2,644 research outputs found

    Semi-probabilistic Routing for Highly Dynamic Networks

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    Abstract. In this paper we describe a semi-probabilistic routing approach designed to enable content-based publish-subscribe on highly dynamic networks, e.g., mobile, peer-to-peer, or wireless sensor networks. We present the rationale and high level strategy of our approach, and then show its application in a link-based graph overlay as well as in a broadcast-based sensor network. Simulation results confirm that, in both scenarios, our semi-probabilistic approach strikes a balance between entirely deterministic and entirely probabilistic solutions, achieving high reliability with low overhead

    Approximative filtering of XML documents in a publish/subscribe system

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    Publish/subscribe systems filter published documents and inform their subscribers about documents matching their interests. Recent systems have focussed on documents or messages sent in XML format. Subscribers have to be familiar with the underlying XML format to create meaningful subscriptions. A service might support several providers with slightly differing formats, e.g., several publishers of books. This makes the definition of a successful subscription almost impossible. This paper proposes the use of an approximative language for subscriptions. We introduce the design of our ApproXFilter algorithm for approximative filtering in a publish/subscribe system. We present the results of our performance analysis of a prototypical implementation

    Efficient Probabilistic Subsumption Checking for Content-Based Publish/Subscribe Systems

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    Abstract. Efficient subsumption checking, deciding whether a subscription or publication is covered by a set of previously defined subscriptions, is of paramount importance for publish/subscribe systems. It provides the core system functionality—matching of publications to subscriber needs expressed as subscriptions—and additionally, reduces the overall system load and generated traffic since the covered subscriptions are not propagated in distributed environments. As the subsumption problem was shown previously to be co-NP complete and existing solutions typically apply pairwise comparisons to detect the subsumption relationship, we propose a ‘Monte Carlo type ’ probabilistic algorithm for the general subsumption problem. It determines whether a publication/subscription is covered by a disjunction of subscriptions in O(k md), wherek is the number of subscriptions, m is the number of distinct attributes in subscriptions, and d is the number of tests performed to answer a subsumption question. The probability of error is problem-specific and typically very small, and sets an upper bound on d. Our experimental results show significant gains in term of subscription set reduction which has favorable impact on the overall system performance as it reduces the total computational costs and networking traffic. Furthermore, the expected theoretical bounds underestimate algorithm performance because it performs much better in practice due to introduced optimizations, and is adequate for fast forwarding of subscriptions in case of high subscription rate.

    Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models

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    Epidemics-inspired techniques have received huge attention in recent years from the distributed systems and networking communities. These algorithms and protocols rely on probabilistic message replication and redundancy to ensure reliable communication. Moreover, they have been successfully exploited to support group communication in distributed systems, broadcasting, multicasting and information dissemination in fixed and mobile networks. However, in most of the existing work, the probability of infection is determined heuristically, without relying on any analytical model. This often leads to unnecessarily high transmission overheads. In this paper we show that models of epidemic spreading in complex networks can be applied to the problem of tuning and controlling the dissemination of information in wireless ad hoc networks composed of devices carried by individuals, i.e., human-based networks. The novelty of our idea resides in the evaluation and exploitation of the structure of the underlying human network for the automatic tuning of the dissemination process in order to improve the protocol performance. We evaluate the results using synthetic mobility models and real human contacts traces

    On Data Dissemination for Large-Scale Complex Critical Infrastructures

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    Middleware plays a key role for the achievement of the mission of future largescalecomplexcriticalinfrastructures, envisioned as federations of several heterogeneous systems over Internet. However, available approaches for datadissemination result still inadequate, since they are unable to scale and to jointly assure given QoS properties. In addition, the best-effort delivery strategy of Internet and the occurrence of node failures further exacerbate the correct and timely delivery of data, if the middleware is not equipped with means for tolerating such failures. This paper presents a peer-to-peer approach for resilient and scalable datadissemination over large-scalecomplexcriticalinfrastructures. The approach is based on the adoption of epidemic dissemination algorithms between peer groups, combined with the semi-active replication of group leaders to tolerate failures and assure the resilient delivery of data, despite the increasing scale and heterogeneity of the federated system. The effectiveness of the approach is shown by means of extensive simulation experiments, based on Stochastic Activity Networks

    Semantic-Based Publish/Subscribe System in Social Network

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    The publish/subscribe model has become a prevalent paradigm for building distributed notification services by decoupling the publishers and the subscribers from each other. The semantics-based publish/subscribe system allows highly expressive descriptions of subscriptions and publications and thus is more appropriate for content dissemination when a finer level of granularity is necessary. In this paper we have designed and implemented a semantic-based publish/subscribe system that can be adapted into social networks where thousands of people can share their common interests through publications and subscriptions. We have described our ontology, defined publishers’ and subscribers’ data semantics or schema, provided a matching algorithm, portrayed the implementation and shown the result of an implemented publish/subscribe system that allows the users to publish and subscribe different kinds of news in a social network platform. Our experience shows that the semantic-based publish/subscribe system can enhance the current social networks by providing an effective content dissemination mechanism

    Efficient event delivery in publish/subscribe systems for wireless mesh networks

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    Internet and Mobile Computing Lab, Department of ComputingRefereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
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