UAV辅助网络中面向数据收集的能量优化研究

发布时间：2018-05-03 09:13

本文选题：UAV辅助网络 + 数据收集　；参考：《南京航空航天大学》2017年硕士论文

【摘要】：无人机的广泛发展使得从空中收集无线传感器网络的数据成为可能。在将无人机作为汇聚节点的无线传感器网络(UAV辅助网络)中,传感器节点和无人机都是由电池供电,提高它们的能量使用效率对于延长网络生命周期至关重要。数据传输是传感器节点的主要能量消耗因素,飞行距离是UAV的主要能量消耗因素,因此,为了延长网络生命周期,需要设计一个高效的数据收集方案和针对数据收集的无人机路径规划方案。分簇和压缩感知是WSNs中常用的提高能量使用效率的方法。UAV辅助网络中只有簇头节点可以和UAV通信,节点的异质性要求网络必须分簇,然而现有的分簇方法都把重点放在簇头的选择上,对传感器节点的划分只是简单的依据到簇头的距离,忽略了数据的特性。只有少量工作将压缩感知和分簇相结合,然而这些工作中忽略了数据的稀疏性在不同时空的差异性,因此并不能有效的减少网络中的能耗。在针对数据收集的UAV路径规划中,优化目标变为在最大化数据收集的条件下最小化能量消耗,另外,无人机特有的移动性又要求路径曲率必须连续且有界,因此与其他应用的路径规划不同。针对上述存在的问题,本文就UAV辅助网络的数据收集问题进行了以下的研究:(1)结合压缩感知和分簇的数据收集方案。将结合压缩感知的最优化分簇问题归纳为混合整数规划问题,证明了此问题是NP难问题。提出了综合考虑数据的压缩率和簇成员到簇头节点之间距离的贪心算法,并进一步对算法进行改进,在保证网络低能耗的同时降低了时间复杂度。真实数据集上的仿真实验结果表明提出的算法在能耗和运行时间方面都取得了较好的表现。(2)针对数据收集的UAV路径规划方案。对针对数据收集的UAV路径规划问题进行了理论分析,并提出了两种解决该问题的方案。一是基于粒子群算法的路径规划方案,虽然该方案可以找到问题的近似最优解,但是时间复杂度也非常高,并不具有实用性。为了降低时间复杂度,提出一种启发式的路径规划方案,通过在路径的产生和选择过程中考虑路径的曲率和数据量来满足无人机路径规划的要求。试验结果表明两个方案在路径长度方面具有相似的表现,但是启发式路径规划方案的时间复杂度远小于基于粒子群算法的路径规划方案。
[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.
【学位授予单位】：南京航空航天大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：V279;TP274.2

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