Multimedia systems applications have become major research interests in both computing and telecommunications industries. In some literature, multimedia is defined as "mant media" where media is derived from medium, and a medium is a means of transporting information. It is generally accepted that multimedia does enhance communication for individuals and among interacting groups of humans. Applications such as video conferencing, distance learning, and medical imaging, gain advantage in the use of multimedia applications. However, while it is recognised that future systems should provide multimedia functionality, many issues are being raised about how best to support multimedia communication. This is because multimedia requires new communications infrastructures to enable integration of various media types as well as to manipulate and control the individual media. There is also the need to support the spatial and temporal requirements of continuous media, and to support group based applications. Thus the requirements of multimedia applications are diverse; this thesis focuses on multimedia group management services. A number of architectures have been advanced on the management strategies for multimedia communications. A review of these architectures shows that they have been either application specific, or too restrictive for group based applications. This thesis presents an architecture for connection management for distributed multimedia group applications. The architecture is intended to encourage a uniform appearance to all group applications to assist in their collective management, whilst being sufficiently flexible to cope with all likely multicast mechanisms upon which such applications may be based. The concept of a media channel is introduced as the application-independent appearance of an instance of a single applications such as a video-on-demand service, or a video conferencing application. A media channel is the basic unit of management in a group user-agent and therefore is used as a basis for the management of multiple applications. The main aspects of the media channel model considered in the thesis have been substantially implemented and the performance accessed as suitable for a reasonable number of multimedia applications.

Data management in parallel computing systems is a broad and increasingly important research topic. As network speeds have surged, so too has the movement to transition storage and computation loads to wide-area network resources. The Grid, the Cloud, and Desktop Grids all represent different aspects of this movement towards highly-scalable, distributed, and utility computing. This dissertation contends that a peer-to-peer (P2P) networking paradigm is a natural match for data sharing within and between these heterogeneous network architectures. Peer-to-peer methods such as dynamic discovery, fault-tolerance, scalability, and ad-hoc security infrastructures provide excellent mappings for many of the requirements in today’s distributed computing environment. In recent years, volunteer Desktop Grids have seen a growth in data throughput as application areas expand and new problem sets emerge. These increasing data needs require storage networks that can scale to meet future demand while also facilitating expansion into new data-intensive research areas. Current practices are to mirror data from centralized locations, a technique that is not practical for growing data sets, dynamic projects, or data-intensive applications. The fusion of Desktop and Service Grids provides an ideal use-case to research peer-to-peer data distribution strategies in a hybrid environment. Desktop Grids have a data management gap, while integration with Service Grids raises new challenges with regard to cross-platform design. The work undertaken here is two-fold: first it explores how P2P techniques can be leveraged to meet the data management needs of Desktop Grids, and second, it shows how the same distribution paradigm can provide migration paths for Service Grid data. The result of this research is a Peer-to-Peer Architecture for Data-Intensive Cycle Sharing (ADICS) that is capable not only of distributing volunteer computing data, but also of providing a transitional platform and storage space for migrating Service Grid jobs to Desktop Grid environments.

In the 1970s and 1980s, a small group of American engineers recognised the importance of the graph-colouring ideas studied by mathematicians and the potential for these ideas to be used in practical radio frequency assignment procedures. Some groundbreaking work led to a long period of study in academia where many variants on the Frequency Assignment Problem have been considered and some advanced algorithms developed. This thesis has investigated the Frequency Assignment Problem for microwave fixed links and, taking account of the constraints experienced in professional practice, extended this to include the problem of Equipment Selection. For a particular data-rate, standard radio equipment using relatively lower-or higher order modulation schemes can be deployed by the fixed link operator. While the higher-order options use less bandwidth, they radiate at higher powers and require more protection in the radio interference environment. That is, they are more potent interferers and present a greater challenge to distant interferers. Therefore, when the assigner’s objective is to minimise the span of frequencies used by a network, the higher-order modulation radio is not always the most spectrally efficient. The thesis has hypothesised that by doubling the bandwidth requirement on selected links, the assigner can actually reduce the overall span of frequencies used to support a frequency assignment for the entire network. With a minimum span objective, fixed link deployment scenarios have been exposed to a standard IP Solver that gives exact solutions. Using graph-theoretic methods, equipment selection heuristics have been developed and tested in offline and online environments. This work has gathered significant evidence in support of the hypothesis.

