利用Rb-Rf方法进行三维定位的噪声距离测量
Noisy Distance Measurements Using 3-D Localization with Rb-Rf Methods
摘要：
无线传感器网络是在不同的拓扑结构上动态形成的。无线传感器网络可以协助进行救援行动，并可以及时提供搜索。长期监测应用包括环境监测、安全监测和栖息地监测。此外，当灾难发生时，它可以在时间紧迫的情况下部署。由于我们在这里处理的是人类的生活，我们不能仅仅依赖于依赖于连接信息Rf的定位方案，即仅无距离算法。此外，救援行动是在高噪声环境中进行的，因此基于距离的Rb（基于距离）定位算法在距离测量中产生高误差。需要一种有效的算法，可以测量传感器节点的位置附近的生物或被附接到他们在3-D空间中具有高精度。为了达到这样的精度，需要两种策略的组合。所提出的方法，结合Rb（基于范围）和Rfrange-free策略，成功地定位节点在传感器网络与噪声的距离测量。我们还描述了可扩展性对算法性能的影响。结果表明，随着网络的可扩展性的信标节点的数量增加，该算法的性能高达90%以上。通过改变系统参数，可以很容易地调整估计区域的粒度，这使得所提出的算法具有灵活性。
英文摘要：
Wireless sensor networks are dynamically formed over the varying topologies. Wireless sensor networks can assist in conducting the rescue operations and can provide search in timely manner. Long time monitoring applications are environment monitoring, security surveillance and habitat monitoring. Further, where it can be deployed in time critical situations when disaster happens. As we are dealing with the human lives here, we can not just rely on the localization schemes that depend upon the connectivity information Rf i.e. range-free algorithms only. Further, rescue operations are carried out in highly noisy environments, so distance based Rb(range-based) localization algorithms generate high error in distance measurements. An efficient algorithm is needed that can measure the location of the sensor nodes near to the living being or being attached to them in 3-D space with a high accuracy. To achieve such kind of accuracy a combination of both the strategies is required. The proposed method which incorporates both the Rb(range-based)&Rfrange-free strategies that successfully localizes nodes in a sensor network with noisy distance measurements. We also have depicted the effect of scalability on the performance of the algorithm. Results show that as the scalability of the network increases with the number of beacon nodes; the performance of the algorithm goes high above 90 percent . The granularity of the areas estimated may be easily adjusted by changing the system parameters which makes the proposed algorithm flexible.
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