Reputation-based internet sharing in wireless neighborhood community networks

This paper proposes easily applicable distributed reputation-based policies to motivate users of a wireless community to share their Internet connection in a p2p fashion. The goal is the provision of free and good quality Internet access anytime and anywhere inside the community. We study general systems of users with different contribution and consumption profiles and show via an extensive simulation study that when our reputation-based allocation scheme is applied, mobile users enjoy a QoS Internet connection from their community in proportion to their cooperation level; the more greedy for community resources users are, the more contributive they should be to satisfy their needs. ©2010 IEEE

A trust-based exchange framework for multiple services in P2P systems

In this paper we propose fully distributed trust-based policies for p2p systems to regulate the exchange of different type of services and discourage misbehaviour. In a system of peers with different service valuations and capabilities, the resource allocation and server selection policies are based on local reputation vectors, whose elements are the reputations of the peers in providing each service of the system. Our studies demonstrate that proposed policies lead to the dynamic formation of coalitions (cooperation) among peers, which mutually profit by the exchange of their services, without pre-existing knowledge of one another's capabilities and values of services. In this way utilities of all peers progressively increase. Only misbehaving (non contributive) peers do not benefit by such a system, as proposed policies efficiently recognize and block misbehaviour. © 2007 IEEE

Dynamic cooperation enforcement through trust-based allocation policies in P2P systems

In this paper we propose distributed trust-based resource allocation policies to provide fairness in p2p-like systems. According to these policies, the portion of the available resource received by each competing peer depends on (a) his trust/reputation, (b) his resource demands and (c) his request generation rate. The reputation of a peer is quantified and an approach for continuously updating the reputation is proposed such that it reflects the contributions of the peer in the transactions he engages. We study both homogeneous and heterogeneous systems of peers with different resource capabilities and needs and we see from theoretical analysis and simulation results that proposed policies motivate peers to contribute resources in the network by guarantying that peers will only receive resources in proportion to their contributions; thus misbehaving (non contributive) peers cannot exploit the system. Furthermore, proposed policies lead to the dynamic formation of coalitions (cooperation) between peers who mutually benefit by their transactions, according to their capabilities and needs. Peers' coalitions are adaptive to network changes and self-organized as new peers enter the system or strategic peers vary their contributions

Privacy and Personalisation Services

No abstract available

Dissemination and Communication Activities Report – 1st version

No abstract available

Dissemination and Communication Activities Report – 2nd version

No abstract available

Community building over neighborhood Wireless Mesh Networks

The fourth generation (4G) networks provide a converged environment for technology and service provision. The user-centric vision next-generation 4G is neighborhood Wireless Mesh Networks (WMNs). The vision for the formation of WMNs reflects the trade-off between the immediate self-interest of the user, and user's need for social contacts. The use of WMNs provide attractive means in reducing costs, as well as offering added value services and free Internet access. It can also provide additional network capacity, enable the sharing of other resources, and offering services to mobile users applications. The deployment of WMN can be seen as an intermediate step towards more general ad-hoc, user-centric environments

Data Crawlers, Adaptors and Extractors

No abstract available

Genetic algorithms for the optimization of support vector machines in credit risk rating

Summarization: The assessment of credit risk usually involves the development of rating models that classify credit applicants (firms or individuals) into predefined risk groups. A plethora of methodologies have been proposed to develop such rating models. Among them support vector machines (SVMs) have rapidly evolved in statistical learning theory as new modeling technique for developing classification models. However, their application requires the specification of several parameters. This paper proposes the use of genetic algorithms for the determination of optimal parameters for SVM models developed for credit risk assessment. The proposed methodology is applied to three data sets related with the development of credit scoring systems and is compared with discriminant analysis and logistic regression.Παρουσιάστηκε στο: 2nd International Conference on Enterprise Systems and Accountin