Energy Efficient Rectangular Indexing for Mobile Peer-to-Peer Environment

Now a days in wireless environment there are many challenges. One of them which is need to be addressed in mobile Peer-to-Peer environment is getting the information of interest quickly and efficiently. Wherein whenever the node tries to get the desired data it has to wait too long or have to contact to unnecessary nodes which are not having their data of interest. This causes the node to waste the limited power resources and incurs more cost in terms of energy wastage. Here we proposed an energy efficient rectangular indexing called PMBR (Peer-to-Peer Minimum Bounding Rectangle) which allows the user to get the information of interest in energy efficient manner. We proposed algorithms namely PMBR_DSS, PMBR_HB and PMBR_CP and processed Nearest Neighbor & Range type queries. The experimental results carried out shows that the proposed algorithm PMBR_CP provides the efficient, quick and assured access to information of interest by saving the scarce power resources

Profiling core-periphery network structure by random walkers

Disclosing the main features of the structure of a network is crucial to understand a number of static and dynamic properties, such as robustness to failures, spreading dynamics, or collective behaviours. Among the possible characterizations, the core-periphery paradigm models the network as the union of a dense core with a sparsely connected periphery, highlighting the role of each node on the basis of its topological position. Here we show that the core-periphery structure can effectively be profiled by elaborating the behaviour of a random walker. A curve—the core-periphery profile—and a numerical indicator are derived, providing a global topological portrait. Simultaneously, a coreness value is attributed to each node, qualifying its position and role. The application to social, technological, economical, and biological networks reveals the power of this technique in disclosing the overall network structure and the peculiar role of some specific nodes

Efficient and decentralized pagerank approximation in a peer-to-peer web search network

PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user’s thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation. This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation. 1

Decentralized Web Search

Centrally controlled search engines will not be sufficient and reliable for indexing and searching the rapidly growing World Wide Web in near future. A better solution is to enable the Web to index itself in a decentralized manner. Existing distributed approaches for ranking search results do not provide flexible searching, complete results and ranking with high accuracy. This thesis presents a decentralized Web search mechanism, named DEWS, which enables existing webservers to collaborate with each other to form a distributed index of the Web. DEWS can rank the search results based on query keyword relevance and relative importance of websites in a distributed manner preserving a hyperlink overlay on top of a structured P2P overlay. It also supports approximate matching of query keywords using phonetic codes and n-grams along with list decoding of a linear covering code. DEWS supports incremental retrieval of search results in a decentralized manner which reduces network bandwidth required for query resolution. It uses an efficient routing mechanism extending the Plexus routing protocol with a message aggregation technique. DEWS maintains replica of indexes, which reduces routing hops and makes DEWS robust to webservers failure. The standard LETOR 3.0 dataset was used to validate the DEWS protocol. Simulation results show that the ranking accuracy of DEWS is close to the centralized case, while network overhead for collaborative search and indexing is logarithmic on network size. The results also show that DEWS is resilient to changes in the available pool of indexing webservers and works efficiently even in the presence of heavy query load

Efficient and Decentralized PageRank Approximation in a Peer-to-Peer Web Search Network

PageRank-style (PR) link analyses are a cornerstone of Web search engines and Web mining, but they are computationally expensive. Recently, various techniques have been proposed for speeding up these analyses by distributing the link graph among multiple sites. However, none of these advanced methods is suitable for a fully decentralized PR computation in a peer-to-peer (P2P) network with autonomous peers, where each peer can independently crawl Web fragments according to the user's thematic interests. In such a setting the graph fragments that different peers have locally available or know about may arbitrarily overlap among peers, creating additional complexity for the PR computation. This paper presents the JXP algorithm for dynamically and collaboratively computing PR scores of Web pages that are arbitrarily distributed in a P2P network. The algorithm runs at every peer, and it works by combining locally computed PR scores with random meetings among the peers in the network. It is scalable as the number of peers on the network grows, and experiments as well as theoretical arguments show that JXP scores converge to the true PR scores that one would obtain by a centralized computation

