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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects: TK5105.5
I designed, implemented and evaluated a decentralised context-aware content distribution middleware. It can support a variety of applications, with all network communication handled transparently behind a tuple space based interface. Content is inserted into the network with an associated condition stipulating the context that must be matched to receive it. Conditions can be expressed using conjunctions, disjunctions, a form of universal and existential quantification and nested block scopes. Conditions are mapped onto a set of spatial indexes to enable lookup; and these are inserted into a distributed multi-dimensional spatial data structure (e.g. an R-Tree). They are also translated into an OWL representation to enable evaluation. \ud \ud Nodes bind to their most geographically proximate neighbours which allows distance-sensitive context sharing. The middleware is capability-aware, pushing computationally expensive tasks onto more capable nodes.\ud \ud I evaluated my system through benchmarks and simulation, defining condition classes which collectively represent a large portion of the condition space. Random conditions were generated from these classes. Node mobility was\ud controlled through a number of probability distributions. Benchmark evaluation times were reasonable, evaluating 500 typical messages in 1.4 seconds each. When the number of stored contexts were reduced, this improved dramatically, evaluating 500 much more complicated conditions in one-tenth of a second each. The number and complexity of context parameters has a major impact on efficiency.\ud \ud The number of spatial indexes generated was reasonable for most conditions, with a 95th percentile of 6. However, existential quantification was a challenge for both condition evaluation and index generation due to the\ud potentially large number of possible combinations of conditions.\ud \ud As expected, simulations found that the distribution of workload was very uneven because nodes tend to cluster in large cities; meaning that most communication is localised within these areas. Also, node density had a dramatic impact on the number of received messages as nodes within sparse areas were unable to obtain context information which precluded condition evaluation.\ud \ud I achieved my research goals of developing a distributed context-aware content distribution framework.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1 Introduction 1 1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Published Works . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Thesis Organisation . . . . . . . . . . . . . . . . . . . . . . . . 11
    • 2 Decentralised Protocols 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Publish-Subscribe Networks . . . . . . . . . . . . . . . . . . . . 13 2.3 Tuple Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.4 Hash Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.5 Spatial Indexes . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
    • 3 Context-Aware Middleware 29 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Context-Aware Frameworks . . . . . . . . . . . . . . . . . . . . 30 3.3 Categories of Context-Awareness . . . . . . . . . . . . . . . . . 31 3.4 Aspects of Context . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Representing Context . . . . . . . . . . . . . . . . . . . . . . . 33 3.6 Context Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Location Models and Services . . . . . . . . . . . . . . . . . . . 37 3.8 Criteria for Evaluating Existing Frameworks . . . . . . . . . . . 41 3.9 Evaluating Frameworks . . . . . . . . . . . . . . . . . . . . . . 42 3.10 A Context-Aware Middleware Taxonomy . . . . . . . . . . . . 46 3.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
    • 4 Context-Aware Content Distribution 48 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.2 The MediateSpace Language . . . . . . . . . . . . . . . . . . . 48 4.3 The MediateSpace Network . . . . . . . . . . . . . . . . . . . . 52 4.4 Pervasive Advertising . . . . . . . . . . . . . . . . . . . . . . . 64 4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
    • 5 A Context-Based Spatial Lookup Algorithm 71 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.2 Value Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.3 Structure Mapping . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
    • 6 Context Reasoning Using OWL 88 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 6.2 The OWL Language . . . . . . . . . . . . . . . . . . . . . . . . 88 6.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.4 MediateSpace OWL Ontology . . . . . . . . . . . . . . . . . . . 92 6.5 MediateSpace Evaluation Ontology . . . . . . . . . . . . . . . . 93 6.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
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