本文關(guān)鍵詞：移動P2P環(huán)境下信任模型與激勵機制的研究　出處：《南京郵電大學》2015年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 移動P2P 社會信任 模糊Q學習 進化博弈 激勵機制

【摘要】：由于移動P2P(Peer to Peer)網(wǎng)絡(luò)中節(jié)點固有的屬性,以及移動P2P網(wǎng)絡(luò)的拓撲結(jié)構(gòu)隨著節(jié)點移動而動態(tài)變化的特性,使得移動P2P網(wǎng)絡(luò)面臨著嚴重的安全問題,例如傳播惡意虛假文件,濫用網(wǎng)絡(luò)資源等。同時由于移動P2P網(wǎng)絡(luò)中節(jié)點的自私性,節(jié)點不合作的情況較為普遍。構(gòu)建合理的信任模型能夠有效的保障節(jié)點之間的交易安全,建立激勵機制則能有效的解決P2P網(wǎng)絡(luò)中的節(jié)點的自私行為。本文首先提出基于社會信任補充的信任模型,該模型針對移動P2P網(wǎng)絡(luò)的中信任信息易缺失的情形,使用社會信任作為補充,社會信任的主要優(yōu)勢在于不需要節(jié)點之間有過交易便可以從社會關(guān)系的角度對節(jié)點是否可信做出評價。而當信任信息可以獲得時,則利用傳統(tǒng)的綜合信任值計算方法:α*直接信任值+β*推薦信任值。在計算推薦信任值時,本文綜合節(jié)點之間的交易次數(shù)和交易滿意次數(shù)得到推薦信任值,而不是直接綜合推薦信息,以免出現(xiàn)由于交易次數(shù)過少導(dǎo)致的評價不穩(wěn)定情況;同時采用三維向量描述節(jié)點之間關(guān)系,通過計算余弦相似度篩選出可信推薦節(jié)點。本文中基于模糊Q學習的激勵機制針對節(jié)點的不同身份,將不同的節(jié)點相關(guān)參數(shù)作為輸入狀態(tài)變量;依據(jù)狀態(tài)和動作模糊集構(gòu)建初始模糊規(guī)則庫;設(shè)計即時收益回報函數(shù);描述了基于模糊Q學習的進化博弈具體過程;最后從不同的角度進行仿真,證明了此機制具有較快的Q值收斂速度,并且能夠有效的激勵節(jié)點積極參與到網(wǎng)絡(luò)活動中。
[Abstract]:The mobile P2P (Peer to Peer) attribute node in the network of natural, as well as the topology of mobile P2P networks with mobile nodes and the dynamic change characteristics of the mobile P2P network is facing serious security problems, such as the spread of malicious false documents, the abuse of cyber source. At the same time due to the mobility of nodes in P2P network selfishness without the cooperation of nodes is more common. The construction of trust model is reasonable and can effectively guarantee the node between the transaction security, the establishment of incentive mechanism can effectively solve the P2P node in the network. This paper proposes the selfish behavior of social trust model based on the model for mobile P2P network trust information missing the use of social trust, social trust as a supplement, the main advantage is that you do not need to have the transaction between nodes can be from the perspective of social relations of the festival is No credible make evaluation. And when the trust information can be obtained when using the traditional comprehensive trust value calculation method: alpha + beta * * direct trust value recommendation trust value. In the calculation of recommended trust value, the comprehensive satisfaction between node number of transactions and transaction times are recommended trust value, rather than directly integrated information recommendation in order to avoid the evaluation of the number of transactions, leading to too little instability; at the same time using the three-dimensional vector to describe the relationship between nodes, by calculating the cosine similarity of selected credible recommender. The incentive mechanism of fuzzy Q learning based on node identity, the nodes associated with different parameters as input variables; on the basis of state and action fuzzy set fuzzy rules of initial design; instant return function; describes the specific process of evolutionary game based on fuzzy Q learning; finally, from different angles The degree of simulation proves that this mechanism has a fast convergence rate of Q value and can effectively encourage nodes to actively participate in the network activity.

【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP393.02

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本文編號：1410532