Abstract

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This paper defines and discusses the implementation of two novel extensions to the Siena Content-based Network (CBN) to extend it to become a Knowledge-based Network (KBN) thereby increasing the expressiveness and flexibility of its publications and subscription. One extension provides ontological concepts as an additional message attribute type, onto which subsumption relationships, equivalence, type queries and arbitrary ontological subscription filters can be applied. The second extension provides for a bag type to be used that allows bag equivalence, sub-bag and super-bag relationships to be used in subscription filters, possibly composed with any of the Siena subscription operators or the ontological operators previously mentioned. The performance of this KBN implementation has also been explored. However, to maintain scalability and performance it is important that these extensions do not break Siena's subscription aggregation algorithm. We also introduce the necessary covering relationships for the new types and operators and examine the subscription matching overhead resulting from these new types and operators.

This paper proposes an open, extensible control plane for a global event service, based on semantically rich messages. This is based on the novel application of control plane separation and semantic-based matching to Content-Based Networks. Here we evaluate the performance issues involved in attempting to perform ontology-based reasoning for content-based routing. This provides us with the motivation to explore peer-clustering techniques to achieve efficient aggregation of semantic queries. The clustering of super-peers using decentralized policy engineering will deliver the incremental deployment of new peer-clustering strategies.

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Abstract-The trends for pushing more operational intelligence towards network elements to achieve more context-aware and self-managing behavior often requires elements to gather network knowledge without necessarily binding explicitly to all of the potential sources of that knowledge. Though event-based publish-subscribe models allow efficient distribution of knowledge where the event types are known globally, dynamic service chains, ad hoc networks and pervasive computing application all introduce a more fluid and heterogeneous range of context knowledge. This requires some runtime translation of knowledge between sources and sinks of network context. This paper builds on existing mapping techniques that use ontological forms of existing management information models to examine the extent to which these can be employed for runtime semantic interoperability for network knowledge. It presents results in developing a management knowledge delivery framework based on existing models and platforms, but which offers a more decentralized knowledge exchange mechanism.

To increase the interoperability and accessibility of data in sensor-rich systems, there has been a recent proliferation of the use of Semantic Web technologies in sensor-rich systems. Quite a range of such applications have emerged, such as hazard monitoring and rescue, context-aware computing, environmental monitoring, field studies, internet of things, and so on. These systems often assume a centralized paradigm for data processing, which does not always hold in reality especially when the systems are deployed in a hostile environment. At runtime, the infrastructure of systems deployed in such an environment is also prone to interference or damage, causing part of the infrastructure to have limited network connection or even to be detached from the rest. A solution to such a problem would be to push the intelligence, such as semantic reasoning, down to the device layer. A key enabler for such a solution is to run semantic reasoning on resourceconstrained devices. This paper shows how reasoner composition (i.e. to automatically adjust a reasoning approach to preserve only a "well-suited" amount of reasoning for a given ontology) can achieve resource-efficient semantic reasoning. Two novel reasoner composition algorithms are introduced and implemented. Evaluation indicates that the reasoner composition algorithms greatly reduce the resources required for OWL reasoning, potentially facilitating greater semantic reasoning on sensor devices.

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