The recent rise of services and networks that rely on human mobility has prompted the need for tools that detect our patterns of visits to locations and encounters with other individuals. The widespread popularity of location- and encounter-aware mobile phones has given us a wealth of empirical mobility data and enabled many novel applications that benefit from automated detection of an individual’s mobility patterns. This thesis explores the presence and character of periodic patterns in the visits and encounters of human individuals. Novel tools for extracting and analysing periodic mobility patterns are proposed and evaluated on real-world data. We investigate these patterns in a range of datasets, including visits to public transport stations on a metropolitan scale, university campus WLAN access point transitions, online location-sharing service checkins, and Bluetooth encounters among university students. The methods developed in this thesis are designed for decentralised implementation to enable their real-world deployment. Analysing an individual’s visit and encounter events is a challenging problem since the data are often highly sparse. In order to study visit patterns we propose a novel inter-event interval (IEI) analysis approach, which is inspired by neural coding techniques. The resulting measure, IEI-irregularity, quantifies the weekly periodic patterns of an individual’s visits to a location. To detect encounter patterns we propose and compare methods based on IEI analysis and periodic subgraph mining. In particular, we introduce the novel concept of a periodic encounter community; that is, a collection of individuals that share the same periodic encounter pattern. The decentralised algorithms we develop for periodic encounter community detection are of particular relevance to human-based opportunistic communication networks. We explore these communities in terms of their opportunistic content sharing performance. Our findings show that periodic patterns are a prominent feature of human mobility and that these patterns are algorithmically detectable.

Wireless communication between mobile users has become more popular than ever in the last decade, leading to increasing demand for network infrastructure. The growing popularity of smartphones among mobile users, leads an alternative infrastructure-less networking paradigm known as opportunistic networks. In opportunistic networks, mobile nodes such as smartphones use the mobility of devices in addition to wireless forwarding between intermediate nodes to facilitate communication without requiring a simultaneous path between source and destination. Without guaranteed connectivity, the strategy for data delivery is a key research challenge for such networks. In this research, we present the design and evaluation of the Repository-based Data Dissemination (RDD) system, a communication system which does not rely on cooperation from mobile nodes but instead employs a small number of well-placed standalone fixed devices (named repositories) to facilitate data dissemination. To find the optimal location for their repositories, RDD employs knowledge of the mobility characteristics of mobile users. To evaluate RDD, a new mobility model “Human mobility model” has been designed, which was able to closely mimic the users’ real mobility, and proven by conducting a series of experiments compared with real mobility traces. Using this model, the performance of the RDD is evaluated using custom simulation. In comparison with epidemic routing, the results show that RDD is able to drastically reduce resource consumption, expressed in terms of message redundancy, while preserving the performance in terms of data object delivery.

