A peer-to-peer collaboration framework for multi-sensor data fusion

A peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In this high data volume real-time application, data from multiple radars are combined to improve the accuracy of radar scans (e.g., correct for attenuation) and to provide a composite view of the area covered by the radars. Data fusion process is subject to two constraints: (1) the accuracy requirement of the final fused results, which may be different at different end nodes, and (2) the real-time requirements of the application. The accuracy requirement is achieved by dynamically selecting the appropriate set of data to exchange among the multiple radar nodes. A mechanism for selecting a dataset based on current application-specific needs is presented. We also present a dynamic peer-selection algorithm, Best Peer Selection (BPS), that chooses a set of peers based on their computation and communication capabilities to minimize the data processing time per integration algorithm. Simulation-based results show that BPS can deliver a significant performance improvement, even when the peers have high variability in available network and computation resources.     

Trust-based privacy preservation for peer-to-peer data sharing

Privacy preservation in a peer-to-peer (P2P) system tries to hide the association between the identity of a participant and the data that it is interested in. This paper proposes a trust-based privacy-preservation method for P2P data sharing. It adopts the trust relation between a peer and its collaborators (buddies). The buddy works as a proxy to send the request and acquire the data. This provides a shield under which the identity of the requester and the accessed data cannot be linked. A privacy measuring method is presented to evaluate the proposed mechanism. Dynamic trust assessment and the enhancement to supplier's privacy are discussed.     

A data access framework for service-oriented rich clients

Facilitated by the SOA and new Web technologies, Service-Oriented Rich Clients (SORCs) compose various Web-delivered services in Web browser to create new applications. The SORCs support client-side data storage and manipulation and provide more features than traditional thin clients. However, the SORCs might suffer from data access issues, mainly due to both client-side incompatible data sources and server-side improper or even undesirable cache strategies. Addressing the data access issues, this paper proposes a data access framework for SORCs. The main contributions of this paper are as follows. First, the framework makes the SORCs accommodate heterogeneous local storage solutions and diverse Web browsers properly. The framework abstracts the underlying details of different local storages and selects the most proper data sources for current SORC in use. Secondly, the framework provides a cache mechanism, which supports client-side customized cache strategies. An adaptive technique for the strategies is also proposed to adjust cache strategies based on users?? historical actions to achieve better performance.     

A secure cloud-assisted urban data sharing framework for ubiquitous-cities

With the accelerated process of urbanization, more and more people tend to live in cities. In order to deal with the big data that are generated by citizens and public city departments, new information and communication technologies are utilized to process the urban data, which makes it more easier to manage. Cloud computing is a novel computation technology. After cloud computing was commercialized, there have been lot of cloud-based applications. Since the cloud service is provided by the third party, the cloud is semi-trusted. Due to the features of cloud computing, there are many security issues in cloud computing. Attribute-based encryption (ABE) is a promising cryptography technique which can be used in the cloud to solve many security issues. In this paper, we propose a framework for urban data sharing by exploiting the attribute-based cryptography. In order to fit the real world ubiquitous-cities utilization, we extend our scheme to support dynamic operations. In particular, from the part of performance analysis, it can be concluded that our scheme is secure and can resist possible attacks. Moreover, experimental results and comparisons show that our scheme is more efficient in terms of computation.     

Nearest neighbor queries with peer-to-peer data sharing in mobile environments

Nearest neighbor queries, such as determining the proximity of stationary objects (e.g., restaurants and gas stations) are an important class of inquiries for supporting location-based services. We present a novel approach to support nearest neighbor queries from mobile hosts by leveraging the sharing capabilities of wireless ad-hoc networks. We illustrate how previous query results cached in the local storage of neighboring mobile users can be leveraged to either fully or partially compute and verify nearest neighbor queries at a local host. The feasibility and appeal of our technique is illustrated through extensive simulation results that indicate a considerable reduction of the query load on the remote database. Furthermore, the scalability of our approach is excellent because a higher density of mobile hosts increases its effectiveness.     

A unifying framework of rating users and data items in peer-to-peer and social networks

We propose a unifying family of quadratic cost functions to be used in Peer-to-Peer ratings. We show that our approach is general since it captures many of the existing algorithms in the fields of visual layout, collaborative filtering and Peer-to-Peer rating, among them Koren spectral layout algorithm, Katz method, Spatial ranking, Personalized PageRank and Information Centrality. Besides of the theoretical interest in finding common basis of algorithms that where not linked before, we allow a single efficient implementation for computing those various rating methods. We introduce a distributed solver based on the Gaussian Belief Propagation algorithm which is able to efficiently and distributively compute a solution to any single cost function drawn from our family of quadratic cost functions. By implementing our algorithm once, and choosing the computed cost function dynamically on the run we allow a high flexibility in the selection of the rating method deployed in the Peer-to-Peer network. Using simulations over real social network topologies obtained from various sources, including the MSN Messenger social network, we demonstrate the applicability of our approach. We report simulation results using networks of millions of nodes.

