have different mobility characteristics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.

Measuring Wireless LANs

Thu, 01 Dec 2005 00:00:00

Tristan Henderson and David Kotz. Measuring Wireless LANs. Mobile, Wireless, and Sensor Networks: Technology, Applications, and Future Directions, chapter 1, pages 5–27. Edited by Rajeev Shorey, Akkihebbal L. Ananda, Mun Choon Chan, and Wei Tsang Ooi. John Wiley & Sons, New York, NY, December 2005. doi:10.1002/0471755591.ch1. ISBN13: 9780471755593.

Abstract:

Wireless local area networks have become increasingly popular in recent years, and are now commonplace in many venues, including academic and corporate campuses, residences, and “hotspots” in public areas. It is important to understand how these wireless LANs are used, both for deploying networks, and for the development of future wireless networking protocols and applications.

In this chapter we discuss the measurement and analysis of the popular 802.11 family of wireless LANs. We describe the tools, metrics and techniques that are used to measure wireless LANs. The results of existing measurement studies are surveyed. We illustrate some of the problems that are specific to measuring wireless LANs, and outline some challenges for collecting future wireless traces.

Measuring wireless network usage with the experience sampling method

Fri, 01 Apr 2005 00:00:00

Tristan Henderson, Denise Anthony, and David Kotz. Measuring wireless network usage with the experience sampling method. Proceedings of the Workshop on Wireless Network Measurements (WiNMee), 6 pages. International Communications Sciences and Technology Association (ICST), April 2005. ISBN: 0-9767294-0-7.

Abstract:

Measuring wireless local area networks has proven useful for characterizing, modeling and provisioning these networks. These measurements are typically taken passively from a vantage point on the network itself. Client devices, or users, are never actively queried. These measurements can indicate what is happening on the network, but it can be difficult to infer why a particular behavior is occurring. In this paper we use the Experience Sampling Method (ESM) to study wireless network users. We monitored 29 users remotely for one week, and signaled them to fill out a questionnaire whenever interesting wireless behavior was observed. We find ESM to be a useful method for collecting data about wireless network usage that cannot be provided by network monitoring, and we present a list of recommendations for network researchers who wish to conduct an ESM study.

Analysis of a Campus-wide Wireless Network

Sat, 01 Jan 2005 00:00:00

David Kotz and Kobby Essien. Analysis of a Campus-wide Wireless Network. Wireless Networks, volume 11, number 1--2, pages 115–133. Springer, January 2005. doi:10.1007/s11276-004-4750-0.

Abstract:

Understanding usage patterns in wireless local-area networks (WLANs) is critical for those who develop, deploy, and manage WLAN technology, as well as those who develop systems and application software for wireless networks. This paper presents results from the largest and most comprehensive trace of network activity in a large, production wireless LAN. For eleven weeks we traced the activity of nearly two thousand users drawn from a general campus population, using a campus-wide network of 476 access points spread over 161 buildings at Dartmouth College. Our study expands on those done by Tang and Baker, with a significantly larger and broader population.

We found that residential traffic dominated all other traffic, particularly in residences populated by newer students; students are increasingly choosing a wireless laptop as their primary computer. Although web protocols were the single largest component of traffic volume, network backup and file sharing contributed an unexpectedly large amount to the traffic. Although there was some roaming within a network session, we were surprised by the number of situations in which cards roamed excessively, unable to settle on one access point. Cross-subnet roams were an especial problem, because they broke IP connections, indicating the need for solutions that avoid or accommodate such roams.

The Changing Usage of a Mature Campus-wide Wireless Network

Wed, 01 Sep 2004 00:00:00

Tristan Henderson, David Kotz, and Ilya Abyzov. The Changing Usage of a Mature Campus-wide Wireless Network. Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom), pages 187–201. ACM, September 2004. doi:10.1145/1023720.1023739.

Abstract:

This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including syslogs, telephone records, SNMP polling and tcpdump packet sniffing. This is the largest WLAN study to date, and the first to look at a large, mature WLAN and consider geographic mobility. We compare this trace to a trace taken after the network’s initial deployment two years ago.

We found that the applications used on the WLAN changed dramatically. Initial WLAN usage was dominated by Web traffic; our new trace shows significant increases in peer-to-peer, streaming multimedia, and voice over IP (VoIP) traffic. On-campus traffic now exceeds off-campus traffic, a reversal of the situation at the WLAN’s initial deployment. Our study indicates that VoIP has been used little on the wireless network thus far, and most VoIP calls are made on the wired network. Most calls last less than a minute.

