Reputation-based content dissemination for user generated wireless podcasting

User-generated podcasting service over human-centric opportunistic network can facilitate user-generated content sharing while humans are on the move beyond the coverage of infrastructure networks. We focus on the aspects of designing efficient forwarding and cache replacement schemes of such service under the constraints of limited capability of handheld device and limited network capacity. In particular, the design of those schemes is challenged by the lack of podcast channel popularity information at each node which is crucial for forwarding and caching decisions. We design a distributed reputation system based on modified Bayesian framework that enable each node estimates the channel popularity in a efficient way. It estimates channel popularity by not only first hand observations but also second hand observations from other nodes. Our simulation result shows reputation system can always well estimate most popular, intermediate and low popular channels, compare to history-based rank scheme which can only well estimate a few most popular channels. Reputation system significantly outperforms history-based rank when the public cache size is small or "a" parameter of Zipf-like distribution is small

MobiTrade: Trading Content in Disruption Tolerant Networks

International audienceThe rapid proliferation of advanced mobile devices has created a growing demand for data content. Existing approaches (e.g. relying on cellular infrastructures) cannot keep up with the large volume of content generated and requested, without the deployment of new expensive infrastructure. Exchanging content of interest opportunistically, when two nodes are in range, presents a low cost and high bandwidth alternative for popular, bulky content. Yet, efficiently collecting, storing, and sharing the content while preventing selfish users from impairing collaborative ones, poses major challenges. In this paper, we present MobiTrade, a collaborative content dissemination system on top of a delay tolerant network. It allows users to head out in the real world, express locally their interests, and wait to get notified whenever an encountered device has content(s) matching these interests. Even though interactions are done between neighboring wireless devices (locally), MobiTrade implements a trading scheme that motivates mobile devices to act as merchants and carry content across the network to satisfy each other's interests. Users continuously profile the type of content requested and the collaboration level of encountered devices. Based on this knowledge, an appropriate utility function is used to rank these requests and collect an optimal inventory of data that maximizes the expected value of stored content for future encounters. Using NS3 simulations based on synthetic and real mobility traces, we show that MobiTrade achieves up to 2 times higher query success rates compared to other content dissemination schemes. Furthermore, we show that MobiTrade successfully isolates selfish, non-collaborative devices. Finally, using a simple game theoretic framework we show that turning on our MobiTrade mechanism is an efficient Nash Equilibrium

The Blended Learning Unit, University of Hertfordshire: A Centre for Excellence in Teaching and Learning, Evaluation Report for HEFCE

