Peers In P2P Network Research Articles

Local Contribution (LC) and Trustworthiness Factors to Induce Fairness in P2P Networks

A peer-to-peer (P2P) network is a distributed system in which the autonomous peers participate at their motivation and resources are shared in distributed manner. P2P systems can be classified in two major categories: centralized and distributed. Distributed P2P systems can be further classified in two types: structured and unstructured. With immense increase in Internet, the P2P networks have found increased interest in the research community since the different protocols for these networks can be gainfully utilized in the resource discovery process for Internet related applications. However, there are several challenges in designing an efficient protocol for the P2P networks. These networks suffer from problems such as fake content distribution, free riding, whitewashing, poor search scalability, lack of a robust trust model, etc. Moreover, the peers can join and leave the network (churn) at any time which makes trust management and searching in these networks more challenging. Free riding nature of the peers damages overall performance of the P2P systems. There are several mechanisms proposed for promoting fairness in the P2P systems like tit-for-tat policy, point-based incentive policy, PageRank, Eigen Trust, layered taxation, advertisement of incentive, etc. We have observed that in some schemes like point-based incentive scheme, peers are stricken in the process of requesting data download which leads to the starvation. We have proposed a new scheme which collects the statics of resources shared by the participating peers in P2P networks and calculated local contribution and trustworthiness factors based on the statistics. The starvation problem is minimized in the proposed scheme. We have done analytical modeling and analysis of different costs involved in downloading a file with the costs in existing scheme and found that the costs involved in proposed scheme is less than the existing scheme.

Soft computing approach based malicious peers detection using geometric and trust features in P2P networks

The attacks in P2P networks can be classified into two types: passive and active attacks. Passive attacks refer to the attempts made by malicious nodes to perceive the activities, whereas the active attacks are attacks performed by the malicious nodes that bear some energy cost to perform the attack. Malicious peer detection in P2P networks is a complication task due to the similar characteristics between trusty peer and malicious peer. This paper focuses the malicious peer detection system using feed forward back propagation neural networks. The characteristic features such as Geometric and Trust features are extracted from the peers in P2P networks and these extracted features are trained and classified using neural networks. The performance of the proposed malicious peer detection system is analyzed in terms of latency and detection rate.

EERP: An enhanced EigenTrust algorithm for reputation management in peer-to-peer networks

Open peer-to-peer (P2P) networks have many applications in large-scale distributed systems, internet computing, file sharing and social networks. Peers in P2P networks share data or computing resources while being strangers to each other. In many cases, peers are anonymous volunteers in a dynamic environment where peers can join and leave the network at any time. The open nature of P2P networks is an ideal environment for malicious peers to spread their inauthentic files. Furthermore, some malicious peers form groups to mislead the rest of the peers by giving malicious friends within their groups faked high trust values to increase their reputations. These challenges increase the need to manage the reputation schemes in P2P networks. This paper proposes the EERP algorithm, which focuses on malicious peers and how they can be identified and isolated from the network. This is done by saving the reputation information of each peer as a chain that contains, in addition to the peer’s global reputation value, the rating given to this particular peer after each transaction they are involved in and the identity of the peer who gave the rating. Analysing these logs of peers’ histories helps to track and identify malicious peers. EERP has the potential to effectively reduce the number of inauthentic files spread in a P2P network as compared with other trust management systems.

A swarm intelligence learning model of adaptive incentive protocols for P2P networks

Incentive protocols are critical for promoting contribution and cooperation among peers in P2P networks. The behaviour of peers has a significant impact on the effects of incentive protocols. Inspired by the biological systems, a swarm intelligence learning model of adaptive incentive protocols is proposed for P2P networks. The learning model is designed by having peers as particles in the moving swarm. The learning and adaption of peers are guided by the current best strategy as well as the best strategy in history. Simulation results demonstrate that the proposed learning model has a faster convergence rate towards at least the quasi-optimum than the two existing learning models.

Detecting Malicious Peers with Past Behavior and Identifying Absolute Trust Peers in P2P Networks

Detecting Malicious Peers with Past Behavior and Identifying Absolute Trust Peers in P2P Networks

Highly Available Hypercube Tokenized Sequential Matrix Partitioned Data Sharing in Large P2P Networks

Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.

