P2P Environment Research Articles

Fake Product Detection Using Image Processing in Blockchain

There In recent times, the expansion of counterfeit products has become worldwide. There are many counterfeit goods in the latest supply chain. According to the record, counterfeit product affairs have increased in the last few years. It is essential to have a system for customers or clients to check the all details of the product so that client can decide that the product is counterfeit or not. In India actually, there is no such system to identify counterfeit products. So, the solution includes a simple QR code-based identification that can help the end-user or customers to scan and detect the genuineness of the product by using a smartphone. To design and developed a system for implementing system to detect fake products using blockchain technology, in this work system carried out three different modules like Manufacturer, Retailer and Consumers as well. Each transaction has stored into the blockchain which eliminate all network data attacks in P2P environment for product systems Index Terms – Fake Product Detection, Image Processing, Java, Blockchain.

SMART: A Subspace based Malicious Peers Detection algorithm for P2P Systems

In recent years, reputation management schemes have been proposed as promising solutions to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system and thus promote the whole system performance. But few of them have been implemented practically since they still suffer from various security threats, such as collusion, Sybil attack and so on. Therefore, how to detect malicious peers plays a critical role in the successful work of these mechanisms, and it will also be our focus in this paper. Firstly, we define malicious peers and show their influence on the system performance. Secondly, based on Multiscale Principal Component Analysis (MSPCA) and control chart, a Subspace based MAlicious peeRs deTecting algorithm (SMART) is brought forward. SMART first reconstructs the original reputation matrix based on subspace method, and then finds malicious peers out based on Shewhart control chart. Finally, simulation results indicate that SMART can detect malicious peers efficiently and accurately.

Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

The peer-to-peer (P2P) paradigm has become very popular for storing and sharing information. In most P2P systems, peers are connected by means of a limited range of uniform networks, leading to issues when some connected peers are isolated from the others. In order to address such issues, isolated peers rely on devices with long-range networks to relay their messages. However, since long-range devices can move freely, the set of connected peers may lose their connection. Hence, it is important not only to identify, but also to maximise the area known as the safe region (SR) where a long-range device can move freely while still maintaining connection with its peers. This paper illustrates an innovative and generic monitoring framework that addresses the issues related to frequent query location updating using a systematic approach. In our approach, we propose to apply the Reverse Nearest Neighbourhood (RNNH) concept in a P2P environment to efficiently identify and maximise the irregularly shaped area of the SR up to four times for the potential movement of the long-range devices. It was found that there is no need for costly re- computation when the query is retained within the SR. Monte-Carlo simulation was performed to calculate the area of the SR by weighing in shape irregularity. Experimental results demonstrate the effectiveness and efficiency of our approach.

SecP2PVoD: a secure peer-to-peer video-on-demand system against pollution attack and untrusted service provider

The Peer-to-Peer video-on-demand (P2P-VoD) streaming has become widespread in recent years. Unlike the traditional client-server model based video-streaming, the P2P-VoD leverages the peer’s capacity of upload bandwidth for the delivery of video contents in a distributed network. The P2P environment is susceptible to various security threats, in which the pollution attack is one of the potentially destructive threats. Moreover, video streaming is prone to other security challenges, such as authenticity, confidentiality, authorization, and integrity. There have been discussed four possible protection to the pollution attack: blacklisting, hash verification, traffic encryption, and chunk signing. In this paper, we present escrow-free identity-based signcryption (EF-IDSC) scheme for secure data transmission scheme in P2P-VoD streaming with an untrusted service provider. The proposed system enables a peer to establish a session key with other peer using the asymmetric key algorithm. The security analysis shows that the proposed P2P-VoS system prevents pollution attacks under well-known random oracle model and achieves privacy, confidentiality, and subscriber authentication simultaneously. The experimental evaluation shows that the proposed scheme has better computation and communication costs as compared to the related schemes.

Modeling of Agent-Based Complex Network to Detect the Trust of Investors in P2P Platform

Due to the lopsided nature of investor investment-related model research under the traditional P2P environment, and in order to improve the research effect, this study proposes an agent-based complex network testing investor trust model. This model is based on interest trust, and combines with the Bayesian method to effectively evaluate the model trust, and builds a multi-steady-state agent system based on this. At the same time, it effectively analyzes the evolutionary mechanism of the system, and validates the model's application in combination with comparative experiments. The research shows that the model can effectively improve the success rate of executing tasks and shorten the distance between cooperative agents, thus ensuring the reliability of the selection of cooperative objects and providing theoretical reference for subsequent related research.

