Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

Mathematical Model Validation of Search Protocols in MP2P Networks

Broadcasting is a basic technique in Mobile ad-hoc network(MANET),and it refers to sending a packet from one node to every other node within the transmission range.Flooding is a type of broadcast where the received packet is retransmitted once by every node.The naive flooding technique,floods the network with query messages,while the random walk technique operates by contacting the subsets of every node’s neighbors at each step,thereby restricting the search space.One of the key challenges in an ad-hoc network is the resource or content discovery problem which is about locating the queried resource.Many earlier works have mainly focused on the simulation-based analysis of flooding,and its variants under a wired network.Although,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of P2P systems running over MANET.In this paper,we describe how P2P resource discovery protocols perform badly over MANETs.To address the limitations,we propose a new protocol named ABRW(Address Broadcast Random Walk),which is a lightweight search approach,designed considering the underlay topology aimed to better suit the unstructured architecture.We provide the mathematical model,measuring the performance of our proposed search scheme with different widely popular benchmarked search techniques.Further,we also derive three relevant search performance metrics,i.e.,mean no.of steps needed to find a resource,the probability of finding a resource,and the mean no.of message overhead.We validated the analytical expressions through simulations.The simulation results closely matched with our analyticalmodel,justifying our findings.Our proposed search algorithm under such highly dynamic self-evolving networks performed better,as it reduced the search latency,decreased the overall message overhead,and still equally had a good success rate.

Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network

Deep Reinforcement Learning(DRL)is a class of Machine Learning(ML)that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently.DRL has been used in many application fields,including games,robots,networks,etc.for creating autonomous systems that improve themselves with experience.It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially.Therefore,a novel query routing approach called Deep Reinforcement Learning based Route Selection(DRLRS)is proposed for unstructured P2P networks based on a Deep Q-Learning algorithm.The main objective of this approach is to achieve better retrieval effectiveness with reduced searching cost by less number of connected peers,exchangedmessages,and reduced time.The simulation results shows a significantly improve searching a resource with compression to k-Random Walker and Directed BFS.Here,retrieval effectiveness,search cost in terms of connected peers,and average overhead are 1.28,106,149,respectively.

Greedysearch based service location in P2P networks

A model is built to analyze the performance of service location based on greedy search in P2P networks. Hops and relative QoS index of the node found in a service location process are used to evaluate the performance as well as the probability of locating the top 5% nodes with highest QoS level. Both model and simulation results show that, the performance of greedy search based service location improves significantly with the increase of the average degree of the network. It is found that, if changes of both overlay topology and QoS level of nodes can be ignored during a location process, greedy-search based service location has high probability of finding the nodes with relatively high QoS in small number of hops in a big overlay network. Model extension under arbitrary network degree distribution is also studied.

Deadline-aware network coding for video on demand service over P2P networks

We are interested in providing Video-on-Demand (VoD) streaming service to a large population of clients using peer-to-peer (P2P) approach. Given the asynchronous demands from multiple clients, continuously changing of the buffered contents, and the continuous video display requirement, how to collaborate with potential partners to get expected data for future content delivery are very important and challenging. In this paper, we develop a novel scheduling algorithm based on deadline- aware network coding (DNC) to fully exploit the network resource for efficient VoD service. DNC generalizes the existing net- work coding (NC) paradigm, an elegant solution for ubiquitous data distribution. Yet, with deadline awareness, DNC improves the network throughput and meanwhile avoid missing the play deadline in high probability, which is a major deficiency of the con- ventional NC. Extensive simulation results demonstrated that DNC achieves high streaming continuity even in tight network conditions.

