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.

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.

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.

A Model of Mobile P2P Network Architecture based on Agent

A model of mobile peer to peer (P2P) network architecture based on agent for mobile value-added services is proposed in this paper. By using this model, wireless devices can build a P2P network dynamically, and then they can access and transmit information peer to peer. An ontologybased semantic information description method is also proposed for the mobile wireless devices. The merit of this method is to enhance search efficiency. Moreover, a small system, called CellChat, has been implemented based on this model in the lab environment. Experimental results are provided to show that the benefi ts of the proposed method. This method will become an effective solution for mobile value-added services in 3G network.

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.

Improving Resilience against DDoS Attack in Unstructured P2P Networks

In unstructured peer-to-peer （P2P） systems such as Gnutella, a general routing search algorithm is used to blindly flood a query through network among peers. But unfortunately, malicious nodes could easily make use of the search approach launching distributed denial of service （DDoS） attack which aims at the whole network. In order to alleviate or minimize the bad effect due to behavior of malicious nodes using the flooding search mechanism, the paper proposes a Markov-based evaluation model which exerts the trust and reputation mechanism to computing the level of trustworthy of nodes having the information requested by evaluation of the nodes＇ history behavior. Moreover, it can differentiate malicious nodes as early as possible for isolating and controlling the ones＇ message transmitted. The simulation results of the algorithm proposed show that it could effectively isolate malicious nodes, and hold back the transmission of vicious messages so that it could enhance tolerance of DDoS based on flooding in Guutella-like P2P network.

A Routing Algorithm for Risk-Scanning Agents Using Ant Colony Algorithm in P2P Network

This paper describes a routing algorithm for risk scanning agents using ant colony algorithm in P2P（peerto peer） network. Every peer in the P2P network is capable of updating its routing table in a real-time way, which enables agents to dynamically and automatically select, according to current traffic condition of the network, the global optimal traversal path. An adjusting mechanism is given to adjust the routing table when peers join or leave. By means of exchanging pheromone intensity of part of paths, the algorithm provides agents with more choices as to which one to move and avoids prematurely reaching local optimal path. And parameters of the algorithm are determined by lots of simulation testing. And we also compare with other routing algorithms in unstructured P2P network in the end.

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.

Caching Scheme Based on Power-Law Characteristic in Unstructured P2P Networks

In order to reduce the traffic load and improve the availability of the shared resources in unstructured P2P networks, a caching scheme combining alternative index and adaptive replication （AIAR） is presented. AIAR uses random walk mechanism to disperse the caching information of resources in the network based on its power-law characteristic, and dynamically adjusts replicas according to the visit frequency on resources and the degree information of peers. Subsequent experimental results show that the proposed AIAR scheme is beneficial to improve the search performance of success rate and respond speed. In addition, compared to some existing caching scheme, AIAR can perform much better in success rate, especially in a dynamic environment.

Research on Video on Demand Based on P2P Network

A P2P approaches to extend the ability of Video on Demand systems to serve more users. In the proposed system users share with each other the media data obtained and the media server is no longer the only source to get data from, thereby, the load on the media server could be greatly alleviated and the overall system capacity increases and more users could be served. The P2P streaming system introduces efficient searching;data transfer dynamically monitoring and initial buffering to maintain a high quality of playback. Its provider selection policy helps to reduce the load of the underlying network by avoiding remote data transfer.

A Novel Digital Rights Management Scheme in P2P Networks

P2P networking is a distributed application architecture that partitions tasks or workloads between peers. How to integrate P2P networks and DRM to offer a novel content distribution mode for digital media resources is a significant research project. In this paper, a novel DRM architecture in P2P Networks is proposed, three phases include content provide phase, content purchase phase and content access phase, are modeled, and key technologies are introduced. Finally analysis indicates that the proposed scheme has the characteristics of security, controllability and scalability.

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.