基于WSN的机场噪声监测点布局优化算法的研究

发布时间：2018-06-02 23:15

本文选题：WSN + 机场噪声监测　；参考：《南京航空航天大学》2014年硕士论文

【摘要】：民航业的不断发展使得困扰民航已久的机场噪声问题愈发严重，有效地监测机场噪声是关系民航业持续健康发展的关键。传感器网络被公认为是二十一世纪里最具影响力的改变世界十大技术之一，而无线传感器网络技术更是广泛地应用于各种环境监测系统中。基于WSN的机场噪声监测系统为细粒度机场噪声监测与控制提供了可能。采用WSN技术，机场噪声感知监测点可以在广大的区域中立体分布，全天时、全天候地进行数据采集。本文为监测机场周围噪声数据状况，根据机场内部噪声数据密集和机场周围居民小区附近噪声数据离散的特点，提出针对机场内部二维环境的SANE-IGAOD监测点优化布局算法及针对机场周围居民小区三维环境的MANE-IGHKCA监测点优化布局算法。 SANE-IGAOD模型先将目标区域网格化，利用INM噪声预测软件来计算各个网格点在每个飞机噪声事件发生时的预测到的噪声值，根据SANE的限值来确定各个网格点监测到的噪声事件，再利用改进的遗传算法求得近似最优解，使得解集的传感器节点能覆盖所有的有效噪声事件并且节点数目尽可能少。 MANE-IGHKCA模型考虑机场附近环境复杂，，需要进行三维全覆盖，因此考虑三维WSN的布局；首先将三维目标区域网格化，根据MANE的限值来确定各个网格点监测到的噪声事件，采用k重覆盖的迭代贪婪启发式算法得到监测点的布局位置，使得部署的传感器节点能覆盖所有的有效噪声事件且节点数目也尽可能少。实验仿真结果表明两种监测点布局算法能获得较高的无线传感器网络覆盖质量，分别能保证目标区域内用尽量少的监测点覆盖所有的飞机噪声事件。
[Abstract]:With the continuous development of civil aviation industry, the airport noise problem which has been puzzling civil aviation for a long time has become more and more serious. The effective monitoring of airport noise is the key to the sustained and healthy development of civil aviation industry. Sensor network is recognized as one of the ten most influential technologies in the world in the 21 century, and wireless sensor network technology is widely used in various environmental monitoring systems. The airport noise monitoring system based on WSN provides the possibility for fine grained airport noise monitoring and control. Using WSN technology, airport noise sensing monitoring points can be distributed stereoscopically in a wide area, and data can be collected all day and all day. In order to monitor the noise data around the airport, according to the characteristics of the dense noise data inside the airport and the discrete noise data near the residential area around the airport, The optimal layout algorithm of SANE-IGAOD monitoring points for the two-dimensional environment of the airport and the optimal layout algorithm of the MANE-IGHKCA monitoring points for the three-dimensional environment of the residential area around the airport are proposed. The SANE-IGAOD model first grips the target area and calculates the predicted noise values of each grid point at the time of each aircraft noise event by using INM noise prediction software. According to the limit value of SANE, the noise events monitored by each grid point are determined. Then the improved genetic algorithm is used to obtain the approximate optimal solution so that the sensor nodes of the solution set can cover all the effective noise events and the number of nodes is as small as possible. Considering the complex environment near the airport, the MANE-IGHKCA model considers the layout of 3D WSN. Firstly, the noise events monitored by each grid point are determined according to the limited values of MANE. The k-overlay iterative greedy heuristic algorithm is used to obtain the location of the monitoring points, so that the deployed sensor nodes can cover all the effective noise events and the number of nodes is as small as possible. The simulation results show that the two algorithms can achieve high coverage quality of wireless sensor networks and can cover all aircraft noise events with as few monitoring points as possible in the target area.
【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】：2014
【分类号】：V351;TB53

【参考文献】