Performance analysis of efficient data distribution in P2P environment using hybrid clustering techniques

In this paper, K-means algorithm has been applied for distributed large data using hybrid clustering techniques. K-means is a simple and scalable algorithm which can be applied on large datasets. It is one of the well-known unsupervised clustering algorithms that fail in providing structured to unstructured data to enable extraction of valuable information. Peer-to-peer (P2P) technologies divide the data or resources between the peers for managing the network bandwidth, network participants and processing powers. During the data distribution process in the P2P environments, accuracy, computation complexity and distributed clustering accuracy are the important issues as they reduce the entire system performance. So, the author in this paper considered the system for the distribution of data in P2P environment using mining techniques. The data have been distributed using the hybrid map reducing method which analyzes the large volume of data by performing filtering and sorting. The cluster approach analyzes and manages the neighboring relationship about the peer nodes that helps in the management of the cluster distribution in the dynamic environment. Determination of the efficiency of the cluster formed is done with the help of the hybrid clustering algorithm, and the related system architecture is proposed. The clustering efficiency has been enhanced in the P2P environment using the distributed data network. The efficiency of the formed cluster was evaluated in terms of Jaccard index, F-measures, mutual information and rand measure. The performance of the system was analyzed using the experimental results and discussions, namely, error rate, accuracy and time. The multi-objective system helps in easing the difficulties in the implementation of P2P environment sensitive to initial solutions.

Construction of personal knowledge maps for a peer-to-peer information-sharing environment

PurposeBrowsing knowledge documents in a peer-to-peer (P2P) environment is difficult because knowledge documents in such an environment are large in quantity and distributed over different peers who organize the documents according to their own views. This paper aims to propose a method for constructing a personal knowledge map for a peer to facilitate knowledge browsing and alleviate information overload in P2P environments.Design/methodology/approachThe research presents a method for constructing a personal knowledge map. The method adopts an ontology-concept-tree-based classification algorithm to recognize a peer’s personal knowledge structure and construct a personal knowledge map, and uses a self-organizing map algorithm to cluster and visualize the knowledge documents. The correctness of the created knowledge map is evaluated with a collection of abstracts of academic papers.FindingsThe method for constructing a personal knowledge map is the main finding of this research. The evaluation shows that the created knowledge map is good in quality.Research limitations/implicationsThe proposed method provides a way for P2P platforms to understand their users’ knowledge background, as well as to improve the P2P platform environment. However, the proposed method will not help a peer when he has nothing in his individual knowledge document repository (i.e. the “cold start” problem). The method also requires a relatively good ontology base for a P2P document sharing system to use the method effectively.Originality/valueIt is novel that the proposed method organizes the knowledge documents related to a peer’s knowledge background into a personal knowledge map. Moreover, the created knowledge map combines the advantages of a hierarchical display and a map display. It has values for a distributed P2P environment to facilitate users’ knowledge browsing and to alleviate information overload.

Fast Fourier transform based efficient data processing technique for big data processing speed enhancement in P2P computing environment

Recently, as the mobile phone technology has been developed, apps used on mobile phones have also been developed rapidly in P2P cloud computing environment. In particular, as the IoT(Internet of Things) technology has been grafted on mobile phones, large and small data have been diversified centering on P2P computing environments. However, due to the diverse sizes and uses of big data used in P2P environments, users have many complaints regarding the low accuracy of big data search results and service delay. Previous studies have been actively researched to develop a technology that can cheaply construct and efficiently utilize IT infrastructure for big data processing. In particular, research has been conducted on technologies in which a large number of servers distribute and manage a huge amount of data generated or processed from a portable device such as a smartphone. However, existing researchers do not only store and manage big data, but also need additional data latency and service delay prevention technology to cope with various problems flexibly and to prevent service interruption. Currently, there are many researchers, but until now, there has been no research to satisfy user’s search delay time and accuracy by using size and usage of big data. This paper proposes a fast Fourier transform-based efficient data processing scheme so that users can accurately search for desired data out of different kinds (size, use, type, etc.) of big data in P2P cloud computing environment. The proposed scheme utilizes the keywords used by users to search big data as coefficients of polynomial expressions with a view to enhancing polynomial expression transformation speeds. In addition, the proposed scheme organized the coefficients of polynomial expressions that constitute subnets in pairs with probability values for processing in linkage with each other to enhance data accessibility. In particular, the proposed scheme transforms the vectors shown using the coefficients of polynomial expressions in pairs with the polynomial expressions so that searched data can be quickly identified thereby minimizing user service delay time. As a result of the performance evaluation, the data processing time was improved by 7.3% on average compared with the existing techniques and the server’s data processing rate per unit time was improved by 11.1% on average compared to existing techniques. In addition, according to the data size, the communication delay time between the server and the user improved by an average of 8.9% and server overhead was 10.4% lower than the existing techniques on average.

