Cryptocurrency Networks: A New P2P Paradigm

Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research.

A P2P network that is overlayed over Internet can consist of thousands, or even millions of nodes. To analyze the performance of a P2P network, or an algorithm or protocol designed for a P2P network, simulation studies have to be performed quite often, and simulation studies require the use of appropriate models for various components and parameters of a P2P network simulated. Therefore it is important to have models and statistical information about various parameters and properties of a P2P network. This paper tries to model and obtain the characteristics of some of the important parameters of one widely used P2P network, Gnutella. The methodology to derive the characteristics is based on collecting P2P protocol traces from the Gnutella network that is currently running over the Internet, and analyzing the collected traces. The results we present in this paper will be an important ingredient for studies that are based on simulation of P2P networks, especially unstructured P2P networks.

Peer-to-peer (P2P) networks are becoming increasingly popular and are an exciting new class of innovative, internet-based resource sharing systems. In a P2P network, a reputation system is essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors. The system collects locally-generated peer feedbacks and aggregates them to yield the global reputation values to represent peer trustworthiness. Reputation system guides peers in choosing a server for services/resources. Most of the approaches for peers to exploit reputation metrics are to select the one with the highest reputation value as a providing server. However, it may lead to unexpectedly low efficiency for high-reputed peers, and prevents P2P networks from taking full advantage of all resources for high performance. Other approaches restrict a peer to select a server in the same or lower reputation values. However, these approaches prevent P2P networks from achieving their goal of widely resource sharing and service exchanges. In this paper, we introduce a trust-based fairness-oriented server selection policy, FairTrust, for a peer to choose another peer to interact. FairTrust takes into account both reputation and capacity factors in server selection. It helps to create a secure P2P communication environment, and meanwhile to take full advantage of system resources for high performance by fair load distribution. Simulation results show the superiority of the FairTrust policy in achieving both high security and high performance in P2P networks in comparison with other related policies.

The Peer-to-Peer network paradigm is drawing the attention of both final users and researchers for its features. P2P networks shift from the classic client-server approach to a high level of decentralization where there is no central control and all the nodes should be able not only to require services, but to provide them to other peers as well. While on one hand such high level of decentralization might lead to interesting properties like scalability and fault tolerance, on the other hand it implies many new problems to deal with. A key feature of many P2P systems is openness, meaning that everybody is potentially able to join a network with no need for subscription or payment systems. The combination of openness and lack of central control makes it feasible for a user to free-ride, that is to increase its own benefit by using services without allocating resources to satisfy other peers’ requests. One of the main goals when designing a P2P system is therefore to achieve cooperation between users. Given the nature of P2P systems based on simple local interactions of many peers having partial knowledge of the whole system, an interesting way to achieve desired properties on a system scale might consist in obtaining them as emergent properties of the many interactions occurring at local node level. Two methods are typically used to face the problem of cooperation in P2P networks: 1) engineering emergent properties when designing the protocol; 2) study the system as a game and apply Game Theory techniques, especially to find Nash Equilibria in the game and to reach them making the system stable against possible deviant behaviors. In this work we present an evolutionary framework to enforce cooperative behaviour in P2P networks that is alternative to both the methods mentioned above. Our approach is based on an evolutionary algorithm inspired by computational sociology and evolutionary game theory, consisting in having each peer periodically trying to copy another peer which is performing better. The proposed algorithms, called SLAC and SLACER, draw inspiration from tag systems originated in computational sociology, the main idea behind the algorithm consists in having low performance nodes copying high performance ones. The algorithm is run locally by every node and leads to an evolution of the network both from the topology and from the nodes’ strategy point of view. Initial tests with a simple Prisoners’ Dilemma application show how SLAC is able to bring the network to a state of high cooperation independently from the initial network conditions. Interesting results are obtained when studying the effect of cheating nodes on SLAC algorithm. In fact in some cases selfish nodes rationally exploiting the system for their own benefit can actually improve system performance from the cooperation formation point of view. The final step is to apply our results to more realistic scenarios. We put our efforts in studying and improving the BitTorrent protocol. BitTorrent was chosen not only for its popularity but because it has many points in common with SLAC and SLACER algorithms, ranging from the game theoretical inspiration (tit-for-tat-like mechanism) to the swarms topology. We discovered fairness, meant as ratio between uploaded and downloaded data, to be a weakness of the original BitTorrent protocol and we drew inspiration from the knowledge of cooperation formation and maintenance mechanism derived from the development and analysis of SLAC and SLACER, to improve fairness and tackle freeriding and cheating in BitTorrent. We produced an extension of BitTorrent called BitFair that has been evaluated through simulation and has shown the abilities of enforcing fairness and tackling free-riding and cheating nodes.

