A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

Self-organizing peer-to-peer social networks

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors.Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules.National Natural Science of Foundation of Chin

A Secure and User Privacy-Preserving Searching Protocol for Peer-to-Peer Networks

File sharing peer-to-peer networks have become quite popular of late as a new paradigm for information exchange among large number of users in the Internet. However, these networks suffer from several problems such as fake content distribution, free riding, whitewashing, poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. In this paper, a secure and efficient searching scheme for peer-to-peer networks has been proposed that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures among the trustworthy peers, the scheme provides a highly reliable module for protecting the privacy of the users and data in the network. Simulation results have demonstrated that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times

Exploiting Geographical and Temporal Locality to Boost Search Efficiency in Peer-to-Peer Systems

As a hot research topic, many search algorithms have been presented and studied for unstructured peer-to-peer (P2P) systems during the past few years. Unfortunately, current approaches either cannot yield good lookup performance, or incur high search cost and system maintenance overhead. The poor search efficiency of these approaches may seriously limit the scalability of current unstructured P2P systems. In this paper, we propose to exploit two-dimensional locality to improve P2P system search efficiency. We present a locality-aware P2P system architecture called Foreseer, which explicitly exploits geographical locality and temporal locality by constructing a neighbor overlay and a friend overlay, respectively. Each peer in Foreseer maintains a small number of neighbors and friends along with their content filters used as distributed indices. By combining the advantages of distributed indices and the utilization of two-dimensional locality, our scheme significantly boosts P2P search efficiency while introducing only modest overhead. In addition, several alternative forwarding policies of Foreseer search algorithm are studied in depth on how to fully exploit the two-dimensional locality

Ad-hoc Limited Scale-Free Models for Unstructured Peer-to-Peer Networks

Several protocol efficiency metrics (e.g., scalability, search success rate, routing reachability and stability) depend on the capability of preserving structure even over the churn caused by the ad-hoc nodes joining or leaving the network. Preserving the structure becomes more prohibitive due to the distributed and potentially uncooperative nature of such networks, as in the peer-to-peer (P2P) networks. Thus, most practical solutions involve unstructured approaches while attempting to maintain the structure at various levels of protocol stack. The primary focus of this paper is to investigate construction and maintenance of scale-free topologies in a distributed manner without requiring global topology information at the time when nodes join or leave. We consider the uncooperative behavior of peers by limiting the number of neighbors to a pre-defined hard cutoff value (i.e., no peer is a major hub), and the ad-hoc behavior of peers by rewiring the neighbors of nodes leaving the network. We also investigate the effect of these hard cutoffs and rewiring of ad-hoc nodes on the P2P search efficiency.Comment: 10 pages, 6 figures, 43 references. Proceedings of The 8th IEEE International Conference on Peer-to-Peer Computing 2008 (IEEE P2P 2008), Aachen, German

Reaching Scalability in Unstructured P2P Networks Using a Divide and Conquer Strategy

Unstructured peer-to-peer networks have a low maintenance cost, high resilience and tolerance to the continuous arrival and departure of nodes. In these networks search is usually performed by flooding, which is highly inefficient. To improve scalability, unstructured overlays evolved to a two-tiered architecture where regular nodes rely on superpeers to locate resources. While this approach takes advantage of node heterogeneity, it makes the overlay less resilient to accidental and malicious faults, and less attractive to users concerned with the consumption of their resources. In this paper we propose a search algorithm, called FASE, which combines a replication policy and a search space division technique to achieve scalability on unstructured overlays with flat topologies. We present simulation results which validate FASE improved scalability and efficienc