Characterising the use of a campus wireless network

Schwab, Daniel; Bunt, Richard B.

doi:10.1109/infcom.2004.1356974

Cited by 89 publications

(69 citation statements)

References 4 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth noting, however, that there are an order of magnitude more hotspot users than modem users, yet the modem users place similar aggregate traffic usage demands on the network. The hotspot application workload most closely resembles previous campus studies of application traffic breakdowns [16], [30] with the predominance of HTTP, peer-to-peer, and other TCP traffic, and negligible interactive traffic.…”

Section: ) Sessionssupporting

confidence: 60%

“…These studies have focused on network performance and reliability as well as user behavior from the perspectives of low-level network characteristics to high-level application use. With their more extensive geographic coverage, larger-scale studies of outdoor 802.11 networks-primarily on university campuses-have provided further insight into mobility and other user behavior [9], [16], [17], [20], [23], [24], [30], [35].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Usage Patterns in an Urban WiFi Network

Afanasyev

Chen

Voelker

et al. 2010

IEEE/ACM Trans. Networking

107

View full text Add to dashboard Cite

Abstract-While WiFi was initially designed as a local-area access network, mesh networking technologies have led to increasingly expansive deployments of WiFi networks. In urban environments, the WiFi mesh frequently supplements a number of existing access technologies, including wired broadband networks, 3G cellular, and commercial WiFi hotspots. It is an open question what role city-wide WiFi deployments play in the increasingly diverse access network spectrum. We study the usage of the Google WiFi network deployed in Mountain View, California, and find that usage naturally falls into three classes, based almost entirely on client device type, which we divide into traditional laptop users, fixed-location access devices, and PDA-like smartphone devices. Moreover, each of these classes of use has significant geographic locality, following the distribution of residential, commercial, and transportation areas of the city. When comparing the network usage of each device class, we find a diverse set of mobility patterns that map well to the archetypal use cases for traditional access technologies. To help place our results in context, we also provide key performance measurements of the mesh backbone and, where possible, compare them to those of previously studied urban mesh networks.

show abstract

Section: ) Sessionssupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Usage Patterns in an Urban WiFi Network

Afanasyev

Chen

Voelker

et al. 2010

IEEE/ACM Trans. Networking

107

View full text Add to dashboard Cite

show abstract

“…Though they have a fairly large testbed, they cannot capture inter-network diversity. Other campus studies address questions of traffic load [20,34] and mobility [27,35].…”

Section: Wireless Measurement Studiesmentioning

confidence: 99%

Measurement and analysis of real-world 802.11 mesh networks

LaCurts

Balakrishnan

2010

Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement

View full text Add to dashboard Cite

Despite many years of work in wireless mesh networks built using 802.11 radios, the performance and behavior of these networks in the wild is not well-understood. This lack of understanding is due in part to the lack of access to data from a wide range of these networks; most researchers have access to only one or two testbeds at any time. In recent years, however, 802.11 mesh networks networks have been deployed commercially and have real users who use the networks in a wide range of conditions. This paper analyzes data collected from 1407 access points in 110 different commercially deployed Meraki [28] wireless mesh networks, constituting perhaps the largest study of real-world 802.11 networks to date.After analyzing a 24-hour snapshot of data collected from these networks, we answer questions from a variety of active research topics, such as the accuracy of SNR-based bit rate adaptation, the impact of opportunistic routing, and the prevalence of hidden terminals. The size and diversity of our data set allows us to analyze claims previously only made in small-scale studies. In particular, we find that the SNR of a link is a good indicator of the optimal bit rate for that link, but that one could not make an SNR-to-bit rate look-up table that was accurate for an entire network. We also find that an ideal opportunistic routing protocol provides little to no benefit on most paths, and that "hidden triples"-network topologies that can lead to hidden terminals-are more common than suggested in previous work, and increase in proportion as the bit rate increases.

show abstract

“…Every laptop, and more recently, every smartphone have also become WiFi enabled. Extended research has been made on the wireless networks usage, e.g., [4] and user mobility and traffic patterns, e.g., [3], aiming to provide useful elements that might help to improve network performance [1] and management and planning [6].…”

Section: Related Workmentioning

confidence: 99%

Groups and Frequent Visitors Shaping the Space Dynamics

Baras

Moreira

2011

Smart Spaces and Next Generation Wired/Wireless Networking

View full text Add to dashboard Cite

Abstract.Our research is about a dynamic symbolic space model that is fed with data from the environment by a set of processing modules that receive raw data from sensor networks. For the conducted experiments we have been using data from a WiFi network as it is a widely available infrastructure in our campus. Here we propose two processing modules which will provide more information about the spaces described in the model. The first one tries to implement our human perception of the usual visitors of a place using two measures, the long term and the short term tenant level. The second one detects where groups of users emerge, how many there are and what are their dimensions. Based on this new perspective of the campus we intend to realize how the presence of people shapes the dynamics of a space.

show abstract

Characterising the use of a campus wireless network

Cited by 89 publications

References 4 publications

Usage Patterns in an Urban WiFi Network

Usage Patterns in an Urban WiFi Network

Measurement and analysis of real-world 802.11 mesh networks

Groups and Frequent Visitors Shaping the Space Dynamics

Contact Info

Product

Resources

About