The advent of the Internet and other modern ICTs has culminated in a “global village,” where people can interact with others across the globe as if they were living next door. This ICT-enabled connectedness has brought opportunities for the creation of new forms of exchange. Companies like YouTube, Alibaba or BlaBlaCar have successfully adopted a novel way of structuring their businesses – a platform model - by shifting organizational design away from selling products towards the facilitation of exchanges between two or more (related) user groups (e.g., content creators and audience in case of Youtube, sellers and buyers for Alibaba, riders and drivers in case of BlaBlaCar). This thesis focuses on two main areas that are affected by the transformation engendered by the ICT-enabled connectedness – business and communication. First, it discusses the sharing economy as a new economic paradigm that disrupted the traditional ownership model by leveraging peer-to-peer technological platforms to facilitate the exchange of resources. While many practitioners have presaged the sharing economy to open significant opportunities for a more sustainable and open society, some experts questioned the potentially devastating future of such peer-to-peer deals, drawing particular attention to the amplified information asymmetries. Prior research has explored uncertainty as a significant source of information asymmetry, mainly in e-commerce (e.g., eBay). Focusing on the unique contextual characteristics of sharing transactions (e.g., absence of ownership transfer, service orientation and intense interaction among parties), seven papers respond to an apparent urgency for systematic and thorough scrutiny of the sources and consequences of uncertainty in this particular domain. Paper A conceptualizes uncertainty in sharing arrangements by building on information asymmetry theory and extends it from supplier and resource to collaboration. We construct and validate a theoretical model that includes the antecedents, nature, and consequences of uncertainty. Building on the fact that ambiguity can be reduced with information, Paper B investigates the effectiveness and monetary value of the information cues commonly used by sharing platforms via a discrete choice experiment methodology. Acknowledging the potentially adverse effect of such cues as negative reviews, peer-to-peer sharing platforms have readily embraced the “response” option, empowering providers with the opportunity to challenge, deny or at least apologize for the subject of critique. Leaning on communication theory, Paper C explores the impact of different response strategies and review negativity on trusting beliefs towards the provider in accommodation sharing settings. Extending this line of research, Paper D, as a practice-oriented article, highlights the implications of negative reviews on the host’s image and willingness to rent a room. Lastly, Paper E reverses the perspective and affirms the receptivity of suppliers to the cues sent from the consumer’s side. As such, it uncovers the impact of different self-presentation strategies of an applicant on the host’s decision to accept a request from a stranger on a peer-to-peer sharing platform. Second, this thesis debates the implications of the ICT-enabled connectedness in the interpersonal communication context. The pervasive use of ICTs (especially smartphones) makes a difference in the ways we maintain and develop relationships, disclose things to each other, and exchange information. Users’ attachment to their smartphones, which often serve to engage with social media, evidenced detrimental intra- and interpersonal consequences, including negative emotions like envy, anger, depression and conflicts among conversational partners. To this end, two papers of the dissertation challenge the frequently promoted euphoria regarding the permanent “connectedness.” Specifically, the phenomenon of snubbing an interlocutor when using the smartphone in his or her company, coined as “phubbing,” motivations behind this behavior and the effect on communicational outcomes in education and relationship contexts have been investigated. Paper F focuses on the academic environment and demonstrates how interruptions through ICT undermine two key learning modalities – visual and auditory attention. Paper G investigates excessive smartphone use in a romantic context. We construct and validate a conceptual model that posits ignoring a partner with the smartphone as a predictor of adverse relationship outcomes through triggering feelings of jealousy. Implications for future research and practitioners are extensively discussed for each article and recapped in the final chapter.