A decentralized gossip based approach for data clustering in peer-to-peer networks

In this paper, a novel distributed approach, named GDSOM-P2P, for clustering distributed data resources is proposed by combining, an improved version of Silhouette algorithm, the dynamic Self-Organizing Map (SOM) neural network, and VICINITY protocol as a generic overlay management framework based on self-organization. The proposed GDSOM-P2P is adapted to the dynamic conditions of these networks. In the proposed GDSOM-P2P algorithm, at first, each node extracts a number of important data through the SOM and Silhouette algorithms. Then each of the nodes chooses one of its neighbors with the help of the VICINITY algorithm, and exchanges their important data with their neighbors. By doing this, over a period, the nodes’ data will be distributed over the entire network and the nodes in the network access the summary data model of the whole data. Finally, each node aggregates its internal data with a summary model and then performs the final clustering to cluster its internal data correctly. Evaluation results over a real P2P environment verify the efficiency of proposed GDSOM-P2P. Furthermore, the proposed GDSOM-P2P is also compared with the existing well-established distributed data clustering techniques. The results show a significant accuracy improvement of the proposed method.

DISTRIBUTED VIDEO IDENTIFICATION WITH PERCEPTUAL TAGS IN PEER-TO-PEER NETWORKS

Existing methods in Peer-to-Peer (P2P) networks mainly use file tags or cryptographic hashes of the entire file for video searching and identification. These methods however become insufficient to correctly identify a video when the name and format of the files are changed. In this paper, a distributed solution is proposed for video identification and copy detection in P2P networks, which represents a video file in the network with a set of (64-256) bits, named as perceptual tags. As such information is derived from the perceptual content of the video rather than its bitstream representation as in the case of cryptographic hashes, it provides a robust identification after the alterations in the file names and formats provided that the visual quality of the video is at acceptable levels. The paper first briefly discusses the requirements for a distributed perceptual tagging system considering the low computational power and low bandwidth of internet users. Then, it presents the proposed perceptual tag extraction method using the temporal differences between the video frame averages and the proposed distributed searching scheme for a P2P implementation. The proposed extraction and searching methods provide robustness to the alterations in video formats and small additions and cuttings in the video content as the typical processing in P2P environment and also achieve uniform distribution and storage load between the peers.

Evaluation of P2P and cloud computing as platform for exhaustive key search on block ciphers

Over the years, parallel computing models have been proposed to solve large-scale application problems. P2P and cloud computing are well-known distributed computing models and have the advantage of running and implementing the parallel computing. Applying the advantages of both models can enhance the benefits of parallel computing. In this paper, we analyze the efficiency of key search algorithm by combining P2P and cloud computing. For our experiment, we apply the key search algorithm in the field of cryptography. The length of the key, which is stable criterion of cryptographic algorithm, is judged according to the amount of exhaustive key search. And the key space required for the whole investigation is easy to divide and is very appropriate for parallel calculation of P2P environment. In addition, cloud computing can provide the fitting environment to meet the various user requirements using virtualization technology. We conduct the following two performance experiments with P2P and cloud computing. First, we propose the method to guarantee the performance in P2P environment based on virtualization. Next, we simulate the performance of the suggested encryption method in the aforementioned system environment. Results reveal effectiveness and validity of the proposed system environment, which can also provide both scalability and flexibility.

Strategic Information Transmission in Peer-to-Peer Lending Markets

Peer-to-peer (P2P) marketplaces, such as Uber, Airbnb, and Lending Club, have experienced massive growth in recent years. They now constitute a significant portion of the world's economy and provide opportunities for people to transact directly with one another. However, such growth also challenges participants to cope with information asymmetry about the quality of the offerings in the marketplace. By conducting an analysis of a P2P lending market, the authors propose and test a theory in which countersignaling provides a mechanism to attenuate information asymmetry about financial products (loans) offered on the platform. Data from a P2P lending website reveal significant, nonmonotonic relationships among the transmission of nonverifiable information, loan funding, and ex post loan quality, consistent with the proposed theory. The results provide insights for platform owners who seek to manage the level of information asymmetry in their P2P environments to create more balanced marketplaces, as well as for P2P participants interested in improving their ability to process information about the goods and services they seek to transact online.

