A framework for proving the self-organization of dynamic systems

This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems

Trust beyond reputation: A computational trust model based on stereotypes

Models of computational trust support users in taking decisions. They are commonly used to guide users' judgements in online auction sites; or to determine quality of contributions in Web 2.0 sites. However, most existing systems require historical information about the past behavior of the specific agent being judged. In contrast, in real life, to anticipate and to predict a stranger's actions in absence of the knowledge of such behavioral history, we often use our "instinct"- essentially stereotypes developed from our past interactions with other "similar" persons. In this paper, we propose StereoTrust, a computational trust model inspired by stereotypes as used in real-life. A stereotype contains certain features of agents and an expected outcome of the transaction. When facing a stranger, an agent derives its trust by aggregating stereotypes matching the stranger's profile. Since stereotypes are formed locally, recommendations stem from the trustor's own personal experiences and perspective. Historical behavioral information, when available, can be used to refine the analysis. According to our experiments using Epinions.com dataset, StereoTrust compares favorably with existing trust models that use different kinds of information and more complete historical information

Alpha Multipliers Breadth-First Search Technique for Resource Discovery in Unstructured Peer-to-Peer Networks

Resource discovery in unstructured peer-to-peer (P2P) networks is important in the field of grid computing. Breadth-first search (BFS) is widely used for resource discovery in unstructured P2P networks. The technique is proven to return as many search results as possible. However, the network cost of the technique is high due to the flooding of query messages that can degenerate the performance of the whole network. The objective of this study is to optimise the BFS technique, so that it will produce good search results without flooding the network with unnecessary walkers. Several resource discovery techniques used in unstructured P2P networks are discussed and categorised. P2P simulators that are used for P2P network experiments were studied in accordance to their characteristics such as, scalability, extensibility and support status. Several network topology generators were also scrutinised and selected in order to find out the most real-life like network generation model for unstructured P2P experiments. Multiple combinations of five-tuple alpha multipliers have been experimented to find out the best set to make -BFS. In our test, the -BFS increases the query efficiency of the conventional BFS from 55.67% to 63.15%