Distributed landmark placement in P2P networks

Peer-to-peer (P2P) file distribution is a scalable way to disseminate content to a wide audience. For a P2P network, one fundamental performance metric is the average time needed to deliver a certain file to all peers, which in general depends on the topology of the network and the scheduling of transmissions. Despite its apparent importance, how to minimize average finish time remains an open question even for a fully- connected network. This is mainly due to the analytical challenges that come with the combinatorial structures of the problem. In this paper, by using the water-filling technique, we determine how each peer should use its capacity to sequentially minimize the file download times in an upload-constrained P2P network. Furthermore, it is argued that this scheduling also potentially minimizes average finish time for the network. This result not only provides fundamental insight to scheduling in such P2P systems, but also can serve as a benchmark to evaluate practical algorithms and illustrate the scalability of P2P networks.

Internet worms pose a serious and ongoing threat to system security, often resulting in significant service downtime and disruption. In recent years peer-to-peer (p2p) networks have become a target for the deployment of worms as their high connectivity allows for rapid dissemination and homogeneity of the adopted software platform ensures the existence of common susceptibilities. In this paper we observe that peer similarity in p2p networks can greatly increase overall vulnerability; peers with largely different system characteristics are unlikely to be infected by the same worm. With this in mind we present di-jest - an autonomic method for neighbour selection based on heterogeneity. Our results show the efficacy of di-jest in reducing the spread rate and potency of p2p worms. By selecting neighbours with different system characteristics di-jest can reduce the number of peers infected by a worm by up to 80%.

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.

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

Morphometric analysis of anatomical landmarks allows researchers to identify specific morphological differences between natural populations or experimental groups, but manually identifying landmarks is time-consuming. We compare manually and automatically generated adult mouse skull landmarks and subsequent morphometric analyses to elucidate how switching from manual to automated landmarking will impact morphometric analysis results for large mouse (Mus musculus) samples (n=1205) that represent a wide range of 'normal' phenotypic variation (62 genotypes). Other studies have suggested that the use of automated landmarking methods is feasible, but this study is the first to compare the utility of current automated approaches to manual landmarking for a large dataset that allows the quantification of intra- and inter-strain variation. With this unique sample, we investigated how switching to a non-linear image registration-based automated landmarking method impacts estimated differences in genotype mean shape and shape variance-covariance structure. In addition, we tested whether an initial registration of specimen images to genotype-specific averages improves automatic landmark identification accuracy. Our results indicated that automated landmark placement was significantly different than manual landmark placement but that estimated skull shape covariation was correlated across methods. The addition of a preliminary genotype-specific registration step as part of a two-level procedure did not substantially improve on the accuracy of one-level automatic landmark placement. The landmarks with the lowest automatic landmark accuracy are found in locations with poor image registration alignment. The most serious outliers within morphometric analysis of automated landmarks displayed instances of stochastic image registration error that are likely representative of errors common when applying image registration methods to micro-computed tomography datasets that were initially collected with manual landmarking in mind. Additional efforts during specimen preparation and image acquisition can help reduce the number of registration errors and improve registration results. A reduction in skull shape variance estimates were noted for automated landmarking methods compared with manual landmarking. This partially reflects an underestimation of more extreme genotype shapes and loss of biological signal, but largely represents the fact that automated methods do not suffer from intra-observer landmarking error. For appropriate samples and research questions, our image registration-based automated landmarking method can eliminate the time required for manual landmarking and have a similar power to identify shape differences between inbred mouse genotypes.

In this study, we present WebCeph2k, an extensive and diverse cephalometric landmark localization dataset that surpasses previous benchmark datasets in terms of number of landmark annotations. This diverse cephalometric landmarks dataset has significant value in medical imaging research. Existing studies predominantly focus on datasets obtained from a single medical center and provider, which offers a limited number of landmarks and a limited diversity of cephalograms, resulting in models that exhibit low robustness and generalization when applied to more diverse datasets. The clinical application of cephalometry is hampered by significant localization errors in landmark localization models, in addition to the inadequacy of existing datasets’ landmarks for clinical cephalometric diagnosis. The limited generalization ability and the occurrence of “overfitting” in deep learning models are mainly caused by the small size in the dataset. In the medical field, the inclusion of large and diverse datasets can greatly improve the generalization and performance of landmark localization models. This paper presents our WebCeph2k dataset from 9 medical centers, covering 9 different imaging devices, which surpasses the only publicly available ISBI2015 dataset in terms of sample size and number of landmarks. In addition, this study employs a low computational cost methodology to achieve optimal landmarks localization: 1) ROI regions of X-ray images are derived by exploiting the prior distribution of the data, 2) the model computational cost is reduced by adopting a spatial-depth transformation strategy, 3) the standard heatmap decoding method is optimized by integrating a compensation strategy. The results show that the proposed method not only achieves competitive localization results to other state-of-the-art approaches, but also offers a reduction of the model computational cost, resulting in faster inference. Consequently, this research offers valuable prospects in the field of general-purpose medical landmark localization methods. We also find that our proposed dataset is more complex and challenging than the ISBI dataset. The dataset and code are available at https://github.com/switch626/WebCeph2k.