Most network applications nowadays are related to the distribution of contents from a source to a number of users. Several developments such as CDN and P2P networking have been used to provide access to contents at the network edges. However, they have no ability to leverage caching at the network level and still rely on a host-based communication model. Recently, Content Centric Networking (CCN) has been emerged as a new networking paradigm. In CCN, the communication focuses on the named content instead of the IP address. This paper provides a framework for enabling video streaming over CCN. We performed experiments using CCNx on the PlanetLab to explore the effects of an overlay delivery path and a chunk size to the streaming quality. Furthermore, we study the deployment of MPEG-DASH to achieve adaptive bit-rate streaming over CCN, which reveals additional challenge.

Super-peer p2p networks inherit the advantages of P2P networks, such as pooling together the shared data (documents in our system) across peers, self- organizing, and fault-tolerance. Most of the searches in p2p networks are plaintext-based and do not satisfy the pressing demands for high security in massive information sharing applications. In this paper, a super-peer p2p system is designed that supports ciphertext-based search for relevant documents. In the proposed system, a super-peer which contains all the encrypted index of documents in all the peers subordinate to it is added to the p2p system, and the indexing efficiency is improved by utilizing the indexing structure that binary trees are nested in a B+ tree. Finally, the experimental results demonstrate the proposed scheme can achieve acceptable security and efficiency.

Efficient resource discovery is a challenge aspect in P2P applications. The topological properties of P2P overlay networks are one of the important factors that dominate the performance of resource discovery. In this paper, we present an efficient P2P network model for resource discovery. Firstly, location of the underlying physical network is taken into account during the constructions of P2P networks aiming to alleviate the topoloty mismatch problem between the P2P overlay nerwork and the phisical nerwork. Furthermore, the nodes with similar resource are clustered together which can increase the chances of finding matching resources quickly. Comparing the performance of this model with other overlay networks, the results show the model of our claims can significantly shorten the latency of the searching process and reduce the searching overhead.

Live streaming applications for Video conferencing, E-learning and Telemedicine have become popular over the Internet. Peer-to-Peer network best suites the streaming architecture because p2p distributed network comprises of nodes which communicates with its neighboring nodes to make chunks available to other peer in the network environment. p2p paradigm serves as the effective solution in delivering videos with cost effective infrastructure. “Free Rider” one of the phenomenon which is identified as one of the challenge in the p2p system. These Free Riders are nothing but the node which simply resides in the p2p network, downloads the incoming streams but refuse to contribute or upload the file to other nodes. This serious threat degrades the performance of the overall system. The stream that it keeps downloading will rapidly increase leaving the system heavily loaded. On the other hand, the contributing nodes will be flooded with queries requesting for the streams. This leads to unfairness in the p2p system. This paper proposes an incentive mechanism “Do or Die” which boosts up the contributing nodes, as a sign of encouragement for their contribution, at the same time simultaneously motivates and threatens the Free Riders to contribute, else leave the network. The proposed incentive mechanism works on increment/Decrement point based scheme. Here the individual nodes are given increment points for uploading the file and decrement point for downloading the file. Hence the contributing nodes acquire high points on uploading and the Free Riders gets their points decreased as they only download the file. Hence the Free Riders can be identified and eliminated based on their decrement point. As the no of Free Riders decreases the overall system performance increases. Simulation results prove that the percentage of Free Riders reduced from 60% to 51% in the system and thereby increasing the performance and fairness of the system.

In this work, the existing Residue-class based peer-to peer (P2P) network has been considered because of its manifold advantages. Two of the most prominent advantages relevant to the present work are: (1) all peers with the same interest (or possessing same resource type) structurally form a group of diameter one, and (2) the group heads are connected in the form of a ring and the ring always remains connected even in presence of any churn. However, data look-up latency is n/2 for an n group-network. It may become substantial if n is large. To improve the latency, some topological properties of Star inter-connection network have been used to modify the existing RC based network appropriately resulting in remarkable improvement of latency.