A novel DRM framework for peer-to-peer music content delivery

Due to rapid advances in the network communications field in recent years, the distribution of large-scale music contents has become easier and more efficient than ever before. However, the unauthorized distribution of copyright-protected content has emerged as a major concern. Accordingly, this paper presents a content distribution framework with a DRM capability for P2P networks. The robustness of the content distribution is ensured by using a network coding approach based on the Lagrange polynomial interpolation method. When the downloading peer within the network receives sufficient coded pieces, it not only reconstructs the associated blocks using a finite field Gaussian elimination method, but also creates its own copies of the coded pieces within these blocks and shares these copies amongst the other peers in the network. As a result, the distribution overhead imposed on the music provider is substantially reduced and the number of coded pieces within the network is significantly increased, thereby overcoming the “last piece problem” inherent in existing P2P schemes. In the DRM module of the framework, the RSA public-key cryptosystem is used to generate a unique digital fingerprint for every user within the network. The fingerprint is embedded within the music file in a protected form such that the music provider can establish the identification of any user performing an unauthorized distribution of the file. The experimental results confirm that the proposed framework provides an efficient and secure means of distributing large-scale copyright-protected music contents with no discernible degradation in the audio quality.     

L-overlay: A layered data management scheme for peer-to-peer computing

Efficient storage and handling of data stored in a peer-to-peer (P2P) network, proves vital for various applications such as query processing and data mining. This paper presents a distributed, scalable and robust layered overlay (L-overlay) to index and manage multidimensional data in a dynamic P2P network. The proposed method distinguishes between the data and peer layers, with efficient mapping between the two. The data is organized such that semantically similar data objects are accessed hastily. Grid and tree structures are proposed for the peer layer. As application examples of L-overlay in query processing and data mining, k-nearest neighbors query processing and distributed Naïve Bayes classification algorithms, are proposed. We show the effectiveness of our scheme in static and dynamic environments using simulation. L-overlay is shown to be more efficient than SSW, an available semantic overly, in terms of maintenance and query processing costs.     

A peer-to-peer system for on-demand sharing of capacity across network applications

As a plethora of various distributed applications emerge, new computing platforms are necessary to support their extra and sometimes evolving requirements. This research derives its motive from deficiencies of real networked applications deployed on platforms unable to fully support their characteristics and proposes a network architecture to address that issue. Hoverlay is a system that enables logical movement of nodes from one network to another aiming to relieve requesting nodes, which experience high workload. Node migration and dynamic server overlay differentiate Hoverlay from Condor-based architectures, which exhibit more static links between managers and nodes. In this paper, we present a number of important extensions to the basic Hoverlay architecture, which collectively enhance the degree of control owners have over their nodes and the overall level of cooperation among servers. Furthermore, we carried out extensive simulations, which proved that Hoverlay outperforms Condor and Flock of Condors in both success rate and average successful query path length at a negligible increase in messages.     

语义P2P系统上的知识分享研究*

由于语义P2P系统的知识分散部署于各个peer节点上,而每个peer节点都拥有其自身异构的知识模式和本体表示,这就给语义P2P节点间的知识分享带来了困难。对语义P2P系统进行了深入分析,着重对P2P节点之间的知识分享问题进行了论述,并进一步提出自己的设计思路来解决这一问题。     

CrowdMed-II: a blockchain-based framework for efficient consent management in health data sharing

World Wide Web - The healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is...     

P2P文件共享系统中面向协商的激励机制

借鉴经济活动中的协商机制提出一种面向协商的P2 P激励机制。文件供求节点通过订立协议对双方的行为进行制约,新节点和信誉度较低的节点也可以通过订立协议参与文件交易,达到提高节点的文件交易成功率的目的。仿真结果表明,该机制有效抑制了搭便车行为,明显提高了网络文件资源的利用率。     

COE: A collaborative ontology editor based on a peer-to-peer framework

In this paper, we present COE, a peer-to-peer collaborative ontology editor, which supports the creation, editing, sharing, and reuse of ontologies, implemented on top of a general-purpose peer-to-peer framework, COPPEER. In large multi-disciplinary design projects, ontologies can be a critical success factor. Their collaborative creation leads to better understanding and increased information sharing between participants of a project, and economy of resources among projects. A peer-to-peer approach enables new modes of collaboration, while simplifying maintenance by propagating individual updates in shared models.     

Skyframe: a framework for skyline query processing in peer-to-peer systems

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161–172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661–672, 2005) to evaluate the effectiveness and scalability of Skyframe.     

