本文選題：Ad　切入點：Hoc　出處：《安徽大學》2013年碩士論文　論文類型：學位論文

【摘要】：由于移動Ad Hoc網(wǎng)絡(luò)具有自組織、無中心和動態(tài)拓撲的特點,因此可以滿足人們隨時隨地通信的需求,然而節(jié)點能量的受限和經(jīng)常變化的拓撲結(jié)構(gòu)成為設(shè)計Ad Hoc網(wǎng)絡(luò)協(xié)議的重大障礙,特別是網(wǎng)絡(luò)規(guī)模較大時其穩(wěn)定性難以保持。Ad Hoc網(wǎng)絡(luò)通過分簇算法來實現(xiàn)網(wǎng)絡(luò)分層,以支持高效的資源管理和路由策略,分層結(jié)構(gòu)可以有效的滿足一定的Qos需求,負載平衡因子和簇穩(wěn)定性是評價分簇算法性能的主要標準。 本文首先介紹了Ad Hoc網(wǎng)絡(luò)和分簇算法的相關(guān)知識和概念,并介紹了一個不借助GPS的輔助來描述簇移動性的方法。接著對TCA分簇算法進行了改進,提出了一種基于距離變化的加權(quán)分簇算法MSED,該算法將網(wǎng)絡(luò)層上處于同一子網(wǎng)的節(jié)點盡量分在一個簇中,并通過一段時間內(nèi)節(jié)點與其鄰居節(jié)點間距離的變化來預(yù)測節(jié)點的穩(wěn)定性,與TCA算法相比,MSED算法考慮了偵測周期期間節(jié)點入簇情況,并改進了權(quán)值計算公式。NS2仿真結(jié)果證明,同TCA算法相比,MSED算法得到的簇穩(wěn)定性較高,在節(jié)點間傳輸有效距離相同或者最大移動速度相同時,其網(wǎng)絡(luò)統(tǒng)治集更新頻率和節(jié)點入簇頻率均較低。 常用的分簇算法沒有很好的保持網(wǎng)絡(luò)的穩(wěn)定性,穩(wěn)定的分簇算法可以減少網(wǎng)絡(luò)的計算和通信開銷,為了提高網(wǎng)絡(luò)的穩(wěn)定性,本文最后在AOW分簇算法的基礎(chǔ)上,提出了一種基于鏈接率的自適應(yīng)按需加權(quán)分簇算法AOWLR,該算法借助GPS的輔助,將節(jié)點的運動軌跡抽象為直線型和圓弧形兩種,并引入鄰居節(jié)點的平均鏈接率預(yù)測值作為衡量簇穩(wěn)定性的一個重要標準。NS2仿真結(jié)果證明,同AOW相比,AOWLR算法的負載平衡因子較高,連通統(tǒng)治集(CDS)更新頻率和節(jié)點充當簇頭的公平性指數(shù)(HFI)較低。
[Abstract]:Because the mobile Ad Hoc network has the characteristics of self-organization, no center and dynamic topology, it can meet the needs of people communicating anytime, anywhere. However, the limitation of node energy and the frequently changing topology have become a major obstacle to the design of Ad Hoc network protocols. Especially, it is difficult to maintain the stability of Ad Hoc network by clustering algorithm when the network size is large. In order to support efficient resource management and routing strategy, hierarchical architecture can effectively meet certain Qos requirements. Load balancing factor and cluster stability are the main criteria to evaluate the performance of clustering algorithm. This paper first introduces the knowledge and concept of Ad Hoc network and clustering algorithm, and introduces a method to describe cluster mobility without the help of GPS, and then improves the TCA clustering algorithm. In this paper, a weighted clustering algorithm based on distance variation is proposed. The algorithm divides the nodes in the same subnet into a cluster as far as possible, and predicts the stability of the nodes by the distance between the nodes and their neighbors over a period of time. Compared with the TCA algorithm, the TCA algorithm takes into account the clustering situation during the detection period, and improves the weight calculation formula. NS2 simulation results show that compared with the TCA algorithm, the cluster stability of the MMSED algorithm is higher than that of the TCA algorithm. When the effective distance between nodes is the same or the maximum moving speed is the same, the updating frequency of the dominant set and the frequency of node joining are lower. The commonly used clustering algorithms do not keep the stability of the network very well. The stable clustering algorithm can reduce the computation and communication overhead of the network. In order to improve the stability of the network, this paper finally based on the AOW clustering algorithm. An adaptive on-demand weighted clustering algorithm based on link rate is proposed. With the aid of GPS, the motion trajectory of nodes is abstracted into straight line and circular arc. As an important criterion of cluster stability, NS2 simulation results show that the load balancing factor of AOWLR algorithm is higher than that of AOW, and the prediction value of average link rate of neighbor nodes is used as an important criterion to measure the stability of cluster. The frequency of CDS) renewal and the fairness index (HFI) of nodes acting as cluster heads are lower.
【學位授予單位】：安徽大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TN929.5

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