The incentive secure mechanism based on quality of service in P2P network

Liu, Yuhua; Li, Yuling; Xiong, Naixue; Park, Jong Hyuk; Lee, Yang Sun

doi:10.1016/j.camwa.2009.12.037

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…In this section we provide the experimental results to benchmark the performance of bandwidth allocation based on our SGIM, compared to even sharing mechanism (ESM) [15], a commonly used schemas for resource allocation. Because of the limitation of objective conditions, we can not do our simulations on a large scale on Internet but test our model with the network simulator to simulate the incentive processes for allocating bandwidth.…”

Section: Experimental Simulation and Results Analysismentioning

confidence: 99%

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A Fair allocation and sharing incentive approach in P2P network based on supervising game

Tao

Zhang

Zhong

et al. 2010

IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010)

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Due to voluntary participation, and lack of incentives or restrictions, we cannot make sure every peer in P2P networks adopts and abides by the assumptions that all peers contribute their resources willingly, then emerge these serious problems such as free-riding, unfair allocation etc.. Therefore, in this paper we bring in the ideas of supervising game in Economics, propose an adaptive incentive approach (SGIM) to allocate resources fairly and boost peers do more contribution, as will construct its mathematical model and use the notions of Nash Equilibrium to analyze the strategic choices by different peers. The experiments' results indicate that our paper's main contributions are: (1) fairly allocate the bandwidth of service providers with the idea of distribution according to work; (2) the free-riding and fair sharing problems can be solved effectively to some extent; (3) the whole system can be evolve into a "good" and "balanced" states from the view of game theory.

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Section: Experimental Simulation and Results Analysismentioning

confidence: 99%

“…Reference [6][7] first introduce game theory into P2P network, and [3] propose a game theoretic framework for incentives in P2P network. These related works provide a reference to start our work [11][14] [15].…”

Section: Introductionmentioning

confidence: 99%

A Fair allocation and sharing incentive approach in P2P network based on supervising game

Tao

Zhang

Zhong

et al. 2010

IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010)

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“…In addition, Liu et al (2010) proposed an incentive mechanism based on QoS in order to solve the free-riding problem. He proposed a strategy, which can guarantee QoS while suppressing free riders.…”

Section: Related Workmentioning

confidence: 99%

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

Yin¹,

Wang²,

Dong³

et al. 2013

Expert Systems with Applications

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“…Li's model [7] is based on a balance mechanism which considers just the contribution of peers, selfish peers can still get some reputation under the comprehensive performance, so the existence of selfish peers is allowed and stabilizes at a percent around 24. Liu's model [8] constrains selfish peers' ability to download resources from the network, so the percent of selfish peers stabilizes at 17. Our model considers a peer's action such as its contribution and service quality in a comprehensive way.…”

Section: The Algorithm Of the Gamementioning

confidence: 99%

“…2 shows the overall utility of P2P networks with different models. They are conventional model without using any mechanism, Roberto's model [9] based on transaction and Liu's model [8] based on quality of service. We set eighty-five percent of peers as selfish peers, ten percent as cooperate peers, and the other five percent as other peers according to [1].…”

Section: The Algorithm Of the Gamementioning

confidence: 99%

An incentive mechanism based on the Bayes Equilibrium of game theory in peer-to-peer networks

Yao

Wang

Sun

et al. 2011

2011 International Conference on Electronics, Communications and Control (ICECC)

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The free-riding problem is widely accepted to be retarding the deployment and growth of P2P networks. In order to solve this problem, we give a strict definition of the peer's contribution, utility and incentive reputation and model a P2P network with heterogeneous peers as an incomplete information static game. We use incentive reputation to incentivize peers to share their resources and provide services to others, for the services available to the peer are directly depend on its current incentive reputation. A peer can enhance its reputation by increasing its contribution. Experimental results show that all peers optimize their strategy to the Bayes Equilibrium in which the utility of each peer's is maximized and the free-riding problem is minimized, then the whole network serves at a high quality.

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The incentive secure mechanism based on quality of service in P2P network

Cited by 8 publications

References 16 publications

A Fair allocation and sharing incentive approach in P2P network based on supervising game

A Fair allocation and sharing incentive approach in P2P network based on supervising game

Wright–Fisher multi-strategy trust evolution model with white noise for Internetware

An incentive mechanism based on the Bayes Equilibrium of game theory in peer-to-peer networks

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