CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth

Abstract

Researchers who work with wireless networks or mobile computing are seriously starved for data. Data captured from live wireless networks would help us all understand how real users, applications, and devices use real networks under real conditions, and how mobile users actually move about. This data helps us to identify and understand the real problems, to evaluate possible solutions, and to evaluate new applications and services.On the other hand, most research today is based on analytical or simulation models. These models are severely limited by the complexity of real-world radio propagation and the lack of understanding about behavior of wireless applications and users. Experimental studies, however, are extremely difficult to set up. To collect data about real users on real networks requires a considerable amount of equipment, specialized software for collecting and anonymizing data, organizational permission and assistance to collect data, and human-subjects research clearance from the appropriate institutional review board (IRB).At Dartmouth College we are fortunate. We have a campuswide wireless infrastructure, with comprehensive data-collection mechanisms to gather traces of wireless users and their behavior. We have developed an extensive toolset for collecting, anonymizing, and analyzing the trace data. We have a cooperative network-management organization, and experience with the IRB process. We have a history of sharing our (anonymized) data with the research community. Several other research groups from around the world, in both academia and industry, have used our data. In our experience, the need for this sort of data is great.To meet this need, the US National Science Foundation is funding an effort to turn this Dartmouth resource into a true community resource: an archive with the capacity to store wireless trace data from many contributing locations, with the staff to develop better tools to handle the data. The resulting CRAWDAD project will work with • community leaders to ensure that the archive meets the research community's needs, • the other leading centers that develop network tracing tools and metadata, and • research organizations and corporations to ensure continuing support for the archive after NSF funding ends. THE WORKSHOPThe CRAWDAD workshop consisted of an invited talk by Ravi Jain, of DoCoMo Labs USA, and then group discussion. The importance of measurementJain gave an inspiring and educational talk about the importance of measurement in our field. He sees the mobility and networking research communities beginning to mature, as evidenced by the increased interplay between theoretical and experimental

Noncooperative 802.11 MAC Layer Fingerprinting and Tracking of Mobile Devices

“…We shall henceforth refer to these experiments as, respectively, the fingerprinting and transmission rate experiments. The used code and anonymized data sets have been made available publicly at https://github.com/rpp0/wifi-mac-tracking, http://crawdad.org [44], and https://wicability .net [45].…”

Characteristics comparison of DTN networks routing protocols using hybrid model of nodes’ mobility

“…Research provided by comparing the target characteristics of the routing protocols: the PDF of packet's delivery delay or time of live of packet ( ) and probability of delivery ( ). This report uses the traces dataset from the territory of KAIST from collection [13]. All traces were collected in the same way: a number of volunteers (university students) wore GPS receivers in their pocket during the day and these receivers record their position every 30 seconds.…”

“…For better adequacy of the hybrid model, both the locations and durations of the daily nodes' routes are taken from the real data from [13]. In particular, we used the traces dataset from the territory of KAIST.…”

A link prediction approach based on deep learning for opportunistic sensor network

“…The reference information of the two experimental data sets is shown in Table 1 (in CRAWDAD, 20 there are more details about INFOCOM05 and MIT data sets).…”