Computing on evolving social networks

Over the past decade, participation in social networking services has seen an exponential growth, so that nowadays most individuals are “virtually” connected to others anywhere in the world. Consistently, analysis of human social behavior has gained momentum in the computer science research community. Several well-known phenomena in the social sciences have been revisited in a computer science perspective, with a new focus on phenomena of emerging behavior, information diffusion, opinion formation and collective intelligence. Furthermore, the recent past has witnessed a growing interest in the dynamics of these phenomena and that of the underlying social structures. This thesis investigates a number of aspects related to the study of evolving social networks and the collective phenomena they mediate. We have mainly pursued three research directions. The first line of research is in a sense functional to the other two and concerns the collection of data tracking the evolution of human interactions in the physical space and the extraction of (time) evolving networks describing these interactions. A number of available datasets describing different kinds of social networks are available on line, but few involve physical proximity of humans in real life scenarios. During our research activity, we have deployed several social experiments tracking face-to-face human interactions in the physical space. The collected datasets have been used to analyze network properties and to investigate social phenomena, as further described below. A second line of research investigates the impact of dynamics on the analytical tools used to extract knowledge from social networks. This is clearly a vast area in which research in many cases is in its early stages. We have focused on centrality, a fundamental notion in the analysis and characterization of social network structure and key to a number of Web applications and services. While many social networks of interest (resulting from “virtual” or “physical” activity) are highly dynamic, many Web information retrieval algorithms were originally designed with static networks in mind. In this thesis, we design and analyze decentralized algorithms for computing and maintaining centrality scores over time evolving networks. These algorithms refer to notions of centrality which are explicitly conceived for evolving settings and which are consistent with PageRank in important cases. A further line of research investigates the wisdom of crowds effect, an important, yet not completely understood phenomenon of collective intelligence, whereby a group typically exhibits higher predictive accuracy than its single members and often experts. Phenomena of collective intelligence involve exchange and processing of information among individuals sharing some common social structure. In many cases of interest, this structure is suitably described by an evolving social network. Studying the interplay between the evolution of the underlying social structure and the computational properties of the resulting process is an interesting and challenging task. We have focused on the quantitative analysis of this aspect, in particular the effect of the network on the accuracy of prediction. To provide a mathematical characterization, we have revisited and modified a number of models of opinion formation and diffusion originally proposed in the social sciences. Experimental analysis using data collected from some of the social experiments we conducted allowed to test soundness of the proposed models. While many of these models seem to capture important aspects of the process of opinion formation in (physical) social networks, one variant we propose achieves higher predictive accuracy and is also robust to the presence of outliers

Online social networks: Measurement, analysis, and applications to distributed information systems

Recently, online social networking sites have exploded in popularity. Numerous sites are dedicated to finding and maintaining contacts and to locating and sharing different types of content. Online social networks represent a new kind of information network that differs significantly from existing networks like the Web. For example, in the Web, hyperlinks between content form a graph that is used to organize, navigate, and rank information. The properties of the Web graph have been studied extensively, and have lead to useful algorithms such as PageRank. In contrast, few links exist between content in online social networks and instead, the links exist between content and users, and between users themselves. However, little is known in the research community about the properties of online social network graphs at scale, the factors that shape their structure, or the ways they can be leveraged in information systems. In this thesis, we use novel measurement techniques to study online social networks at scale, and use the resulting insights to design innovative new information systems. First, we examine the structure and growth patterns of online social networks, focusing on how users are connecting to one another. We conduct the first large-scale measurement study of multiple online social networks at scale, capturing information about over 50 million users and 400 million links. Our analysis identifies a common structure across multiple networks, characterizes the underlying processes that are shaping the network structure, and exposes the rich community structure. Second, we leverage our understanding of the properties of online social networks to design new information systems. Specifically, we build two distinct applications that leverage different properties of online social networks. We present and evaluate Ostra, a novel system for preventing unwanted communication that leverages the difficulty in establishing and maintaining relationships in social networks. We also present, deploy, and evaluate PeerSpective, a system for enhancing Web search using the natural community, structure in social networks. Each of these systems has been evaluated on data from real online social networks or in a deployment with real users