HyCast-Podcast Discovery in Mobile Networks

Podcasts are a popular way to provide multimedia information about certain topics. A multitude of podcast servers exist in the Internet, allowing people to subscribe to them. Typically, podcasts are downloaded onto desktop computers and copied on mobile devices to be played while being on the move. In this paper, we extend the idea of podcasts, making them available in mobile network environments. In particular, HyCast does not rely on central podcast directories. Instead, HyCast also allows discovering, subscribing to, and downloading podcasts and episodes in the local neighborhood. For the dissemination of podcast information, we introduce and evaluate two different strategies. One is based on peer-to-peer communication between one-hop neighbors. The second one employs clustering to reduce the overhead of the podcast information dissemination

Towards a Geocentric Mobile Syndication System

International audienceThe present paper proposes a new approach towards filtering and processing the ever growing quantity of data published from mobile devices before it even reaches the Internet. We tackle this issue by circumscribing data to the zone where it is published (geocentricity) and allowing mobile device owners to republish the data they deem relevant (syndication). Results we obtained through simulation show that our solution enables to extract information that is relevant for a majority of users, whilst allowing less relevant data to be exchanged on a more local scale before it disappears entirely

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

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

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

Colocation aware content sharing in urban transport

People living in urban areas spend a considerable amount of time on public transport. During these periods, opportunities for inter-personal networking present themselves, as many of us now carry electronic devices equipped with Bluetooth or other wireless capabilities. Using these devices, individuals can share content (e.g., music, news or video clips) with fellow travellers that happen to be on the same train or bus. Transferring media takes time; in order to maximise the chances of successfully completing interesting downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this thesis, a peer-to-peer content distribution system for wireless devices is proposed, grounded on three main contributions: (1) a technique to predict colocation durations (2) a mechanism to exclude poorly performing peers and (3) a library advertisement protocol. The prediction scheme works on the observation that people have a high degree of regularity in their movements. Ensuring that content is accurately described and delivered is a challenge in open networks, requiring the use of a trust framework, to avoid devices that do not behave appropriately. Content advertising methodologies are investigated, showing their effect on whether popular material or niche tastes are disseminated. We first validate our assumptions on synthetic and real datasets, particularly movement traces that are comparable to urban environments. We then illustrate real world operation using measurements from mobile devices running our system in the proposed environment. Finally, we demonstrate experimentally on these traces that our content sharing system significantly improves data communication efficiency, and file availability compared to naive approaches

Enabling Censorship Tolerant Networking

Billions of people in the world live under heavy information censorship. We propose a new class of delay tolerant network (DTN), known as a censorship tolerant network (CTN), to counter the growing practice of Internet-based censorship. CTNs should provide strict guarantees on the privacy of both information shared within the network and the identities of network participants. CTN software needs to be publicly available as open source software and run on personal mobile devices with real-world computational, storage, and energy constraints. We show that these simple assumptions and system constraints have a non-obvious impact on the design and implementation of CTNs, and serve to differentiate our system design from previous work. We design data routing within a CTN using a new paradigm: one where nodes operate selfishly to maximize their own utility, make decisions based only on their own observations, and only communicate with nodes they trust. We introduce the Laissez-faire framework, an incentivized approach to CTN routing. Laissez-faire does not mandate any specific routing protocol, but requires that each node implement tit-for-tat by keeping track of the data exchanged with other trusted nodes. We propose several strategies for valuing and retrieving content within a CTN. We build a prototype BlackBerry implementation and conduct both controlled lab and field trials, and show how each strategy adapts to different network conditions. We further demonstrate that, unlike existing approaches to routing, Laissez-faire prevents free-riding. We build an efficient and reliable data transport protocol on top of the Short Message Service (SMS) to serve a control channel for the CTN. We conduct a series of experiments to characterise SMS behaviour under bursty, unconventional workloads. This study examines how variables such as the transmission order, delay between transmissions, the network interface used, and the time-of-day affect the service. We present the design and implementation of our transport protocol. We show that by adapting to the unique channel conditions of SMS we can reduce message overheads by as much as 50\% and increase data throughput by as much as 545% over the approach used by existing applications. A CTN's dependency on opportunistic communication imposes a significant burden on smartphone energy resources. We conduct a large-scale user study to measure the energy consumption characteristics of 20100 smartphone users. Our dataset is two orders of magnitude larger than any previous work. We use this dataset to build the Energy Emulation Toolkit (EET) that allows developers to evaluate the energy consumption requirements of their applications against real users' energy traces. The EET computes the successful execution rate of energy-intensive applications across all users, specific devices, and specific smartphone user-types. We also consider active adaptation to energy constraints. By classifying smartphone users based on their charging characteristics we demonstrate that energy level can be predicted within 72% accuracy a full day in advance, and through an Energy Management Oracle energy intensive applications, such as CTNs, can adapt their execution to maintain the operation of the host device