We saw greater heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the “session diameter.” We use this metric to show that embedded devices have different mobility characteristics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.

Simulating mobile ad hoc networks: a quantitative evaluation of common MANET simulation models

Tue, 01 Jun 2004 00:00:00

Calvin Newport. Simulating mobile ad hoc networks: a quantitative evaluation of common MANET simulation models. Technical Report number TR2004-504, Dartmouth Computer Science, Hanover, NH, June 2004. Available as Dartmouth Computer Science Technical Report TR2004-504. Senior Honors Thesis. Advisor: David Kotz.

Abstract:

Because it is difficult and costly to conduct real-world mobile ad hoc network experiments, researchers commonly rely on computer simulation to evaluate their routing protocols. However, simulation is far from perfect. A growing number of studies indicate that simulated results can be dramatically affected by several sensitive simulation parameters. It is also commonly noted that most simulation models make simplifying assumptions about radio behavior. This situation casts doubt on the reliability and applicability of many ad hoc network simulation results.

In this study, we begin with a large outdoor routing experiment testing the performance of four popular ad hoc algorithms (AODV, APRL, ODMRP, and STARA). We present a detailed comparative analysis of these four implementations. Then, using the outdoor results as a baseline of reality, we disprove a set of common assumptions used in simulation design, and quantify the impact of these assumptions on simulated results. We also more specifically validate a group of popular radio models with our real-world data, and explore the sensitivity of various simulation parameters in predicting accurate results. We close with a series of specific recommendations for simulation and ad hoc routing protocol designers.

The Changing Usage of a Mature Campus-wide Wireless Network

Mon, 01 Mar 2004 00:00:00

Tristan Henderson, David Kotz, and Ilya Abyzov. The Changing Usage of a Mature Campus-wide Wireless Network. Technical Report number TR2004-496, Dartmouth Computer Science, March 2004.

Abstract:

Wireless Local Area Networks (WLANs) are now common on academic and corporate campuses. As “Wi-Fi” technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks. This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including syslogs, telephone records, SNMP polling and tcpdump packet sniffing. This is the largest WLAN study to date, and the first to look at a large, mature WLAN and consider geographic mobility. We compare this trace to a trace taken after the network’s initial deployment two years ago.

We saw more heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the “session diameter.” We use this metric to show that embedded devices have different mobility characteristics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.

Problems with the Dartmouth wireless SNMP data collection

Mon, 01 Dec 2003 00:00:00

Tristan Henderson and David Kotz. Problems with the Dartmouth wireless SNMP data collection. Technical Report number TR2003-480, Dartmouth Computer Science, December 2003.

Abstract:

The original Dartmouth wireless network study used SNMP to query the college’s Cisco 802.11b access points. The perl scripts that performed the SNMP queries suffered from some problems, in that they queried inappropriate SNMP values, or misunderstood the meaning of other values. This data was also used in a subsequent analysis. The same scripts were used to collect data for a subsequent study of another wireless network. This document outlines these problems and indicates which of the data collected by the original scripts may be invalid.

Persistence and Prevalence in the Mobility of Dartmouth Wireless Network Users

Thu, 01 May 2003 00:00:00

Clara Lee. Persistence and Prevalence in the Mobility of Dartmouth Wireless Network Users. Technical Report number TR2003-455, Dartmouth Computer Science, Hanover, NH, May 2003. The data in this paper is highly suspect; see TR2003-480. Available as Dartmouth Computer Science Technical Report TR2003-455. Senior Honors Thesis. Advisor: David Kotz.

Abstract:

Wireless local-area networks (WLANs) are increasing in popularity. As more people use WLANs it is important to understand how these users behave. We analyzed data collected over three months of 2002 to measure the persistence and prevalence of users of the Dartmouth wireless network.

We found that most of the users of Dartmouth's network have short association times and a high rate of mobility. This observation fits with the predominantly student population of Dartmouth College, because students do not have a fixed workplace and are moving to and from classes all day.

Analysis of a Campus-wide Wireless Network

Sun, 01 Sep 2002 00:00:00

David Kotz and Kobby Essien. Analysis of a Campus-wide Wireless Network. Technical Report number TR2002-432, Dartmouth Computer Science, September 2002.