Mining health-risk factors using PHR similarity in a hybrid P2P network

In an era of many diseases and increased longevity, more attention has been paid to chronic diseases that require constant health care. Under this circumstance, the development of research and development (R&D) for smart-device-based constant health care has drawn great attention. With the emergence of wearable devices, personal health devices (PHDs), and smartphones, various contents for constant health care have been developed. By using these devices, the users are able to collect personal health records (PHRs) that include data such as activity amount, heart rate, stress, and blood sugar. The range of the collected PHRs can be limited depending on the equipment or the surrounding environment. To overcome this problem, it is necessary to make a comparison with similar users in a cluster. Also, it is necessary to provide a service that can analyze and visually display the collected personal-health information. In this paper, we propose the mining of health-risk factors using the PHR similarity in a hybrid P2P network. This is a method of predicting a user’s health status using similarity-based data mining, where the PHRs are employed in a hybrid P2P environment consisting of a peer, a server, and a gateway. In a hybrid P2P environment, a user receives feedback on the result of a structured-data analysis. A peer searches for a different peer and gateway through a server and exchanges information. Depending on the data type, the PHR is divided into medical health examination, self-diagnosis, and personal-health data. The medical health examination contains the personal-health data that are generated regularly by a medical institution. Self-diagnosis represents the data of mental health, pains, and fatigue that can be changed often but cannot be collected by devices. Personal-health data mean the data that can be collected by individuals in everyday life. For the PHR-data analysis, an index is given to each attribute, and preprocessing is performed after a binary-code conversion. To predict a user’s health status, the PHR data are clustered on the basis of similarity in a hybrid P2P environment. The similarity between a user’s PHR and a PHR that is searched for in the network is measured. After the measurement, an index is given to the PHR that meets the minimum similarity and the PHR is incorporated into a Similarity PHR Group. The Similarity PHR Group flexibly changes depending on a user’s PHR status and the statuses of the users who have accessed the hybrid P2P network. A representative value of the Similarity PHR Group is extracted and is then compared with the user’s PHR to judge the user’s health status. The proposed method is suitable for a smart health service for chronic diseases requiring constant care, elderly health, and aftercare. This is a user-oriented health-care and promotion service wherein a user’s health status can be predicted through the mining of the health-risk factors of PHRs.

Initialization Method for Communication and Data Sharing in P2P Environment Between Wireless Sensor Nodes

Wireless Sensor Networks have increased notewor-thy thought nowadays, rather than wired sensor systems, by presenting multi-useful remote hubs, which are littler in size. However, WSNs correspondence is inclined to negative impacts from the physical environment, like, physical hurdles and interfer-ence. The reason for this work is to outline a testbed, to introduce method for communication startup and data sharing in a peer to peer (p2p) environment between wireless sensor nodes. The work is directed on both the IEEE 802.15.4 physical and the application layers. In this testbed, one channel, from the IEEE 802.15.4 channels range is devoted as an “emergency channel” which is utilized for handshaking or in case there is communication failure between the Transmitter (Tx) and Receiver (Rx) nodes. The remaining 15 channels are called “data channels” and are utilized for real information transmission and control signals. Linux based TinyOS-2.x is utilized as a working framework for low power sensors. MICAz bits are utilized as nodes and a MIB520 programming board is utilized for burning the codes and for the purpose of gateways.

A Blockchain Based Truthful Incentive Mechanism for Distributed P2P Applications

In distributed peer-to-peer (P2P) applications, peers self-organize and cooperate to effectively complete certain tasks such as forwarding files, delivering messages, or uploading data. Nevertheless, users are selfish in nature and they may refuse to cooperate due to their concerns on energy and bandwidth consumption. Thus each user should receive a satisfying reward to compensate its resource consumption for cooperation. However, suitable incentive mechanisms that can meet the diverse requirements of users in dynamic and distributed P2P environments are still missing. On the other hand, we observe that Blockchain is a decentralized secure digital ledger of economic transactions that can be programmed to record not just financial transactions and Blockchain-based cryptocurrencies get more and more market capitalization. Therefore in this paper, we propose a Blockchain based truthful incentive mechanism for distributed P2P applications that applies a cryptocurrency such as Bitcoin to incentivize users for cooperation. In this mechanism, users who help with a successful delivery get rewarded. As users and miners in the Blockchain P2P system may exhibit selfish actions or collude with each other, we propose a secure validation method and a pricing strategy, and integrate them into our incentive mechanism. Through a game theoretical analysis and evaluation study, we demonstrate the effectiveness and security strength of our proposed incentive mechanism.