Adaptive video protection in large scale peer‐to‐peer video streaming over mobile wireless mesh networks

SummaryBecause video streaming over mobile handheld devices has been of great interest, the necessity of introducing new methods with low implementation cost and scalable infrastructures is a strong demand of the service. In particular, these requirements are present in popular wireless networks such as wireless mesh networks (WMN). Peer‐to‐peer (P2P) networks promise an efficient scalable network infrastructure for video streaming over wired and wireless networks. Limited resources of the peers in P2P networks and high error rate in wireless channels make it more challenging to run P2P streaming applications over WMNs. Therefore, it is necessary to design efficient and improved error protection methods in P2P video streaming applications over WMNs. In this paper, we propose a new adaptive unequal video protection method specially intended for large scale P2P video streaming over mobile WMNs. Using this method, different frames have different priorities in receivers along the recovery process. Moreover, we precisely and completely evaluate different aspects related to frame protection in these networks using five important performance metrics including video distortion, late arrival distortion, end‐to‐end delay, overhead and initial start‐up delay. The results obtained from a precise simulation in OMNeT++ show that the proposed adaptive method significantly outperforms other solutions by providing better video quality on mobile wireless nodes. Copyright © 2015 John Wiley & Sons, Ltd.

On Optimizing Overlay Topologies for Search in Unstructured Peer-to-Peer Networks

Unstructured peer-to-peer (P2P) file-sharing networks are popular in the mass market. As the peers participating in unstructured networks interconnect randomly, they rely on flooding query messages to discover objects of interest and thus introduce remarkable network traffic. Empirical measurement studies indicate that the peers in P2P networks have similar preferences, and have recently proposed unstructured P2P networks that organize participating peers by exploiting their similarity. The resultant networks may not perform searches efficiently and effectively because existing overlay topology construction algorithms often create unstructured P2P networks without performance guarantees. Thus, we propose a novel overlay formation algorithm for unstructured P2P networks. Based on the file sharing pattern exhibiting the power-law property, our proposal is unique in that it poses rigorous performance guarantees. Theoretical performance results conclude that in a constant probability, 1) searching an object in our proposed network efficiently takes O(\ln^{c} {\cal N}) hops (where c is a small constant), and 2) the search progressively and effectively exploits the similarity of peers. In addition, the success ratio of discovering an object approximates 100 percent. We validate our theoretical analysis and compare our proposal to competing algorithms in simulations. Based on the simulation results, our proposal clearly outperforms the competing algorithms in terms of 1) the hop count of routing a query message, 2) the successful ratio of resolving a query, 3) the number of messages required for resolving a query, and 4) the message overhead for maintaining and formatting the overlay.

Free-riding Analysis Via Dynamic Game with Incomplete Information

P2P networks are distributed, acentric and self-organized systems. Due to the incomplete information of network environment, the uncertainty of trust relationship among peers and the selfishness of the peers in P2P networks, which give rise to many free-riders that seriously impact the stability and scalability of P2P networks. In this paper, by analyzing the incomplete information of network environment, the uncertainty of trust relationship among nodes, the phenomenon of the free-riding is studied based on game theory. The IIDGTrust (Incomplete Information Dynamic Game Trust)mechanism is presented through the case “Supplying the Public Resources”. Updating the trust relationship among the nodes according to the Bayesian law, which make nodes choose better strategies in time. The experimental results demonstrate that the IIDGTrust mechanism can effectively reduce the proportion of the free-riders in the P2P networks and maintain the stability of networks better.

Randomized Search Strategy for Unstructured P2P Networks

P2P (peer-to-peer) file sharing systems have been in operation for years. However, recent studies show that many peers in P2P networks are free-riders, who download files but are unwilling to share. This paper proposes a randomized search algorithm that considers the potential of a peer for information exchange and controlled query forwarding in the search process. Based on churn situations, the simulation results in this study demonstrate that the proposed algorithm can reduce network traffic and search latency while searching for files in the system.

ON COVERAGE BOUNDS OF UNSTRUCTURED PEER-TO-PEER NETWORKS

In this paper, we develop methods to estimate the network coverage of a TTL-bound query packet undergoing flooding on an unstructured p2p network. The estimation based on the degree distribution of the networks, reveals that the presence of certain cycle-forming edges, that we name as cross and back edges, reduces the coverage of the peers in p2p networks and also generate a large number of redundant messages, thus wasting precious bandwidth. We therefore develop models to estimate the back/cross edge probabilities and the network coverage of the peers in the presence of these back and cross edges. Extensive simulation is done on random, power-law and Gnutella networks to verify the correctness of the model. The results highlight the fact that for real p2p networks, which are large but finite, the percentage of back/cross edges can increase enormously with increasing distance from a source node, thus leading to huge traffic redundancy.

