本文選題：無線傳感器網(wǎng)絡(luò) + 改進(jìn)的蟻群優(yōu)化算法�。� 參考：《江南大學(xué)》2014年碩士論文

【摘要】：路由環(huán)節(jié)對于無線傳感器網(wǎng)絡(luò)節(jié)省能量非常關(guān)鍵，，因?yàn)槁酚蓞f(xié)議決定著節(jié)點(diǎn)通信的路徑，而通信路徑影響著通信的能耗�？紤]到層次型路由協(xié)議有優(yōu)于平面型路由協(xié)議的性能，本文重點(diǎn)在層次路由協(xié)議上開展研究。 (1)利用改進(jìn)的蟻群優(yōu)化算法優(yōu)化WSN簇頭多跳最優(yōu)路徑：在DCHS算法分簇的基礎(chǔ)上，利用改進(jìn)的蟻群算法，搜尋從距匯聚節(jié)點(diǎn)最近的簇頭節(jié)點(diǎn)出發(fā)，遍歷所有簇頭節(jié)點(diǎn)，最終到達(dá)匯聚節(jié)點(diǎn)的最優(yōu)路徑。這是一個(gè)改進(jìn)的TSP模型。由于每個(gè)簇頭之間距離很短，大大減少了每一個(gè)簇頭都單獨(dú)與匯聚節(jié)點(diǎn)通信的能耗。并且該算法的最優(yōu)路徑是全局最優(yōu)路徑，并不是每個(gè)簇頭節(jié)點(diǎn)都有一條由它至匯聚節(jié)點(diǎn)的最優(yōu)路徑。改進(jìn)的蟻群優(yōu)化算法體現(xiàn)在，優(yōu)化了選擇概率公式中的啟發(fā)函數(shù)。 (2)利用特殊粒子群算法優(yōu)化WSN簇頭的選�。合扔肈CHS算法進(jìn)行預(yù)分簇，再采用特殊粒子群算法選擇出每個(gè)簇內(nèi)最適合當(dāng)選簇頭的節(jié)點(diǎn)。特殊粒子群算法中，每個(gè)粒子對應(yīng)于一個(gè)簇，只在簇內(nèi)跳動，并且不重復(fù)之前經(jīng)過的節(jié)點(diǎn)。因此，迭代次數(shù)大大地減少。而且由于全局極值因子對每個(gè)簇的簇頭選取沒有參考價(jià)值，特殊粒子群算法中沒有全局極值因子。它的特點(diǎn)就是迭代次數(shù)少，效率高，且能明顯延長第一個(gè)節(jié)點(diǎn)死亡時(shí)間。 (3)融合上述兩種優(yōu)化算法，分別作用于簇的建立階段、簇頭與匯聚節(jié)點(diǎn)的通信階段。綜合了兩種算法的PSO-ACO算法，分別與這兩種算法通過仿真圖進(jìn)行性能上的比較。經(jīng)過驗(yàn)證，PSO-ACO算法很好地均衡了網(wǎng)絡(luò)能耗，在第一個(gè)節(jié)點(diǎn)死亡時(shí)間、網(wǎng)絡(luò)的生命周期兩方面有了較大的提升。
[Abstract]:Routing is very important for energy saving in wireless sensor networks because the routing protocol determines the path of node communication and the communication path affects the energy consumption of communication. Considering that hierarchical routing protocol has better performance than planar routing protocol, this paper focuses on hierarchical routing protocol. 1) the improved ant colony optimization algorithm is used to optimize the multi-hop optimal path of WSN cluster head. Based on the clustering of DCHS algorithm, the improved ant colony algorithm is used to search the cluster head node nearest to the convergent node and traverse all cluster head nodes. Finally, the optimal path to the convergent node is obtained. This is an improved TSP model. Because of the short distance between each cluster head, the energy consumption of each cluster head communicating with the sink node is greatly reduced. Moreover, the optimal path of the algorithm is the global optimal path, and not every cluster head node has an optimal path from it to the convergence node. The improved ant colony optimization algorithm is embodied in the optimization of the heuristic function in the selection probability formula. (2) using special particle swarm optimization algorithm to optimize the selection of WSN cluster heads: firstly, the DCHS algorithm is used to pre-cluster, then the special particle swarm optimization algorithm is used to select the nodes in each cluster that are most suitable for the selection of cluster heads. In the special particle swarm optimization algorithm, each particle corresponds to one cluster, only beats in the cluster, and does not repeat the nodes passed before. As a result, the number of iterations is greatly reduced. Since the global extremum factor has no reference value for the cluster head selection of each cluster, there is no global extremum factor in the special particle swarm optimization algorithm. It is characterized by less iterations, high efficiency and the ability to prolong the death time of the first node. The fusion of the above two optimization algorithms acts on the establishment of the cluster and the communication between the cluster head and the convergent node respectively. The PSO-ACO algorithm of the two algorithms is synthesized and compared with the two algorithms by simulation graph. It is proved that the PSO-ACO algorithm can balance the network energy consumption well and improve the lifetime of the first node and the lifetime of the network.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.5;TP212.9

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