Using peer-to-peer technology for knowledge sharing in communities of practices

Communities of Practices (CoPs) are informal structures within organizations that bind people together through informal relationships and the sharing of expertise and experience. As such, they are effective tools for the creation and sharing of organizational knowledge, and an increasing number of organizations are adopting them as part of their knowledge management strategies. In this paper, we examine the knowledge sharing characteristics and roles of CoPs and develop a peer-to-peer knowledge sharing architecture that matches the behavioral characteristics of the members of the CoPs. We also propose a peer-to-peer knowledge sharing tool called KTella that enables a community's members to voluntarily share and retrieve knowledge more effectively.     

A trusted decentralized access control framework for the client/server architecture

This paper proposes a trusted decentralized access control (TDAC) framework for the client/server architecture. As the fundamental principle, TDAC enforces access control policies at the client side and protects sensitive objects at the server side by leveraging trusted computing technologies. Compared with the previous work of Sandhu and Zhang (2005), TDAC uses fewer requirements for trusted components. To implement TDAC, we design a private trusted reference monitor that runs at the client side, evaluates an access control request, and signs a temporary access control credential for a client application trustworthily; we also design a master reference monitor that runs at the server side, evaluates the request from the client application only according to the temporary access control credential. As a typical application, TDAC can protect client's private context data in subject-context aware access control.     

CISS: An efficient object clustering framework for DHT-based peer-to-peer applications

In most DHT-based peer-to-peer systems, objects are totally declustered since such systems use a hash function to distribute objects evenly. However, such an object de-clustering can result in significant inefficiencies in advanced access operations such as multi-dimensional range queries, continuous updates, etc, which are common in many emerging peer-to-peer applications. In this paper, we propose CISS (Cooperative Information Sharing System), a framework that supports efficient object clustering for DHT-based peer-to-peer applications. CISS uses a Locality Preserving Function (LPF) instead of a hash function, thereby achieving a high level of clustering without requiring any changes to existing DHT implementations. To maximize the benefit of object clustering, CISS provides efficient routing protocols for multi-dimensional range queries and continuous updates. Furthermore, our cluster-preserving load balancing schemes distribute loads without hotspots while preserving the object clustering property. We demonstrate the performance benefits of CISS through extensive simulation.     

A fault tolerant peer-to-peer spatial data structure

In recent years, many layered indexing techniques over distributed hash table (DHT)-based peer-to-peer (P2P) systems have been proposed to realize distributed range search. In this paper, we present a fault tolerant constant degree dynamic Distributed Spatial Data Structure called DSDS that supports orthogonal range search on a set of N d-dimensional points published on n nodes. We describe a total order binary relation algorithm to publish points among supernodes and determine supernode keys. A non-redundant rainbow skip graph is used to coordinate message passing among nodes. The worst case orthogonal range search cost in a d-dimensional DSDS with n nodes is \(O\left (\log n+m+\frac {K}{B}\right )\) messages, where m is the number of nodes intersecting the query, K is the number of points reported in range, and B is the number of points that can fit in one message. A complete backup copy of data points stored in other nodes provides redundancy for our DSDS. This redundancy permits answering a range search query in the case of failure of a single node. For single node failure, the DSDS routing system can be recovered to a fully functional state at a cost of O(log n) messages. Backup sets in DSDS nodes are used to first process a query in the most efficient dimension, and then used to process a query containing the data in a failed node in d-dimensional space. The DSDS search algorithm can process queries in d-dimensional space and still tolerate failure of one node. Search cost in the worst case with a failed node increases to \(O\left (d\log n+dm+\frac {K}{B}\right )\) messages for d dimensions.     

A conceptual model for data management and distribution in peer-to-peer systems

While Peer-to-Peer (P2P) model gains significant attention in distributed computing, it is also expected to be a powerful model for information sharing. P2P systems are expected to provide exhaustive reliable computational resources and scalable accessibility. The data management and distribution in such systems requires storage, replication, data modeling, indexing, querying, retrieval, streaming, and topology management. While a lot of data management strategies have been proposed through the last years, these strategies have not been investigated with respect to a common model for P2P systems. However, since the services provided by the P2P systems are so diverse, it is very challenging to come up with a common layer-based model for all P2P systems. In this paper, we firstly propose a conceptual model for P2P systems, and then provide a classification and summary of data management and distribution strategies by referring to this model. The horizontal layers of the model correspond to modules of a P2P system whereas the columns are related to the services provided. The modules include base P2P service, storage, indexing, logical, service, and application modules. The services include security, querying, publish, join/leave, collaboration, and streaming. The paper concludes by providing a comprehensive list of data management and distribution strategies used in the existing P2P systems.