FineTrust: a fine‐grained trust model for peer‐to‐peer networks

AbstractTrust research is a key issue in peer‐to‐peer (P2P) networks. Reputation‐based trust models as one of the good solutions to resolve the trust problems in P2P network are received more and more attention in recent years. One of the fundamental challenges is to capture the evolving nature of a trust relationship between peers and reflect the varied bias or preference of peers in a distributed and open environment. In this paper, we present a fine‐grained trust computation model for P2P networks. Our model defines the service as a fined‐grained quality‐of‐service (QoS) (N‐dimensional vector), and in order to accurate the recommendation trust computing, several concepts are introduced to reflect the recommenders' current status, history behavior, and the gap between these two behaviors. Also, we firstly introduce the Gauss‐bar function to measure the preference similarity between peers. All these will result in a flexible model which represents trust in a manner more close to human intuitions and satisfies the diverse QoS requirements of peers in P2P networks. The extensive simulations have confirmed the efficiency of our model. Copyright © 2009 John Wiley & Sons, Ltd.

Robust Feedback Credibility-Based Distributed P2P Trust Model

开放、共享与匿名的Peer-to-Peer(简称P2P)网络已经取得了越来越多的应用,无中心对等的特性也吸引了越来越多的用户.但由于其网络中的节点不受约束,资源的共享是用户自愿的行为,因此节点间的信任很难通过传统的信任机制建立.一种可行的解决方案是借鉴人际网络中的信任关系,建立一种基于信誉的全局信任模型.已有的工作基本建立在信任度高的节点其反馈也更可信这个假设的基础上,将节点的反馈质量简单地等同于服务质量.针对这一问题,提出了一种基于节点反馈可信度的分布式P2P全局信任模型(简称FCTrust),用于量化和评估节点的可信程度,并给出了模型的数学表述和分布式实现方法.分析及仿真实验结果表明,FCTrust较已有的全局信任模型在遏制更广泛类型的恶意节点攻击的有效性、迭代计算的收敛性及消息成本上有较大提高.;The open, sharing and anonymous nature of peer-to-peer (P2P) networks has made it popular in many large-scale distributed applications over the Internet. However, due to the fact that resource-sharing activity of a peer in P2P networks is a volunteer behavior and it is not responsible for its irresponsible bartering history, the trust relationship between participants can not be constructed only on the traditional trust mechanism. A feasible resolution derived from the trust relationship in social networks, is to establish a reputation based global trust model. The previous work about the global trust model is mostly based on the assumption that the peer with higher trust value will provide more honest feedbacks, and make the quality of feedback of a peer be approximately equal to that of service of the peer. However, this is not always true. To solve this problem, this paper proposes a robust feedback credibility (FC) based distributed P2P global trust model (FCTrust), to quantify and evaluate the trustworthiness of participants, and gives the mathematic analyses and distributed implementation method. Theoretical analyses and simulation experiments show that FCTrust has advantages in combating various malicious behaviors such as dishonest feedbacks from malicious peers, the collusion and the strategic attacks to the trust model itself, over the current global trust models, and demonstrates more robustness and effectiveness.

Priority Random Linear Codes in Distributed Storage Systems

Node churn and failures exist as fundamental characteristics in both peer-to-peer (P2P) and sensor networks. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes (RLCs), as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority RLCs is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority RLCs.

An agent-based incentive mechanism for P2P systems

With the increasing uses of peer-to-peer (P2P) systems, the problems of fair information and resources sharing become serious. P2P systems are self-organizing, distributed systems with no centralized authority, such as Free-riding and Tragedy of Commons. Because of the voluntary participation and lack of motivation, the information and resources available in P2P networks are extremely variable and unpredictable. This article studied the problems existing in P2P networks and propose a different method to stimulate the peers in P2P networks to share information and resources, using agents so as to improve the performance of P2P networks. And through simulation experiments and analyzing the results, it can be concluded that this mechanism can effectively solve the problems of fair sharing in P2P systems.

Incentive Compatible Adaptive Topologies for P2P Networks

In file sharing P2P (peer-to-peer) networks, the service availability is seriously affected by peers’ voluntary actions. For example, there are many freeriders and malicious peers in P2P networks. However, the pure P2P networks don’t take the issue of freeriders and malicious peers as the inherent part of the topology design, and all the peers are symmetry in the topology. This paper proposes a reciprocal capacity based adaptive topology protocol for P2P networks, which takes account of the peer’s rational belief of maintaining connections. The simulation and analyses show that the resulting topology is incentive compatible to different types of peers. In addition, compared with the proposed similar scheme, it is also more efficient with less network cost.