METrust: A Mutual Evaluation-based Trust Model for P2P Networks

It is necessary to construct an effective trust model to build trust relationship between peers in peer-to-peer （P2P） network and enhance the security and reliability of P2P systems. The current trust models only focus on the consumers＇ evaluation to a transaction, which may be abused by malicious peers to exaggerate or slander the provider deliberately. In this paper, we propose a novel trust model based on mutual evaluation, called METrust, to suppress the peers＇ malicious behavior, such as dishonest evaluation and strategic attack. METrust considers the factors including mutual evaluation, similarity risk, time window, incentive, and punishment mechanism. The trust value is composed of the direct trust value and the recommendation trust value. In order to inhibit dishonest evaluation, both participants should give evaluation information based on peers＇ own experiences about the transaction while computing the direct trust value. In view of this, the mutual evaluation consistency factor and its time decay function are proposed. Besides, to reduce the risk of computing the recommendation trust based on the recommendations of friend peers, the similarity risk is introduced to measure the uncertainty of the similarity computing, while similarity is used to measure credibility. The experimental results show that METrust is effective, and it has advantages in the inhibition of the various malicious behaviors.

INCENTIVE MECHANISMS FOR TRUSTWORTHY ROUTING BASED ON GAME MODEL OF THE STRATEGIES OF NODES IN P2P NETWORKS

The trustworthiness and security of routing in the existing Peer-to-Peer (P2P) networks can not be ensured because of the diversity of the strategies of P2P nodes. This paper firstly uses game theory to establish game model of the strategies and profits of various types of routing nodes. Then,two incentive mechanisms for the corresponding stages of P2P trustworthy routing are proposed,namely trust associated mechanism and trust compensated mechanism. Simulation results show that the incentive mechanisms proposed in this paper will encourage cooperation actions of good nodes and restrain malicious actions of bad nodes,which ensure the trustworthiness of routing consequently.

A Scalable and Robust DHT Protocol for Structured P2P Network

Distributed Hash Tables (DHTs) were originated from the design of structured peer-to-peer (P2P) systems. A DHT provides a key-based lookup service similar to a hash table. In this paper, we present the detailed design of a new DHT protocol, Tambour. The novelty of the protocol is that it uses parallel lookup to reduce retrive latency and bounds communication overhead to a dynamically adjusted routing table. Tambour estimates the probabilities of routing entries' liveness based on statistics of node lifetime history and evicts dead entries after lookup failures. When the network is unstable, more routing entries will be evicted in a given period of time, and the routing tables will be getting smaller which minimize the number of timeouts for later lookup requests. An experimental prototype of Tambour has been simulated and compared against two popular DHT protocols. Results show that Tambour outperforms the compared systems in terms of bandwith cost, lookup latency and the overall efficiency.

FPSblo:A Blockchain Network Transmission Model Utilizing Farthest Point Sampling

Peer-to-peer(P2P)overlay networks provide message transmission capabilities for blockchain systems.Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems.However,traditional blockchain P2P networks face a common challenge where there is often a mismatch between the upper-layer traffic requirements and the underlying physical network topology.This mismatch results in redundant data transmission and inefficient routing,severely constraining the scalability of blockchain systems.To address these pressing issues,we propose FPSblo,an efficient transmission method for blockchain networks.Our inspiration for FPSblo stems from the Farthest Point Sampling(FPS)algorithm,a well-established technique widely utilized in point cloud image processing.In this work,we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritize message forwarding so that messages reach the network edge quickly and are evenly distributed.Moreover,we compare our model with the Kadcast transmission model,which is a classic improvement model for blockchain P2P transmission networks,the experimental findings show that the FPSblo model reduces 34.8%of transmission redundancy and reduces the overload rate by 37.6%.By conducting experimental analysis,the FPS-BT model enhances the transmission capabilities of the P2P network in blockchain.