Abstract:

Understanding usage patterns in wireless local-area networks (WLANs) is critical for those who develop, deploy, and manage WLAN technology, as well as those who develop systems and application software for wireless networks. This paper presents results from the largest and most comprehensive trace of network activity in a large, production wireless LAN. For eleven weeks we traced the activity of nearly two thousand users drawn from a general campus population, using a campus-wide network of 476 access points spread over 161 buildings. Our study expands on those done by Tang and Baker, with a significantly larger and broader population.

Analysis of a Campus-wide Wireless Network

Sun, 01 Sep 2002 00:00:00

David Kotz and Kobby Essien. Analysis of a Campus-wide Wireless Network. Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom), pages 107–118. ACM, September 2002. doi:10.1145/570645.570659. Revised and corrected as Dartmouth CS Technical Report TR2002-432. Winner of ACM SIGMOBILE Test-of-Time award, 2017.

Abstract:

Understanding usage patterns in wireless local-area networks (WLANs) is critical for those who develop, deploy, and manage WLAN technology, as well as those who develop systems and application software for wireless networks. This paper presents results from the largest and most comprehensive trace of network activity in a large, production wireless LAN. For eleven weeks we traced the activity of nearly two thousand users drawn from a general campus population, using a campus-wide network of 476 access points spread over 161 buildings. Our study expands on those done by Tang and Baker, with a significantly larger and broader population.

Characterizing Usage of a Campus-wide Wireless Network

Fri, 01 Mar 2002 00:00:00

David Kotz and Kobby Essien. Characterizing Usage of a Campus-wide Wireless Network. Technical Report number TR2002-423, Dartmouth Computer Science, March 2002.

Abstract:

Wireless local-area networks (WLANs) are increasingly common, but little is known about how they are used. A clear understanding of usage patterns in real WLANs is critical information to those who develop, deploy, and manage WLAN technology, as well as those who develop systems and application software for wireless networks. This paper presents results from the largest and most comprehensive trace of network activity in a large, production wireless LAN. For eleven weeks we traced the activity of nearly two thousand users drawn from a general campus population, using a campus-wide network of 476 access points spread over 161 buildings. Our study expands on those done by Tang and Baker, with a significantly larger and broader population.

Mobile Voice Over IP (MVOIP): An Application-level Protocol

Fri, 01 Jun 2001 00:00:00

G. Ayorkor Mills-Tettey. Mobile Voice Over IP (MVOIP): An Application-level Protocol. Technical Report number TR2001-390, Dartmouth Computer Science, Hanover, NH, June 2001. Available as Dartmouth Computer Science Technical Report TR2001-390. Senior Honors Thesis. Advisor: David Kotz.

Abstract:

Current Voice over Internet Protocol (VOIP) protocols require participating hosts to have fixed IP addresses for the duration of a VOIP call. When using a wireless-enabled host, such as a tablet computer on an 802.11 wireless network, it is possible for a participant in a VOIP call to roam around the network, moving from one subnet to another and needing to change IP addresses. This address change creates the need for mobility support in VOIP applications.

We present the design of Mobile Voice over IP (MVOIP), an application-level protocol that enables such mobility in a VOIP application based on the ITU H.323 protocol stack. An MVOIP application uses hints from the surrounding network to determine that it has switched subnets. It then initiates a hand-off procedure that comprises pausing its current calls, obtaining a valid IP address for the current subnet, and reconnecting to the remote party with whom it was in a call. Testing the system shows that on a Windows 2000 platform there is a perceivable delay in the hand-off process, most of which is spent in the Windows API for obtaining DHCP addresses. Despite this bottleneck, MVOIP works well on a wireless network.

Measuring early usage of Dartmouth's wireless network

Fri, 01 Jun 2001 00:00:00

Pablo Stern. Measuring early usage of Dartmouth's wireless network. Technical Report number TR2001-393, Dartmouth Computer Science, Hanover, NH, June 2001. Available as Dartmouth Computer Science Technical Report TR2001-393. Senior Honors Thesis. Advisor: David Kotz.

Abstract:

In Spring 2001, Dartmouth College installed a campus-wide 802.11b wireless network. To understand how that network is used, we examined the usage characteristics of the network over a five-week period. We monitored access points to determine user behavior, and user and network traffic characteristics. Because our study coincided with the deployment of the access points, our analysis captures the growth of a wireless network. The results of this study help understand the behavior of mobile users and provide a reference to network engineers wishing to deploy and expand similar wireless networks.