The Device-to-Device communication (D2D) is believed to be one of promising paradigm for 5G wireless communication networks. D2D networks are based on a distributed peer to peer (P2P) network architecture where the nodes are self-organized. Moreover, as the devices are controlled by humans, their behaviours become unpredictable. Trust and reputation management (TRM) emerges as a significant research problem to tackle the security issues in D2D networks. Recently, there is a rapid growth of literature related to TRM but mainly focus their attention on trust modelling and quantification, so as to effectively detect and avoid various malicious attacks or selfish behaviours. However, there are only very limited existing works on the impact of the underlying network structure on overlay dynamic trust behaviours. The network trust behaviour is not merely a function of trust modes, but also a network-wide activity. In this paper, we propose the concept of "Network-Trustworthiness-as-a-Service" (NTaaS) - a network trustworthiness evaluation framework for the trustworthiness of the networks from global point of view. We also evaluate the evaluation framework through a thorough simulation study comparing different network topologies. The study will validate the trustworthiness evaluation framework, and thus present a guideline to evaluate network trustworthiness and network structure optimisation.

P2P is a popular networking paradigm in today’s Internet. As such, many research and development efforts are geared toward services that can be useful to the users of P2P networks. An important class of such services is that based on the publish/subscribe paradigm to allow the nodes of a network to publish data and subscribe data interests efficiently. This chapter is focused on the techniques that enable these services in P2P networks.

The anonymity and dynamic characters of p2p (peer to peer) system makes p2p become hotbed with selfish and vicious action. In order to solve the problem of node cheating caused by high dynamic property of P2P, this paper designs a security trust model -- BGTR based on the idea of probability model and fuzzy mathematics. The scheme adopts a new query method that the inquiry peer initiates the calculated trust value of the queried peer, and combines it with the global trust value calculated at the same time by the queried peer. The theory of uncertainty is also introduced into three aspects of the model to make more adaptive to real situation, which includes Bayesian distinguish model, the triad of trust value, and the transaction times and interval of time. The BGTR model has been tested in effectiveness of transaction success rate caused by the risk index, and in the properties of anti-interference. The P2P network with BGTR trust model has proved higher security and has better performance to resolve problems of impostors, malicious collective attacks and free-rides.

We proposes a hybrid architecture MTreeTV which combines the use of P2P networks with dedicated streaming servers, and was proposed to build on the advantages of both P2P and CDN paradigms. MTreeTV uses a novel locality-aware overlay construction and scheduling algorithm to assure the effectiveness of P2P overlay. We also study the placement of the servers with constraints on the client to server paths and evaluate the effect of the server parameters. Through analysis and simulation, we show that MTreeTV supports fast channel switching (<4s).

In recent years, a paradigm shift in neuroscience has been occurring from "localizationism," or the idea that the brain is organized into separately functioning modules, toward "connectomics," or the idea that interconnected nodes form networks as the underlying substrates of behavior and thought. Accordingly, our understanding of mechanisms of neurological function, dysfunction, and recovery has evolved to include connections, disconnections, and reconnections. Brain tumors provide a unique opportunity to probe large-scale neural networks with focal and sometimes reversible lesions, allowing neuroscientists the unique opportunity to directly test newly formed hypotheses about underlying brain structural-functional relationships and network properties. Moreover, if a more complete model of neurological dysfunction is to be defined as a "disconnectome," potential avenues for recovery might be mapped through a "reconnectome." Such insight may open the door to novel therapeutic approaches where previous attempts have failed. In this review, we briefly delve into the most clinically relevant neural networks and brain mapping techniques, and we examine how they are being applied to modern neurosurgical brain tumor practices. We then explore how brain tumors might teach us more about mechanisms of global brain dysfunction and recovery through pre- and postoperative longitudinal connectomic and behavioral analyses.