ActiveCampus: experiments in community-oriented ubiquitous computing
By W. Griswold, Patricia Shanahan, Steven W. Brown, Robert T. Boyer, M. Ratto, R. Benjamin Shapiro, T. Truong. Computer, 2004
314 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Information Systems
View Paper: https://www.semanticscholar.org/paper/192a592d11117e020cc0bc4c7a8ac3dfb9d58ed9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Jeffrey Hightower
- Gaetano Borriello
- Wei Xi
- Ionut Constandache
- Ahmed Elgohary
- Moustafa Youssef
- Romit Roy Choudhury
- Seth J. Teller
In Information Systems
- Ahmed Elgohary
- Romit Roy Choudhury
- Sunny Consolvo
- Yunhao Liu
- Bozidar Radunovic
- Chenshu Wu
- Zheng Yang
- Tom Minka
Citations in the corpus
- Towards Mobile Phone Localization without War-Driving (2010, background): “Active Campus [ 21 ] is similar to PlaceLab, but assumes that the locations of the WiFi access points are known ap riori.”
- SurroundSense: mobile phone localization via ambience fingerprinting (2009, background): “UCSD’s Active Campus project [12] adopts similar techniques of localization, but assumes that the location of the WiFi access points are known a priori.”
- No need to war-drive: unsupervised indoor localization (2012, background): “UCSD’s Active Campus project [6] adopts similar techniques of localization, but assumes that the location of the WiFi access points are known a priori.”
- Growing an organic indoor location system (2010, methodology): “Building on the ActiveCampus approach [12], less trusted, organic refinements could complement an initial, trusted survey of mostly public spaces.”
- Place Lab: Device Positioning Using Radio Beacons in the Wild (2005, methodology): “The Active Campus project is one of the most widely used 802.11-based location-enhanced systems [ 4 ].”
- Avoiding multipath to revive inbuilding WiFi localization (2013, background): “Place Lab [20] and Active Campus [21] attempt to reduce the overhead of calibration by combining WiFi and GSM signal information.”
- Smartphones Based Crowdsourcing for Indoor Localization (2015, methodology): “Most of these techniques utilize the RF signals such as RADAR [2], Horus [40], improved upon RADAR, LANDMARC [21], ActiveCampus [11], PlaceLab [16] and OIL [25].”
- Locating in fingerprint space: wireless indoor localization with little human intervention (2012, methodology): “ActiveCampus: experiments in community-oriented ubiquitous computing.”
- You are facing the Mona Lisa: spot localization using PHY layer information (2012, background): “Place Lab [6] and the Active Campus project [20] attempt to reduce the overhead of calibration – they show that collecting signal information from WiFi and GSM base stations through war-driving is adequate for reasonably accurate localization.”
- WILL: Wireless indoor localization without site survey (2012, methodology): “Active- Campus [10] project adopts similar techniques but assumes availability of AP locations.”