本文關(guān)鍵詞： 無線傳感器網(wǎng)絡 節(jié)點定位 蟻群算法 DV-hop算法 自適應　出處：《華中師范大學》2014年碩士論文　論文類型：學位論文

【摘要】：無線傳感器網(wǎng)絡(WSN)是由大量傳感器節(jié)點以自組織方式組成的一個監(jiān)控系統(tǒng),可以對目標區(qū)域的信息進行實時地監(jiān)控和處理,應用十分廣泛。對于大多數(shù)WSN來說,未知節(jié)點所感知的信息時沒有意義的,我們必須了解無線傳感器網(wǎng)絡中各個節(jié)點的位置信息。因此,節(jié)點定位在無線傳感器網(wǎng)絡應用中起著至關(guān)重要的作用。目前,節(jié)點定位已經(jīng)成為學術(shù)界研究的熱點問題。 節(jié)點定位算法主要分為基于測距(Range-based)定位算法和無需測距(Range-free)定位算法。基于測距定位算法需要給節(jié)點配置額外的硬件設備來完成相應的測距任務,該類算法定位的結(jié)果精度高,但是增加了網(wǎng)絡的成本和能量消耗,影響網(wǎng)絡的使用壽命；相比較而言,無需測距定位算法實現(xiàn)起來更加簡單方便一些,該類算法不需要額外的硬件設備,通過節(jié)點間的通信大致估算出未知節(jié)點的位置,但是定位精度不如基于測距定位算法。 蟻群算法(ACO)作為人工智能的一個分支,在處理組合優(yōu)化問題時有較好的效果。本文通過對節(jié)點定位問題進行相應的轉(zhuǎn)化,把節(jié)點定位問題變成函數(shù)優(yōu)化問題,將蟻群算法應用在節(jié)點定位問題上,提出了基于蟻群算法的節(jié)點定位算法(ACOL)。由于蟻群算法自身的局限性,容易導致算法早熟或收斂速度過慢。在基本蟻群算法的基礎上,我們進行了相應的改進,提出了自適應蟻群算法(AACO),并將該算法應用在節(jié)點定位問題上,形成了基于自適應蟻群算法的節(jié)點定位算法(AACOL)來避免算法早熟或收斂過慢。 最后本文采用MATLAB進行仿真實驗,在相同的實驗環(huán)境下比較了DV-Hop算法、ACOL算法和AACOL算法的定位精度。實驗結(jié)果表明,ACOL算法和AACOL算法較DV-Hop算法定位精度更高,AACOL算法比ACOL算法結(jié)果更加穩(wěn)定,收斂速度更快。
[Abstract]:Wireless Sensor Network (WSNs) is a self-organizing monitoring system composed of a large number of sensor nodes. It can monitor and process the information of the target area in real time. It is widely used. For most WSN, the information perceived by unknown nodes is meaningless, we must know the location information of each node in WSN. Node location plays an important role in wireless sensor network applications. At present, node location has become a hot topic in academic research. Node localization algorithm is mainly divided into Range-based location algorithm and Range-free-based location algorithm. Location algorithm. Based on the location algorithm, the nodes need to be equipped with additional hardware equipment to complete the corresponding ranging tasks. The accuracy of this algorithm is high, but the cost and energy consumption of the network are increased, and the service life of the network is affected. By comparison, it is more simple and convenient to realize the location algorithm without the need of distance location. This kind of algorithm does not need additional hardware equipment, and the location of unknown nodes can be estimated roughly by the communication between nodes. However, the location accuracy is not as good as the location algorithm based on ranging. As a branch of artificial intelligence, ACO (Ant Colony algorithm) has a good effect in dealing with combinatorial optimization problem. In this paper, the node location problem is transformed accordingly. The problem of node location is turned into a function optimization problem, and the ant colony algorithm is applied to the problem of node location, and a node location algorithm based on ant colony algorithm is proposed. Due to the limitations of ant colony algorithm itself. It is easy to cause premature convergence or slow convergence. Based on the basic ant colony algorithm, we improve the algorithm and propose an adaptive ant colony algorithm (AAC). The algorithm is applied to the problem of node location and a node location algorithm based on adaptive ant colony algorithm (ACO) is developed to avoid premature convergence or slow convergence. Finally, this paper uses MATLAB to carry on the simulation experiment, under the same experimental environment, compares the DV-Hop algorithm ACOL algorithm and the AACOL algorithm localization accuracy. The experimental results show that. The accuracy of ACOL algorithm and AACOL algorithm is higher than that of DV-Hop algorithm. The algorithm is more stable and convergent than ACOL algorithm.
【學位授予單位】：華中師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP212.9;TN929.5;TP18

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