The University of Hertfordshire’s Blended Learning Unit (BLU) was one of the 74 Centres for Excellence in Teaching and Learning (CETLs) funded by the Higher Education Funding Council for England (HEFCE) between 2005 and 2010. This evaluation report follows HEFCE’s template. The first section provides statistical information about the BLU’s activity. The second section is an evaluative reflection responding to 13 questions. As well as articulating some of our achievements and the challenges we have faced, it also sets out how the BLU’s activity will continue and make a significant contribution to delivery of the University of Hertfordshire’s 2010-2015 strategic plan and its aspirations for a more sustainable future. At the University of Hertfordshire, we view Blended Learning as the use of Information and Communication Technology (ICT) to enhance the learning and learning experience of campus-based students. The University has an excellent learning technology infrastructure that includes its VLE, StudyNet. StudyNet gives students access to a range of tools, resources and support 24/7 from anywhere in the world and its robustness, flexibility and ease of use have been fundamental to the success of the Blended Learning agenda at Hertfordshire. The BLU has comprised a management team, expert teachers seconded from around the University, professional support and a Student Consultant. The secondment staffing model was essential to the success of the BLU. As well as enabling the BLU to become fully staffed within the first five months of the CETL initiative, it has facilitated access to an invaluable spectrum of Blended Learning, research and Change Management expertise to inform pedagogically sound developments and enable change to be embedded across the institution. The BLU used much of its capital funding to reduce barriers to the use of technology by, for example, providing laptop computers for all academic staff in the institution, enhancing classroom technology provision and wirelessly enabling all teaching accommodation. Its recurrent funding has supported development opportunities for its own staff and staff around the institution; supported evaluation activities relating to individual projects and of the BLU’s own impact; and supported a wide range of communication and dissemination activities internally and externally. The BLU has led the embedding a cultural change in relation to Blended Learning at the University of Hertfordshire and its impact will be sustained. The BLU has produced a rich legacy of resources for our own staff and for others in the sector. The University’s increased capacity in Blended Learning benefits all our students and provides a learning experience that is expected by the new generation of learners in the 21st century. The BLU’s staffing model and partnership ways of working have directly informed the structure and modus operandi of the University’s Learning and Teaching Institute (LTI). Indeed a BLU team will continue to operate within the LTI and help drive and support the implementation of the University’s 2010-2015 Strategic plan. The plan includes ambitions in relation to Distance Learning and Flexible learning and BLU will be working to enable greater engagement with students with less or no need to travel to the university. As well as opening new markets within the UK and overseas, even greater flexibility for students will also enable the University to reduce its carbon footprint and provide a multifaceted contribution to our sustainability agenda. We conclude this executive summary with a short paragraph, written by Eeva Leinonen, our former Deputy Vice-Chancellor, which reflects our aspiration to transform Learning and Teaching at the University of Hertfordshire and more widely in the sector. ‘As Deputy Vice Chancellor at Hertfordshire I had the privilege to experience closely the excellent work of the Blended Learning Unit, and was very proud of the enormous impact the CETL had not only across the University but also nationally and internationally. However, perhaps true impact is hard to judge at such close range, but now as Vice Principal (Education) at King's College London, I can unequivocally say that Hertfordshire is indeed considered as the leading Blended Learning university in the sector. My new colleagues at King's and other Russell Group Universities frequently seek my views on the 'Hertfordshire Blended Learning' experience and are keen to emulate the successes achieved at an institutional wide scale. The Hertfordshire CETL undoubtedly achieved not only what it set out to achieve, but much more in terms of scale and impact. All those involved in this success can be justifiably proud of their achievements.’ Professor Eeva Leinonen, Vice Principal (Education), King's College, Londo

Reputation aware obfuscation for mobile opportunistic networks

© 2013 IEEE. Current anonymity techniques for mobile opportunistic networks typically use obfuscation algorithms to hide node's identity behind other nodes. These algorithms are not well suited to sparse and disconnection prone networks with large number of malicious nodes and new opportunistic, adaptive. So, new, opportunistic, adaptive fully localized mechanisms are needed for improving user anonymity. This paper proposes reputation aware localized adaptive obfuscation for mobile opportunistic networks that comprises of two complementary techniques: opportunistic collaborative testing of nodes' obfuscation behaviour (OCOT) and multidimensional adaptive anonymisation (AA). OCOT-AA is driven by both explicit and implicit reputation building, complex graph connectivity analytics and obfuscation history analyses. We show that OCOT-AA is very efficient in terms of achieving high levels of node identity obfuscation and managing low delays for answering queries between sources and destinations while enabling fast detection and avoidance of malicious nodes typically within the fraction of time within the experiment duration. We perform extensive experiments to compare OCOT-AA with several other competitive and benchmark protocols and show that it outperforms them across a range of metrics over a one month real-life GPS trace. To demonstrate our proposal more clearly, we propose new metrics that include best effort biggest length and diversity of the obfuscation paths, the actual percentage of truly anonymised sources' IDs at the destinations and communication quality of service between source and destination

Emerging technologies for learning (volume 1)

Collection of 5 articles on emerging technologies and trend

An Analysis of Data Quality Defects in Podcasting Systems

Podcasting has emerged as an asynchronous delay-tolerant method for the distribution of multimedia files through a network. Although podcasting has become a popular Internet application, users encounter frequent information quality problems in podcasting systems. To better understand the severity of these quality problems, we have applied the Total Data Quality Management methodology to podcasting. Through the application of this methodology we have quantified the data quality problems inherent within podcasting metadata, and performed an analysis that maps specific metadata defects to failures in popular commercial podcasting platforms. Furthermore, we extracted the Really Simple Syndication (RSS) feeds from the iTunes catalog for the purpose of performing the most comprehensive measurement of podcasting metadata to date. From these findings we attempted to improve the quality of podcasting data through the creation of a metadata validation tool - PodCop. PodCop extends existing RSS validation tools and encapsulates validation rules specific to the context of podcasting. We believe PodCop is the first attempt at improving the overall health of the podcasting ecosyste