Simpleweb/University of Twente Traffic Traces Data Repository

The computer networks research community lacks of shared measurement information. As a consequence, most researchers need to expend a considerable part of their time planning and executing measurements before being able to perform their studies. The lack of shared data also makes it hard to compare and validate results. This report describes our efforts to distribute a portion of our network data through the Simpleweb/University of Twente Traffic Traces Data Repository

On the feasibility of monitoring DTN: Impacts of fine tuning on routing protocols and the user experience

The “machine to machine” communication paradigm will become a central element for mobile networks. This paradigm can be easily constructed by a contact-based network, notably a disruption/delay tolerant networks (DTN). To characterize a DTN, we can use the Inter-contact time among the nodes. The better understanding of inter-contact time (ICT) has practical applications on the tuning of forwarding strategies, and hence in the quality of the User Experience. Nevertheless, the fine tuning of those parameters is tight to a set of assumptions about the regularity of movement or periodicity of patterns in an usually non complete and cumbersome statistical analysis. That is why in a dynamic environment where we cannot assume any previous information the tuning of parameters is usually overestimated. In this work we study how monitoring can help to adapt those parameters to give a better understanding of both natural evolution of the network and non periodical events

Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization

Network researchers benefit tremendously from access to traces of production networks, and several repositories of such network traces exist. By their very nature, these traces capture sensitive business and personal activity. Furthermore, network traces contain significant operational information about the target network, such as its structure, identity of the network provider, or addresses of important servers. To protect private or proprietary information, researchers must “sanitize” a trace before sharing it. \par In this chapter, we survey the growing body of research that addresses the risks, methods, and evaluation of network trace sanitization. Research on the risks of network trace sanitization attempts to extract information from published network traces, while research on sanitization methods investigates approaches that may protect against such attacks. Although researchers have recently proposed both quantitative and qualitative methods to evaluate the effectiveness of sanitization methods, such work has several shortcomings, some of which we highlight in a discussion of open problems. Sanitizing a network trace, however challenging, remains an important method for advancing network–based research

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios