發(fā)布時間：2018-06-19 05:41

  本文選題：移動群智感知 + 激勵機(jī)制�。� 參考：《南京郵電大學(xué)》2017年碩士論文

【摘要】：移動群智感知被認(rèn)為是大數(shù)據(jù)時代最重要的數(shù)據(jù)采集方式之一,是當(dāng)前計(jì)算機(jī)網(wǎng)絡(luò)研究領(lǐng)域中的一個研究熱點(diǎn),近年已引起了國內(nèi)外科研人員的密切關(guān)注。移動群智感知的核心思想是利用集體的智慧和力量去完成個體很難完成或者需要長期完成的任務(wù),它是以大規(guī)模用戶參與為前提的。而激勵機(jī)制對群智系統(tǒng)提升用戶參與積極性、保證交易公平性和提高數(shù)據(jù)質(zhì)量等方面具有重要作用。同時,移動群智感知中用戶的隱私保護(hù)問題和感知過程中數(shù)據(jù)的質(zhì)量也值得關(guān)注。因此,針對不同應(yīng)用場景和目標(biāo)設(shè)計(jì)的激勵機(jī)制是移動群智感知系統(tǒng)需要解決的一個基本問題。本文主要考慮使得社會成本,即智能手機(jī)用戶的總成本最小化的激勵機(jī)制。本文基于該目標(biāo)針對不同的應(yīng)用場景,對系統(tǒng)進(jìn)行建模,對所研究的具體問題進(jìn)行形式化,采用博弈論方法和相關(guān)技術(shù),提出解決用戶選擇和支付問題的算法。針對時間窗口依賴任務(wù)的移動群智感知應(yīng)用場景,本文分別設(shè)計(jì)了兩類不同的激勵機(jī)制。在單時間窗口場景中,本文設(shè)計(jì)出一種基于動態(tài)規(guī)劃方法的最優(yōu)化算法MST去選取參與用戶,而在多時間窗口場景中,本文設(shè)計(jì)出一種基于貪心方法的算法MMT,可以在多項(xiàng)式時間內(nèi)獲得近似最優(yōu)解;針對平臺有預(yù)算約束的應(yīng)用場景,本文著重考慮一種應(yīng)用在最大連續(xù)時間覆蓋且基于預(yù)算約束的移動群智感知模型,基于此,本文分別設(shè)計(jì)了采用單時間窗口激勵機(jī)制的預(yù)算可行性框架和采用多時間窗口激勵機(jī)制的預(yù)算可行性框架,使得平臺期望得到的效用最大化。之后,將預(yù)算可行的框架擴(kuò)展至更一般的場景中,即每個手機(jī)用戶同時可以報多個時間區(qū)間的任務(wù);針對移動群智感知過程中感知數(shù)據(jù)質(zhì)量需求和用戶差分隱私保護(hù)的需要,本文考慮一種基于數(shù)據(jù)質(zhì)量且滿足差分隱私保護(hù)的激勵機(jī)制,將感知數(shù)據(jù)質(zhì)量指標(biāo)和差分隱私保護(hù)方法相結(jié)合,并且在用戶選取階段分別設(shè)計(jì)了線性和對數(shù)兩類得分函數(shù)來滿足相應(yīng)性質(zhì)。通過嚴(yán)格的理論分析和大量實(shí)驗(yàn)?zāi)M,證明本文設(shè)計(jì)的激勵機(jī)制都能夠滿足真實(shí)性和個人理性。其中,時間窗口依賴任務(wù)的激勵機(jī)制能夠使得社會成本最小化,并且MST是單時間窗口情況下解決SOUS問題的最優(yōu)算法,MMT在多時間窗口中能夠在In||+1范圍以內(nèi)獲得最優(yōu)解的近似值。預(yù)算約束下最大連續(xù)時間覆蓋模型的激勵機(jī)制同樣能夠使得社會成本最小化,并且滿足預(yù)算可行性�；诓罘蛛[私的激勵機(jī)制能夠達(dá)到近似的社會成本最小化和近似的差分隱私性質(zhì)。
[Abstract]:Mobile group intelligence perception is regarded as one of the most important data acquisition methods in the big data era, and it is a research hotspot in the field of computer network research. In recent years, it has attracted the close attention of researchers both at home and abroad. The core idea of mobile group intelligence perception is to use collective wisdom and power to accomplish tasks that are difficult for individuals to complete or need to be completed for a long time. It is based on large-scale user participation. The incentive mechanism plays an important role in improving user participation enthusiasm, ensuring transaction fairness and improving data quality. At the same time, the user privacy protection problem and the data quality in the process of mobile group intelligence perception are also worthy of attention. Therefore, the incentive mechanism for different application scenarios and targets is a basic problem to be solved in mobile swarm intelligence sensing systems. This paper focuses on the incentive mechanism to minimize the social cost, that is, the total cost of smartphone users. Based on this goal, this paper models the system for different application scenarios, formalizes the specific problems studied, and proposes an algorithm to solve the problem of user selection and payment by using game theory method and related technology. In this paper, two different kinds of incentive mechanisms are designed for mobile group intelligence perception application scenarios of time-window dependent tasks. In a single time window scenario, an optimization algorithm based on dynamic programming method, MST, is designed to select the participating users. In this paper, we design an algorithm based on greedy method, which can obtain approximate optimal solution in polynomial time. In this paper, we focus on a mobile group intelligence perception model which is applied to the maximum continuous time coverage and budget constraints. In this paper, the budget feasibility framework with single time window incentive mechanism and the budget feasibility framework with multiple time window incentive mechanism are designed to maximize the expected utility of the platform. Then, the framework of budget feasibility is extended to a more general scenario, that is, each mobile phone user can report tasks in multiple time intervals at the same time, aiming at the needs of perceived data quality and user differential privacy protection in the process of mobile group intelligence perception. In this paper, an incentive mechanism based on data quality and satisfying differential privacy protection is considered, which combines perceptual data quality index with differential privacy protection method. And the linear and logarithmic score functions are designed in the user selection stage to satisfy the corresponding properties. Through strict theoretical analysis and a large number of experimental simulations, it is proved that the incentive mechanism designed in this paper can satisfy the reality and individual rationality. The incentive mechanism of time window dependent task can minimize the social cost, and MST is the optimal algorithm for solving the sos problem in the case of single time window. MMT can obtain the approximate value of the optimal solution in the range of in 1 in multiple time windows. The incentive mechanism of the maximum continuous time coverage model under budget constraints can also minimize the social cost and satisfy the budget feasibility. The incentive mechanism based on differential privacy can achieve approximate social cost minimization and approximate differential privacy property.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN929.5

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相關(guān)期刊論文 前2條

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本文編號：2038718