An important goal in P2P networks is that all peers provide resources. However, free riding and tragedy of common are real issues in P2P networks. To resolve these problems, most of the existing work is concerning probabilistic estimation to evaluate the trustworthiness or mechanism design to provide incentive. Instead of design a protocol to solve free riding, we build a micro-payment architecture for these existing protocols using virtual currency which can be more precisely measured and easily be replaced by reputation or other tokens. Our system can avoid from long-term trust learning interactions and high cost of collecting and analyzing reputation information. It can also provide peers incentive to truly report their connection type and security to malicious attacks.
Clustering plays an important role in data mining. It helps to reveal intrinsic structure in data sets with little or no prior
knowledge. The approaches of clustering have received great attention in recent years. However many published
algorithms fail to do well in determining the number of cluster, finding arbitrary shapes of clusters or identifying the
presence of noise. In this paper we present an efficient clustering algorithm which employs the theory of grid, density
and fractal that can partition points in the same cluster with minimum change of fractal dimension meanwhile
maximizing the self-similarity in the clusters. We show via experiments that FDC can quickly deal with multidimensional
large data sets, identify the number of clusters, be capable of recognizing clusters of arbitrary shape and
furthermore explore some qualitative information from data sets.
Peer-to-Peer (P2P) systems are currently receiving considerable interest. However, as experience with P2P networks shows, the selfish behaviors of peers may lead to serious problems of P2P network, such as free-riding and white-washing. In order to solve these problems, there are increasing considerations on reputation system
design in the study of P2P networks. Most of the existing works is concerning probabilistic estimation or social
networks to evaluate the trustworthiness for a peer to others. However, these models can not be efficient all the
time. In this paper, our aim is to provide a general mechanism that can maximize P2P networks social welfare in
a way of Vickrey-Clarke-Groves family, while assuming every peer in P2P networks is rational and selfish, which
means they only concern about their own outcome. This mechanism has some desirable properties using an <i>O</i>(<i>n</i>)
algorithm: (1) incentive compatibility, every peer truly report its connection type; (2) individually rationality;
and (3) fully decentralized, we design a multiple-principal multiple-agent model, concerning about the service
provider and service requester individually.
Clustering or grouping of similar objects is one of the most widely used procedures in data mining, which has received
enormous attentions and many methods have been proposed in these recent decades. However these traditional clustering
algorithms require all the data objects to be located at one single site where it is analyzed. And such limitation cannot
face the challenge as nowadays monstrous sizes of data sets are often stored on different independently working
computers connected to each other via local or wide area networks instead of one single site. Therefore in this paper, we
propose a fully distributed clustering algorithm, called a fully distributed clustering based on fractal dimension
(FDCFD), which enables each site to collaborate in forming a global clustering model with low communication cost. The
main idea behind FDCFD is via calculating fractal dimension to group points in a cluster in such a way that none of the
points in the cluster changes the cluster's fractal dimension radically. In our theoretical analysis, we will demonstrate
that our approach can work very well for clustering data that is inherently distributed, collect information spread over
several local sites to form a global clustering meanwhile without communication costs and delays for transmitting.
P2P systems are usually used for information exchange between peers in recent years. However, the open and anonymous nature of a P2P network makes it an ideal medium for malicious peers. There is a lack of efficient mechanism for existing P2P systems to avoid from free riding, whitewashing, collusion and malicious attackers. In this paper, we describe a novel role-base trust model for P2P file sharing system. First, we give object criteria to track each peer's contribution to the system. Second, according to their contribution we divide all the peers into 2 parts: super peers and normal peers. Each of the 2 roles is bonded with different rights and obligations. Third, we show how to carry out the computation and storage at local and global. Finally, we discuss how our trust model allows peer to revoke relationships with distrusted peers. We present a concrete method to validate the proposed trust model and report sets of simulation-based experiments, showing the feasibility and robustness of this model.
HASN is a hierarchical routing protocol for heterogeneous sensor networks, optimized via cross-layer designs to save sensor's power and improve reliability. There are two kinds of nodes in heterogeneous sensor networks: normal sensor and header node that has more powerful battery and higher performance antenna. A header and sensors in its radio transmitting range compose a cluster. The header takes charge of data collection and data aggregation in its cluster. In a cluster, the communication is unsymmetrical. From the header to sensors is directly reachable, but from sensors to their header needs multi-hop. In this paper, a new dynamic address assignment method is introduced for large number of sensors automatically. A mathematics model of energy optimum relay tree is designed, which can guarantee the minimum energy cost forwarding and relay load balance. We give an approximation algorithm to resolve the model. A centralized scheduling management is proposed to avoid collisions completely in a cluster. We also introduce a mechanism to depress data redundancy.
In existing peer-to-peer database framework designs, coordination rules are assumed already present and never changed
during the whole course of operation. This paper investigates how coordination rules are created and changed, hence
helping ease the procedure. Local database can be on and off dynamically, but this feature of P2P database is
inconsistent with fixed coordination rules, for dependency path will be broken when an intermediate peer is absent. A
restoration mechanism is designed in this scenario to realize dynamic coordination rule. To achieve this, coordination
rules on the same dependency path have to be available after the path is broken, and combined together to form a new
dependency path and bypass the absent peer. To backup rules before host is down they can be published as resource
advertisement to remote peers by underlying P2P platform facility. Actually since coordination rules are no longer
bounded with their host, they can be viewed independent from the database system to form a coordination rule P2P
network, with some peers having no database and purely as rule cache. The protocols about rule cache, combination and
new rule creation request in such network are discussed. Rules float along dependency paths across network and
combine to form a new rule where necessary. A peer wanting to create new coordination rules can publish query and if
there is a rule on another peer which can be combined with the existing one, a new rule is created and send back. This
dependency path discovery process can be similar to route discovery process.