A Semantic Searching Scheme in Heterogeneous Unstructured P2P Networks

Semantic-based searching in peer-to-peer （P2P） networks has drawn significant attention recently. A number of semantic searching schemes, such as GES proposed by Zhu Y et al., employ search models in Information Retrieval （IR）. All these IR-based schemes use one vector to summarize semantic contents of all documents on a single node. For example, GES derives a node vector based on the IR model： VSM （Vector Space Model）. A topology adaptation algorithm and a search protocol are then designed according to the similarity between node vectors of different nodes. Although the single semantic vector is suitable when the distribution of documents in each node is uniform, it may not be efficient when the distribution is diverse. When there are many categories of documents at each node, the node vector representation may be inaccurate. We extend the idea of GES and present a new class-based semantic searching scheme （CSS） specifically designed for unstructured P2P networks with heterogeneous single-node document collection. It makes use of a state-of-the-art data clustering algorithm, online spherical k-means clustering （OSKM）, to cluster all documents on a node into several classes. Each class can be viewed as a virtual node. Virtual nodes are connected through virtual links. As a result, the class vector replaces the node vector and plays an important role in the class-based topology adaptation and search process. This makes CSS very efficient. Our simulation using the IR benchmark TREC collection demonstrates that CSS outperforms GES in terms of higher recall, higher precision, and lower search cost.

A Comprehensive and Adaptive Trust Model for Large-Scale P2P Networks

Based on human psychological cognitive behavior, a Comprehensive and Adaptive Trust （CAT） model for largescale P2P networks is proposed. Firstly, an adaptive trusted decision-making method based on HEW （Historical Evidences Window） is proposed, which can not only reduce the risk and improve system efficiency, but also solve the trust forecasting problem when the direct evidences are insufficient. Then, direct trust computing method based on IOWA （Induced Ordered Weighted Averaging） operator and feedback trust converging mechanism based on DTT （Direct Trust Tree） are set up, which makes the model have a better scalability than previous studies. At the same time, two new parameters, confidence factor and feedback factor, are introduced to assign the weights to direct trust and feedback trust adaptively, which overcomes the shortage of traditional method, in which the weights are assigned by subjective ways. Simulation results show that, compared to the existing approaches, the proposed model has remarkable enhancements in the accuracy of trust decision-making and has a better dynamic adaptation capability in handling various dynamic behaviors of peers.

SKIP: an efficient search mechanism in unstructured P2P networks

Search efficiency and accuracy of resource are important considerations for search algorithm in peer-to-peer （P2P） network.Most search algorithms use flooding among neighbor nodes to search relevant resource.However,this usually causes great amount of redundant messages,which results in high search costs and low search precision.In this paper,we use vector space model （VSM） and relevance ranking algorithms to construct overlay network,and a novel search mechanism search with K-iteration preference （SKIP） based on semantic group for P2P networks is proposed to efficiently solve these problems.The key idea of SKIP is to reorder the semantic neighbors of nodes according to relevant scores and to utilize preference selection during the process of query.We analysis and implement the scheme and reveal that the SKIP provides a low overhead on topology maintenance,which can be effectively used in P2P searching and verify it outperformance in higher precision and lower search cost by comparing with current semantic-based searching mechanism gnutella-like efficient searching system （GES）.

Modeling and analysis of anti-worm in P2P networks

Anti-worm is an effective way to fight against malicious worm and has been followed closely by malicious worm researchers recently. However, active and passive confronting technologies in peer-to-peer （P2P） networks have not been studied in depth. This paper introduces both of them to fight against malicious worm in P2P networks. To study their effectiveness in P2P networks, this paper takes the topology degree in P2P networks into consideration and puts forward a four-state propagation model for active anti-worm and a five-state propagation model for passive anti-worm respectively. Both of the models are simplified in the case that size of a P2P network is large enough. The simulation results have not only validated the effectiveness of our propagation models but also evaluated the excellent performance of both active anti-worm and passive anti-worm.

基于国密的P2P数据开放共享平台设计与实现

在数字化时代,海量的数据在流转中才能创造更多的价值,但在挖掘信息价值的同时也会带来各种数据泄露的风险。如何有效地进行数据安全防控已成为国家、单位和个人所面临的严峻挑战。为此,文中设计一种基于国密的P2P数据开放共享平台。该平台通过P2P模式进行组网,满足网络节点之间自愿发布共享数据、平等存储共享数据、点对点传输共享数据的需求。与传统的C/S模式相比,所设计平台具有去中心化、扩展性强、维护成本低等优势,且通过采用混合国密算法方案,在传输过程中能够保障数据的机密性、完整性及不可否认性。