모바일 P2P환경에서 제한적인 자원을 고려한 신뢰성 판별 기법

최근 모바일 기기와 근거리 무선 통신 기술 등의 발달로 기존 중앙 집중 처리 시스템의 한계를 개선하기 위해 모바일 P2P 네트워크에 대한 연구가 활발하게 진행되고 있다. 모바일 P2P네트워크에서 피어는 배터리, 메모리, 저장 공간 등과 같은 제한적인 요소들을 가진다. 모바일 P2P 네트워크에서 안전한 콘텐츠 공유를 위해서는 피어의 신뢰성이 판별되어야 한다. 본 논문에서는 제한된 모바일 P2P 네트워크에서 신뢰도를 판별하는 기법을 제안한다. 제안하는 기법은 피어의 평가 정보를 이용하여 직접적으로 피어가 평가한 값과 다른 피어가 평가한 간접적인 평가 값을 이용하여 피어의 신뢰성을 판별한다. 피어가 가지는 평가 정보로 최근 갱신 시간을 활용한다. 피어 평가 정보의 최근 갱신 시간을 비교하여 평가 정보 공유 시 중복되는 평가 정보를 감소시킨다. 성능 평가를 통해 제안된 기법이 메시지 수를 감소시키며 신뢰성에 대한 정확도를 향상 시키는 것을 입증한다. Recently, with the development of mobile devices and near field communication, mobile P2P networks have been actively studied to improve the limits of the existing centralized processing system. A peer has limited components such as batteries, memory and storage spaces in mobile P2P networks. The trust of a peer should be discriminated in order to share reliable contents in mobile P2P networks. In this paper, we propose a trust discrimination scheme considering limited resources in mobile P2P environments. The proposed scheme discriminates the trust of a peer by direct rating values using the rating information of the peer and indirect rating values by the other peers. The recent update time is included in the rating information. The proposed scheme reduces the redundant rating information by comparing the recent update times of the rating information. It is shown through performance evaluation that the proposed scheme reduces the number of messages and improves the accuracy of trust over the existing scheme.

Work Stealing Based Volunteer Computing Coordination in P2P Environments

Work Stealing Based Volunteer Computing Coordination in P2P Environments

Work Stealing Based Volunteer Computing Coordination in P2P Environments

Work Stealing Based Volunteer Computing Coordination in P2P Environments

Cooperation enhanced by indirect reciprocity in spatial prisoner’s dilemma games for social P2P systems

Abstract With the growing interest in social Peer-to-Peer (P2P) applications, relationships of individuals are further exploited to improve the performances of reputation systems. It is an on-going challenge to investigate how spatial reciprocity aids indirect reciprocity in sustaining cooperation in practical P2P environments. This paper describes the construction of an extended prisoner’s dilemma game on square lattice networks with three strategies, i.e., defection, unconditional cooperation, and reciprocal cooperation. Reciprocators discriminate partners according to their reputations based on image scoring, where mistakes in judgment of reputations may occur. The independent structures of interaction and learning neighborhood are discussed, with respect to the situation in which learning environments differ from interaction networks. The simulation results have indicated that the incentive mechanism enhances cooperation better in structured peers than among a well-mixed population. Given the realistic condition of inaccurate reputation scores, defection is still successfully held down when the players interact and learn within the unified neighborhoods. Extensive simulations have further confirmed the positive impact of spatial structure on cooperation with different sizes of lattice neighborhoods. And similar conclusions can also be drawn on regular random networks and scale-free networks. Moreover, for the separated structures of the neighborhoods, the interaction network has a critical effect on the evolution dynamics of cooperation and learning environments only have weaker impacts on the process. Our findings further provide some insights concerning the evolution of collective behaviors in social systems.

A Peer Management Policy for Energy Efficiency in Mobile P2P Networks

Recently, mobile devices with multiple network interfaces are not only being used in the daily life but also across the entire industry in diverse ways and in many different sectors, including medical, health, education, business, and entertainment. Especially, analyzing the mobile usage patterns of users, there's been an increasing number of services that provide diverse types of content on an Internet-based environment, including video conferencing and VOD streaming services. For these kinds of services, due to the limitations imposed by the client/server architecture which involves going by a base station, the need for P2P is currently being emphasized, which allows direct communication between mobile devices. In order to improve the stability of mobile P2P services, however, there are challenges to overcome, including constraints of wireless communication (such as irregular bandwidth and frequent interrupts in the connection) as well as limitations of the mobile devices themselves (such as limited CPU and battery capabilities), which are classical mobile computing problems. Accordingly, this paper suggests a peer management policy for mobile P2P environments with high degrees of mobility which is designed to increase the efficiency of data transmission by taking sufficiently into account network characteristics and energy efficiency of mobile devices.