An Empirical Activity Model for WLAN Users

Phillips, Caleb; Singh, Suresh

doi:10.1109/infocom.2008.272

Cited by 6 publications

(9 citation statements)

References 22 publications

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“…The details of this are outside of the scope of the present work, but are explained further in [22] and [18].…”

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

“…Starting with a pcap-format trace, such as is generated by the popular tool tcpdump, we first identify and split on good clients, omitting trace noise as described in [18]. Each packet in each user trace is reduced to the bare minimum information: (1) frame timestamp encoded as the relative time since the previous frame and (2) the size in bytes.…”

Section: Traffic Generation Frameworkmentioning

confidence: 99%

“…Our second classifier is based on the stochastic similarity metric introduced by Nechyba et al in [22], and first applied to trace classification in our work in [18]. This similarity metric provides a convenient number between 0 and 1 which describes the extent to which two observation sequences are similar.…”

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

“…This similarity metric provides a convenient number between 0 and 1 which describes the extent to which two observation sequences are similar. Each user trace is converted to a 1-second bucketed 4-state activity trace of the type proposed in [18]. We then perform a factorial similarity analysis of all the user-activity traces, finding the similarity for each unique pair of user-activity traces.…”

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

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Techniques for simulation of realistic infrastructure wireless network traffic

Phillips

Sicker

Grunwald

et al. 2009

2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

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In the design of wireless networking protocols and systems, simulation has become the primary form of initial validation and performance evaluation. Hence, ensuring the realism of simulators and simulation methods is fundamental for simulated results to be interpretable. In this paper, we provide a simulation framework for infrastructure wireless network traffic that allows researchers to use publicly available captured traces as a primary or background traffic source. We investigate the question of trace classification as a necessary task for these traces to be useful and apply our framework to a well-known power-saving application, showing that the use of real traffic provides substantially different results as compared to traffic generated from an application-specific fitted model or contrived source. Additionally, we show how trace classification provides unique insights into application behavior in both typical and extreme scenarios.

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“…The details of this are outside of the scope of the present work, but are explained further in [22] and [18].…”

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

Section: Traffic Generation Frameworkmentioning

confidence: 99%

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

Section: Stochastic Similarity Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

Techniques for simulation of realistic infrastructure wireless network traffic

Phillips

Sicker

Grunwald

et al. 2009

2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…The interfaces are designed to handle different states in order to decrease the power consumption [7] [8]. It can be described with three states: active (ON), idle and sleeping (OFF).…”

Section: Background and Related Workmentioning

confidence: 99%

Delay-conscious caching scheme for energy-efficient video streaming

Huszák

2013

2013 IEEE 14th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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Wireless multimedia streaming became a popular service of today's mobile networks, however due to the limited battery capacity energy efficient solutions are required to extend the availability of the mobile device. Although energy efficiency is getting more and more critical factor, the experienced quality of the customer can not be deteriorated. In this paper an energy efficient caching scheme is proposed that keeps the balance between the power consumption and the acceptable playout delay caused by caching that has direct influence on the perceived quality. According to the obtained results the proposed delay-conscious caching model can serve as a guideline for the offline playout delay determination in order to keep the delay as low as possible, but reduce the consumed energy as well.

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Modeling 802.11 AP usage through daily keep-alive event counts

Massa

Morla

2012

Wireless Netw

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Wireless and in particular 802.11 is one of the major technologies for accessing the Internet at home, in coffee shops or other public places, and in enterprises and university campuses. While most recent work on modeling wireless sites focuses on user mobility, this paper presents and compares a number of models for characterizing access point (AP) usage; moreover, rather than looking at throughput we focus on daily counts of keep-alive events that mobile devices generate every 15 minutes they are connected to the wireless network. Our models are trained and evaluated on data collected from a Porto hotspot of Eduroam, the European academic wireless network. The models we present are generative, in the sense they can be used to generate synthetic daily event counts for a single AP or a collection of APs. We provide standard crossvalidation comparison of models using the log-likelihood of the models on training and test data.

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An Empirical Activity Model for WLAN Users

Cited by 6 publications

References 22 publications

Techniques for simulation of realistic infrastructure wireless network traffic

Techniques for simulation of realistic infrastructure wireless network traffic

Delay-conscious caching scheme for energy-efficient video streaming

Modeling 802.11 AP usage through daily keep-alive event counts

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