Adaptive service discovery on service-oriented and spontaneous sensor systems

Service-oriented architecture, Spontaneous networks, Self-organisation, Self-configuration, Sensor systems, Social patternsNatural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with service-oriented sensor systems. However, the benefits of integration of services will not be realised unless we have a dependable method to discover all required services in dynamic environments. In this paper, we propose an Adaptive and Efficient Peer-to-peer Search (AEPS) approach for dependable service integration on service-oriented architecture based on a number of social behaviour patterns. In the AEPS network, the networked nodes can autonomously support and co-operate with each other in a peer-to-peer (P2P) manner to quickly discover and self-configure any services available on the disaster area and deliver a real-time capability by self-organising themselves in spontaneous groups to provide higher flexibility and adaptability for disaster monitoring and relief

Peer to Peer Information Retrieval: An Overview

Peer-to-peer technology is widely used for file sharing. In the past decade a number of prototype peer-to-peer information retrieval systems have been developed. Unfortunately, none of these have seen widespread real- world adoption and thus, in contrast with file sharing, information retrieval is still dominated by centralised solutions. In this paper we provide an overview of the key challenges for peer-to-peer information retrieval and the work done so far. We want to stimulate and inspire further research to overcome these challenges. This will open the door to the development and large-scale deployment of real-world peer-to-peer information retrieval systems that rival existing centralised client-server solutions in terms of scalability, performance, user satisfaction and freedom

A schema-based P2P network to enable publish-subscribe for multimedia content in open hypermedia systems

Open Hypermedia Systems (OHS) aim to provide efficient dissemination, adaptation and integration of hyperlinked multimedia resources. Content available in Peer-to-Peer (P2P) networks could add significant value to OHS provided that challenges for efficient discovery and prompt delivery of rich and up-to-date content are successfully addressed. This paper proposes an architecture that enables the operation of OHS over a P2P overlay network of OHS servers based on semantic annotation of (a) peer OHS servers and of (b) multimedia resources that can be obtained through the link services of the OHS. The architecture provides efficient resource discovery. Semantic query-based subscriptions over this P2P network can enable access to up-to-date content, while caching at certain peers enables prompt delivery of multimedia content. Advanced query resolution techniques are employed to match different parts of subscription queries (subqueries). These subscriptions can be shared among different interested peers, thus increasing the efficiency of multimedia content dissemination

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions

By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re- identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results