Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As "Wi-Fi" technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks.This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including syslogs, telephone records, SNMP polling and tcpdump packet sniffing. This is the largest WLAN study to date, and the first to look at a large, mature WLAN and consider geographic mobility. We compare this trace to a trace taken after the network's initial deployment two years ago.We found that the applications used on the WLAN changed dramatically. Initial WLAN usage was dominated by Web traffic; our new trace shows significant increases in peer-to-peer, streaming multimedia, and voice over IP (VoIP) traffic. On-campus traffic now exceeds off-campus traffic, a reversal of the situation at the WLAN's initial deployment. Our study indicates that VoIP has been used little on the wireless network thus far, and most VoIP calls are made on the wired network. Most calls last less than a minute.We saw greater heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the "session diameter." We use this metric to show that embedded devices have different mobility characteristics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems.

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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

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Abstract-Opportunistic networks enable users to communicate in the absence of network infrastructure. But forwarding messages in such a network incurs costs for nodes in terms of energy and storage. This may lead to nodes being selfish and not forwarding messages for other nodes, resulting in degraded network performance. This paper presents a novel incentive mechanism for opportunistic networks that uses pre-existing social-network information to detect and punish selfish nodes, incentivising them to participate in the network. Trace-driven simulations demonstrate that our mechanism performs better than existing mechanisms, and that social-network information can also be used to improve existing incentive mechanisms.

Multiuser applications can be complex to develop due to their large or intricate nature. Many of the issues encountered are related to performance and security. These issues are exacerbated when the scale of the application increases. This paper introduces a novel distributed architecture called OpenGL|D (OpenGL Distributed). This technology enables an application to pass through the graphical calls between a Virtual Machine (VM) and the graphics processing unit (GPU) on the native host across a network. This ability allows applications to run inside a virtual machine (VM), whilst still benefiting from hardware accelerated performance from the GPU for the computationally intensive graphical processing. This allows for the development of 3D software requiring no dependencies on specific hardware or technology other than ANSI C and a network stack, demonstrating our approach to platform agnostic development and digital preservation.