本文選題：UAV輔助網(wǎng)絡 + 數(shù)據(jù)收集��； 參考：《南京航空航天大學》2017年碩士論文

【摘要】：無人機的廣泛發(fā)展使得從空中收集無線傳感器網(wǎng)絡的數(shù)據(jù)成為可能。在將無人機作為匯聚節(jié)點的無線傳感器網(wǎng)絡(UAV輔助網(wǎng)絡)中,傳感器節(jié)點和無人機都是由電池供電,提高它們的能量使用效率對于延長網(wǎng)絡生命周期至關(guān)重要。數(shù)據(jù)傳輸是傳感器節(jié)點的主要能量消耗因素,飛行距離是UAV的主要能量消耗因素,因此,為了延長網(wǎng)絡生命周期,需要設計一個高效的數(shù)據(jù)收集方案和針對數(shù)據(jù)收集的無人機路徑規(guī)劃方案。分簇和壓縮感知是WSNs中常用的提高能量使用效率的方法。UAV輔助網(wǎng)絡中只有簇頭節(jié)點可以和UAV通信,節(jié)點的異質(zhì)性要求網(wǎng)絡必須分簇,然而現(xiàn)有的分簇方法都把重點放在簇頭的選擇上,對傳感器節(jié)點的劃分只是簡單的依據(jù)到簇頭的距離,忽略了數(shù)據(jù)的特性。只有少量工作將壓縮感知和分簇相結(jié)合,然而這些工作中忽略了數(shù)據(jù)的稀疏性在不同時空的差異性,因此并不能有效的減少網(wǎng)絡中的能耗。在針對數(shù)據(jù)收集的UAV路徑規(guī)劃中,優(yōu)化目標變?yōu)樵谧畲蠡瘮?shù)據(jù)收集的條件下最小化能量消耗,另外,無人機特有的移動性又要求路徑曲率必須連續(xù)且有界,因此與其他應用的路徑規(guī)劃不同。針對上述存在的問題,本文就UAV輔助網(wǎng)絡的數(shù)據(jù)收集問題進行了以下的研究:(1)結(jié)合壓縮感知和分簇的數(shù)據(jù)收集方案。將結(jié)合壓縮感知的最優(yōu)化分簇問題歸納為混合整數(shù)規(guī)劃問題,證明了此問題是NP難問題。提出了綜合考慮數(shù)據(jù)的壓縮率和簇成員到簇頭節(jié)點之間距離的貪心算法,并進一步對算法進行改進,在保證網(wǎng)絡低能耗的同時降低了時間復雜度。真實數(shù)據(jù)集上的仿真實驗結(jié)果表明提出的算法在能耗和運行時間方面都取得了較好的表現(xiàn)。(2)針對數(shù)據(jù)收集的UAV路徑規(guī)劃方案。對針對數(shù)據(jù)收集的UAV路徑規(guī)劃問題進行了理論分析,并提出了兩種解決該問題的方案。一是基于粒子群算法的路徑規(guī)劃方案,雖然該方案可以找到問題的近似最優(yōu)解,但是時間復雜度也非常高,并不具有實用性。為了降低時間復雜度,提出一種啟發(fā)式的路徑規(guī)劃方案,通過在路徑的產(chǎn)生和選擇過程中考慮路徑的曲率和數(shù)據(jù)量來滿足無人機路徑規(guī)劃的要求。試驗結(jié)果表明兩個方案在路徑長度方面具有相似的表現(xiàn),但是啟發(fā)式路徑規(guī)劃方案的時間復雜度遠小于基于粒子群算法的路徑規(guī)劃方案。
[Abstract]:The extensive development of UAVs makes it possible to collect wireless sensor network data from the air. In UAV (Wireless Sensor Network), both sensor nodes and UAVs are powered by batteries. Improving their energy efficiency is very important to prolong the network life cycle. Data transmission is the main energy consumption factor of sensor nodes, and flight distance is the main energy consumption factor of UAV. Therefore, in order to prolong the network life cycle, It is necessary to design an efficient data collection scheme and an UAV path planning scheme for data collection. Clustering and compressed sensing are commonly used methods to improve energy efficiency in WSNs. Only cluster head nodes can communicate with UAV in UAV-assisted network. The heterogeneity of nodes requires that the network must be clustered. However, the existing clustering methods focus on the selection of cluster heads, and the sensor nodes are divided simply according to the distance from cluster heads, and the characteristics of data are ignored. Only a small amount of work combines compressed sensing and clustering, but these work ignore the difference of data sparsity in different time and space, so it can not effectively reduce the energy consumption in the network. In UAV path planning for data collection, the optimization goal is to minimize energy consumption under the condition of maximizing data collection. In addition, the unique mobility of UAV requires that the path curvature must be continuous and bounded. Therefore, path planning is different from other applications. In view of the above problems, this paper studies the problem of data collection in UAV auxiliary networks as follows: 1) Compression sensing and clustering data collection scheme. The optimal clustering problem with compressed perception is generalized to a mixed integer programming problem, and it is proved that the problem is NP-hard. A greedy algorithm considering the compression ratio of data and the distance between cluster members and cluster head nodes is proposed, and the algorithm is further improved to reduce the time complexity while ensuring the low energy consumption of the network. Simulation results on real data sets show that the proposed algorithm achieves good performance in terms of energy consumption and running time. (2) UAV path planning scheme for data collection is proposed. In this paper, the UAV path planning problem for data collection is theoretically analyzed, and two solutions to this problem are proposed. One is a path planning scheme based on particle swarm optimization. Although this scheme can find the approximate optimal solution of the problem, the time complexity is also very high and it is not practical. In order to reduce the time complexity, a heuristic path planning scheme is proposed to meet the requirements of UAV path planning by considering the curvature and the amount of data in the process of path generation and selection. The experimental results show that the two schemes have similar performance in path length, but the time complexity of the heuristic path planning scheme is much less than the path planning scheme based on particle swarm optimization.
【學位授予單位】：南京航空航天大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：V279;TP274.2

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本文編號：1837906