BibTeX for papers by David Kotz; for complete/updated list see https://www.cs.dartmouth.edu/~kotz/research/papers.html @InProceedings{martinez:poster, author = {Eduardo Antonio Ma{\~{n}}as-Mart{\'{\i}}nez and Elena Cabrera and Katarzyna Wasielewska and David Kotz and Jos{\'{e}} Camacho}, title = {{Mining social interactions in connection traces of a campus Wi-Fi network}}, booktitle = {{Proceedings of the SIGCOMM Poster and Demo Sessions}}, year = 2021, month = {August}, numpages = 3, pages = {6--8}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3472716.3472844}, URL = {https://www.cs.dartmouth.edu/~kotz/research/martinez-poster/index.html}, 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. \par 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.}, } @Article{rawassizadeh:datasets, author = {Reza Rawassizadeh and David Kotz}, title = {{Datasets for Mobile, Wearable and IoT Research}}, journal = {GetMobile: Mobile Computing and Communications}, year = 2017, month = {April}, volume = 20, number = 4, pages = {5--7}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/3081016.3081018}, URL = {https://www.cs.dartmouth.edu/~kotz/research/rawassizadeh-datasets/index.html}, abstract = {The advent of affordable devices with sensors and communication capabilities has led to the proliferation of computing paradigms, such as the Internet of Things (IoT), mobile devices, and wearable technologies. For the sake of simplicity, we use the umbrella term ``small devices'' for these technologies. At the same time, in the past decade, the increasing availability of large datasets has shifted scientists' attention toward data science, and defined new trends in computation. Even some scientists call it an evolutionary shift that has changed the pace of scientific progress, i.e., the ``fourth paradigm''.}, } @Article{henderson:citation-practices, author = {Tristan Henderson and David Kotz}, title = {{Data citation practices in the CRAWDAD wireless network data archive}}, journal = {D-Lib Magazine}, year = 2015, month = {January}, volume = 21, number = {1/2}, numpages = 12, publisher = {Corporation for National Research Initiatives (CNRI)}, copyright = {the authors}, DOI = {10.1045/january2015-henderson}, URL = {https://www.cs.dartmouth.edu/~kotz/research/henderson-citation-practices/index.html}, abstract = {CRAWDAD (Community Resource for Archiving Wireless Data At Dartmouth) is a popular research data archive for wireless network data, archiving over 100 datasets used by over 6,500 users. In this paper we examine citation behaviour amongst 1,281 papers that use CRAWDAD datasets. We find that (in general) paper authors cite datasets in a manner that is sufficient for providing credit to dataset authors and also provides access to the datasets that were used. Only 11.5\% of papers did not do so; common problems included (1) citing the canonical papers rather than the dataset, (2) describing the dataset using unclear identifiers, and (3) not providing URLs or pointers to datasets.}, } @Misc{kotz:dartmouth-campus-20090909, author = {David Kotz and Tristan Henderson and Ilya Abyzov and Jihwang Yeo}, title = {{CRAWDAD dataset dartmouth/campus (v. 2009-09-09)}}, howpublished = {Available for download on IEEE DataPort}, year = 2009, month = {September}, copyright = {the authors}, DOI = {10.15783/C7F59T}, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-dartmouth-campus-20090909/index.html}, 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.}, } @Article{yeo:crawdad-2007, author = {Jihwang Yeo and David Kotz and Tristan Henderson}, title = {{Workshop report --- CRAWDAD Workshop 2007}}, journal = {ACM SIGCOMM Computer Communication Review}, year = 2008, month = {July}, volume = 38, number = 3, pages = {79--82}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/1384609.1384619}, URL = {https://www.cs.dartmouth.edu/~kotz/research/yeo-crawdad-2007/index.html}, abstract = {Wireless network researchers are hungry for data about how real users, applications, and devices use real networks under real network conditions. CRAWDAD, the Community Resource for Archiving Wireless Data at Dartmouth, is an NSF-funded project that is building a wireless network data archive for the research community. We host wireless data, and provide tools and documents to make it easy to collect and use wireless network data. We hope that this resource will help researchers to identify and evaluate real and interesting problems in mobile and pervasive computing. This report outlines the CRAWDAD project and summarizes the third CRAWDAD workshop, held at MobiCom 2007.