The thesis investigates how content produced by end-users on the World Wide Web — referred to as user-generated content — can enhance the news vertical aspect of a universal Web search engine, such that news-related queries can be satisfied more accurately, comprehensively and in a more timely manner. We propose a news search framework to describe the news vertical aspect of a universal web search engine. This framework is comprised of four components, each providing a different piece of functionality. The Top Events Identification component identifies the most important events that are happening at any given moment using discussion in user-generated content streams. The News Query Classification component classifies incoming queries as news-related or not in real-time. The Ranking News-Related Content component finds and ranks relevant content for news-related user queries from multiple streams of news and user-generated content. Finally, the News-Related Content Integration component merges the previously ranked content for the user query into theWeb search ranking. In this thesis, we argue that user-generated content can be leveraged in one or more of these components to better satisfy news-related user queries. Potential enhancements include the faster identification of news queries relating to breaking news events, more accurate classification of news-related queries, increased coverage of the events searched for by the user or increased freshness in the results returned. Approaches to tackle each of the four components of the news search framework are proposed, which aim to leverage user-generated content. Together, these approaches form the news vertical component of a universal Web search engine. Each approach proposed for a component is thoroughly evaluated using one or more datasets developed for that component. Conclusions are derived concerning whether the use of user-generated content enhances the component in question using an appropriate measure, namely: effectiveness when ranking events by their current importance/newsworthiness for the Top Events Identification component; classification accuracy over different types of query for the News Query Classification component; relevance of the documents returned for the Ranking News-Related Content component; and end-user preference for rankings integrating user-generated content in comparison to the unalteredWeb search ranking for the News-Related Content Integration component. Analysis of the proposed approaches themselves, the effective settings for the deployment of those approaches and insights into their behaviour are also discussed. In particular, the evaluation of the Top Events Identification component examines how effectively events — represented by newswire articles — can be ranked by their importance using two different streams of user-generated content, namely blog posts and Twitter tweets. Evaluation of the proposed approaches for this component indicates that blog posts are an effective source of evidence to use when ranking events and that these approaches achieve state-of-the-art effectiveness. Using the same approaches instead driven by a stream of tweets, provide a story ranking performance that is significantly more effective than random, but is not consistent across all of the datasets and approaches tested. Insights are provided into the reasons for this with regard to the transient nature of discussion in Twitter. Through the evaluation of the News Query Classification component, we show that the use of timely features extracted from different news and user-generated content sources can increase the accuracy of news query classification over relying upon newswire provider streams alone. Evidence also suggests that the usefulness of the user-generated content sources varies as news events mature, with some sources becoming more influential over time as new content is published, leading to an upward trend in classification accuracy. The Ranking News-Related Content component evaluation investigates how to effectively rank content from the blogosphere and Twitter for news-related user queries. Of the approaches tested, we show that learning to rank approaches using features specific to blog posts/tweets lead to state-of-the-art ranking effectiveness under real-time constraints. Finally this thesis demonstrates that the majority of end-users prefer rankings integrated with usergenerated content for news-related queries to rankings containing only Web search results or integrated with only newswire articles. Of the user-generated content sources tested, the most popular source is shown to be Twitter, particularly for queries relating to breaking events. The central contributions of this thesis are the introduction of a news search framework, the approaches to tackle each of the four components of the framework that integrate user-generated content and their subsequent evaluation in a simulated real-time setting. This thesis draws insights from a broad range of experiments spanning the entire search process for news-related queries. The experiments reported in this thesis demonstrate the potential and scope for enhancements that can be brought about by the leverage of user-generated content for real-time news search and related applications.

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The deregulation of the telecommunications sector, and the resulting multi-operator competitive marketplace, has reduced the potential profitability of simple voice traffic. To return to the high dividend levels to which shareholders have become accustomed, operators are forced to turn to other sources of income. Future profitability is likely to come from content delivery and associated value added services, the mergers between various Internet Service Providers (ISPs) and content-creators being championed as the beginning of a new trend within these industries. Faced with a competitive market, network operators are increasingly offering value-added services to differentiate themselves from the competition. At present this is taking the form of concentrating on specialist markets, for example the provision of Virtual Private Networks (VPNs) or multipeer services. This thesis explores the technology necessary to program the network to provide specialist network layer services to meet the distinct customer requirements. This begins with a critical examination of existing service provisioning approaches from both the “open-signalling” and “active networking” domains. The Alpine framework, A Lightweight Programmable Internet Environment, is presented and discussed. This is a model for providing network programmability to third parties, combining programmable and active network technolody. Current emerging industry trends and standardisation are drawn upon the framework. The Alpine framework is compatible with the programmable interface standardisation activity in the IEEE P.1521.3 IP sub-working group. The resulting implementation of the “Active Router” component can this be viewed as an early interpretation of IEEE P.1520 within a novel active networking context. Finally, an evaluation of the architecture is presented, based around an analysis of the mechanisms for service deployment. Central to the architecture has been the adoption of distributed middleware technologies, the performance of which is measured and discussed with respect to applicability in the active networking domain. The architecture and router design fundamentals are qualified through comparison with a state-of-the-art family of commercial routers.

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