本文選題：物聯(lián)網(wǎng)　切入點(diǎn)：大數(shù)據(jù)　出處：《中國科學(xué)技術(shù)大學(xué)學(xué)報(bào)》2017年04期

【摘要】：為了解決物聯(lián)網(wǎng)大數(shù)據(jù)源頭的可靠問題,給出一種基于感知源信任評(píng)價(jià)的物聯(lián)網(wǎng)數(shù)據(jù)可靠保障的模型.模型首先構(gòu)建感知層評(píng)測單元,每個(gè)評(píng)測單元均包括工作節(jié)點(diǎn)、伴生節(jié)點(diǎn)和判決節(jié)點(diǎn)三種類別.感知同種指標(biāo)的工作節(jié)點(diǎn)之間可互相作為對(duì)方信任值計(jì)算的依據(jù);伴生節(jié)點(diǎn)和判決節(jié)點(diǎn)均用于對(duì)工作節(jié)點(diǎn)的狀態(tài)進(jìn)行監(jiān)測,伴生節(jié)點(diǎn)用于對(duì)工作節(jié)點(diǎn)的數(shù)據(jù)進(jìn)行定期的驗(yàn)證,從而確定工作節(jié)點(diǎn)的狀態(tài);判決節(jié)點(diǎn)則是當(dāng)工作節(jié)點(diǎn)出現(xiàn)疑似異常,無法最終確定時(shí),被動(dòng)啟用以作為最終的判斷結(jié)果.通過以上三種節(jié)點(diǎn)的數(shù)據(jù)收集和驗(yàn)證給出了一種用于節(jié)點(diǎn)可靠度計(jì)算、調(diào)整的方法,以此獲得每個(gè)工作節(jié)點(diǎn)的信任值.然后在給定閾值的情況下,構(gòu)建信任列表,剔除不可信的感知節(jié)點(diǎn),只傳輸和處理可信節(jié)點(diǎn)所感知的數(shù)據(jù).同時(shí)為了保證感知節(jié)點(diǎn)的初始可靠,引入接入認(rèn)證機(jī)制.從理論分析和仿真的結(jié)果看,該模型具有節(jié)點(diǎn)感知數(shù)據(jù)可靠、靈活可擴(kuò)展等特點(diǎn),能夠有效提高物聯(lián)網(wǎng)大數(shù)據(jù)源頭的可靠性.
[Abstract]:In order to solve the reliability problem of big data's source, this paper presents a model to guarantee the reliability of Internet of things data based on the trust evaluation of perceptual sources. Firstly, the perceptual layer evaluation unit is constructed, and each evaluation unit includes working nodes. The working nodes of the same index can be used as the basis for the calculation of the trust value of the other side, and the associated nodes and the decision nodes are used to monitor the status of the working nodes, the three categories of the associated nodes and the decision nodes are used to monitor the status of the working nodes. The associated node is used to verify the data of the working node periodically to determine the status of the working node, and the decision node is used when the working node is suspected to be abnormal and can not be determined. Through the data collection and verification of the above three kinds of nodes, a method for calculating and adjusting the reliability of the nodes is presented. In order to get the trust value of each working node. Then, under the given threshold, we build a trust list and remove the untrusted perceptual nodes. Only the data perceived by trusted nodes are transmitted and processed. In order to ensure the initial reliability of the perceived nodes, an access authentication mechanism is introduced. The theoretical analysis and simulation results show that the model has reliable perceptual data. Flexible and extensible features, can effectively improve the source of the Internet of things big data reliability.
【作者單位】： 東北大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院;華北科技學(xué)院計(jì)算機(jī)學(xué)院;清華大學(xué)計(jì)算機(jī)系;
【基金】：國家自然科學(xué)基金(61472137) 青海省農(nóng)業(yè)科技成果轉(zhuǎn)化與推廣計(jì)劃(2012-N-525) 河北省科技計(jì)劃(15210703) 國家安全監(jiān)管總局安全生產(chǎn)重大事故防治關(guān)鍵技術(shù)科技項(xiàng)目(zhishu-031-2013AQ) 中央高校基本科研業(yè)務(wù)費(fèi)資助項(xiàng)目(3142014125,3142015022,3142013098)資助
【分類號(hào)】：TN929.5;TP391.44

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