}, } @Article{yeo:crawdad-mc2r, author = {Jihwang Yeo and Tristan Henderson and David Kotz}, title = {{Workshop report --- CRAWDAD Workshop 2006}}, journal = {ACM SIGMOBILE Mobile Computing and Communication Review}, year = 2007, month = {January}, volume = 11, number = 1, pages = {67--69}, publisher = {ACM}, copyright = {ACM}, URL = {https://www.cs.dartmouth.edu/~kotz/research/yeo-crawdad-mc2r/index.html}, abstract = {Wireless network researchers are seriously starved for data about how real users, applications, and devices use real networks under real network conditions. CRAWDAD, the Community Resource for Archiving Wireless Data at Dartmouth, is an NSF-funded project that is building a wireless network data archive for the research community. We host wireless data, and provide tools and documents to make it easy to collect and use wireless network data. We hope that this resource will help researchers to identify and evaluate real and interesting problems in mobile and pervasive computing. This report outlines the CRAWDAD project and summarizes the second CRAWDAD workshop, held at MobiCom 2006.}, } @Article{yeo:crawdad-ccr, author = {Jihwang Yeo and David Kotz and Tristan Henderson}, title = {{CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth}}, journal = {ACM SIGCOMM Computer Communication Review}, year = 2006, month = {April}, volume = 36, number = 2, pages = {21--22}, publisher = {ACM}, copyright = {ACM}, DOI = {10.1145/1129582.1129588}, URL = {https://www.cs.dartmouth.edu/~kotz/research/yeo-crawdad-ccr/index.html}, note = {Project overview}, abstract = {Wireless network researchers are seriously starved for data about how real users, applications, and devices use real networks under real network conditions. CRAWDAD, a Community Resource for Archiving Wireless Data at Dartmouth, is a new NSF-funded project to build a wireless network data archive for the research community. We host wireless data, and provide tools and documents to make it easy to collect and use wireless network data. We hope that this resource will help researchers identify and evaluate real and interesting problems in mobile and pervasive computing. This report outlines the CRAWDAD project, the kick-off workshop that was held at MobiCom 2005, and the latest news.}, } @InProceedings{blinn:hotspot, author = {David P. Blinn and Tristan Henderson and David Kotz}, title = {{Analysis of a Wi-Fi Hotspot Network}}, booktitle = {{Proceedings of the International Workshop on Wireless Traffic Measurements and Modeling (WiTMeMo)}}, year = 2005, month = {June}, pages = {1--6}, publisher = {USENIX Association}, copyright = {the authors}, URL = {https://www.cs.dartmouth.edu/~kotz/research/blinn-hotspot/index.html}, abstract = {Wireless hotspot networks have become increasingly popular in recent years as a means of providing Internet access in public areas such as restaurants and airports. In this paper we present the first study of such a hotspot network. We examine five weeks of SNMP traces from the Verizon Wi-Fi HotSpot network in Manhattan. We find that far more cards associated to the network than logged into it. Most clients used the network infrequently and visited few APs. AP utilization was uneven and the network displayed some unusual patterns in traffic load. Some characteristics were similar to those previously observed in studies of campus WLANs.}, } @Article{kotz:crawdad-workshop05, author = {David Kotz and Tristan Henderson}, title = {{CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth}}, journal = {IEEE Pervasive Computing}, year = 2005, month = {October}, volume = 4, number = 4, pages = {12--14}, publisher = {IEEE}, copyright = {IEEE}, DOI = {10.1109/MPRV.2005.75}, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-crawdad-workshop05/index.html}, abstract = {Wireless network researchers are seriously starved for data about how real users, applications, and devices use real networks under real network conditions. CRAWDAD (Community Resource for Archiving Wireless Data at Dartmouth) is a new National Science Foundation-funded project to build a wireless-network data archive for the research community. It will host wireless data and provide tools and documents to make collecting and using the data easy. This resource should help researchers identify and evaluate real and interesting problems in mobile and pervasive computing. To learn more about CRAWDAD and discuss its direction, about 30 interested people gathered at a workshop held in conjunction with MobiCom 2005.}, } @Misc{kotz:crawdad-sw, author = {David Kotz and Tristan Henderson and Chris McDonald}, title = {{CRAWDAD archive: a Community Resource for Archiving Wireless Data At Dartmouth}}, howpublished = {Web site}, year = 2005, copyright = {the authors}, URL = {https://www.cs.dartmouth.edu/~kotz/research/kotz-crawdad-sw/index.html}, abstract = {CRAWDAD is the Community Resource for Archiving Wireless Data At Dartmouth, a wireless network data resource for the research community. This archive has the capacity to store wireless trace data from many contributing locations, and staff to develop better tools for collecting, anonymizing, and analyzing the data.}, }