Peer to peer is an important way for files sharing. In existing P2P systems, users need to query and retrieve files, and have to know some knowledge, such as file names, keywords or hash values, about those files before searching. However, files on the Internet are not only abundant but also mutable. It's rather difficult for users to determine how, and at what frequency to query. This paper presents a P2P system named Active P2P, which is different from current P2P systems in that documents will automatically come to peer's storage space users shared after user install this system and connect it to the P2P network. In Active P2P system, users can get the most important files on the Internet without querying, which keeps the difficulties of files locating and searching away from users. To achieve this purpose, the basic idea of this paper is to make the naturally passive files into logically active ones which can compete and cooperate with each other. Winners in the competition can reproduce themselves and possess storage space of more peers while the losers will be died out at a higher probability.

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users' privacy. Many implementations of CF and PPCF techniques proposed so far are centralized. In centralized systems, data is collected and stored by a central server for CF purposes. Centralized storage poses several hazards to users because the central server controls users' data. In this work, we investigate how to produce naïve Bayesian classifier (NBC)-based recommendations while preserving users' privacy without using a central server. In a community of people, users might create a peer-to-peer (P2P) network. Through P2P network, users can communicate with each other and exchange data to produce predictions. We share the workload of prediction process and offer referrals efficiently using P2P network. We propose privacy-preserving schemes and analyze them in terms of accuracy, privacy, and efficiency. Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.

Availability of applications or devices is known to be one of the most critical variables impacting the performances of software systems. We study in this article the problem of finding peers matching a given availability pattern in a peer-to-peer (P2P) system. Motivated by practical examples, we specify two formal problems of availability matching that arise in real applications: disconnection matching , where peers look for partners expected to disconnect at the same time, and presence matching , where peers look for partners expected to be online simultaneously in the future. As a scalable and inexpensive solution, we propose to use epidemic protocols for topology management; we provide corresponding metrics for both matching problems. We evaluated this solution by simulating two P2P applications, task scheduling and file storage , over a new trace of the eDonkey network, the largest one with availability information. We first proved the existence of regularity patterns in the sessions of 14M peers over 27 days. We also showed that, using only 7 days of history, a simple predictor could select predictable peers and successfully predicted their online periods for the next week. Finally, simulations showed that our simple solution provided good partners fast enough to match the needs of both applications, and that consequently, these applications performed as efficiently at a much lower cost. This solution is purely distributed as it does not rely on any central server or oracle to operate. We believe that this work will be useful for many P2P applications for which it has been shown that choosing good partners, based on their availability, drastically improves their performance and stability.

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.

Peer-to-peer (P2P) video streaming over the Internet plays an important role in reshaping today's Internet traffic. The performance of a P2P system is typically bottlenecked by the limited upload bandwidth of the participating peers, which typically have asymmetric upload/download bandwidth connections to the Internet. Increasing the streaming bitrate in P2P systems beyond what is sustainable with participating peers' upload bandwidths therefore places extra burden on the content server, clearly not a desirable solution in terms of scalability. Motivated by this, we explore here an alternative architecture based on the notion of "helpers", who are online users with spare upload capacity. We show how efficient utilization of helpers' spare upload capacity can lead to significantly improved streaming bitrate in a P2P network without incurring additional server burden. For instance, a 2000 node P2P system with an average upload bandwidth of 512 kbps can sustain streaming rate of 512 kbps with very little server usage. But with an additional 533 helpers, the system can stream at 640 kbps without incurring any additional server load.

Landmark and Measurement-Based Data Assistant (LAMbDA)

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.

Peer-to-peer based applications are coming, such as instant messaging, file sharing and video streaming. With the maturation of P2P technology there is larger and larger number of users. But P2P network's characteristics make itself a potential vehicle for the active worms to achieve fast worm propagation. So it is important to study the worm's spreading model and relative countermeasure under P2P systems. P2P worm's new characteristics are analyzed and a simple model is proposed in this paper. A novel method P2P worm evolution observer (PWO) is also described, this method introduces some observers in the P2P network to monitor some nodes' infected during the worm's spreading period, and traces the worm's infected origin and attacking paths under the global P2P network topology. Theory and simulation results show that this method can gain nearly 100% probability to trace the worm evolution, even when only less than 1% of the nodes are deployed with observers.

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.