CRII: CIF: Distributed Sensor Localization With Ordinal Data Constraints

这项研究涉及一类新的基于能量效率优化的无线传感器网络(WSN)定位算法的研究，该算法可以在传统的定位技术(如GPS)不可用时(例如，当WSN用户在室内时)使用。这样的系统仅使用比较距离测量(而不是精确距离测量)来操作，这排除了在设备之间严格的时钟同步的需要，并实现了异类跨平台WSN。利用无线传感器网络的分布式容量进行计算。通过在Android试验台上的仿真和实验相结合的方法，从理论上验证了算法的性能和能量效率。这项研究有多种应用领域，如健康监测(位置感知患者护理)、安全(第一响应者)和消费电子产品(室内购物中心的位置感知)。这项研究涉及到一种新的传感器网络定位方法的调查，其中不需要精确的距离测量？传感器只需要比较的和顺序的距离测量。这种方法不需要设备之间严格的时钟同步，也不需要准确了解操作条件和环境常量。它还消除了对专门硬件或外部设备的需要，使得在跨平台环境中探索基于本地化的移动电话和平板电脑上的WSN应用成为可能。研究方法包括应用凸优化方法来解决欧氏距离嵌入问题。虽然这种方法已经被用于图像去噪和模式识别等领域，但从来没有人提出将其用于无线传感器网络中的定位。具体的研究课题包括使用基于优化的传感器定位，以及通过分布式计算进行参数和函数估计。一个Android设备的试验台被用来验证结果。

This research involves the study of a new class of energy efficient optimization-based algorithms for localization in wireless sensor networks (WSNs) that can be used when conventional localization techniques such as GPS are not available (e.g. when WSN users are indoors). Such a system operates using only comparative distance measurements (as opposed to exact distance measurements) which precludes the need for strict clock synchronization among devices and enables heterogeneous cross-platform WSNs. Computation is implemented using the distributed capacity of the WSNs. The performance and energy efficiency of the algorithms are validated theoretically and by a combination of simulation and experimentation on an Android test bed. This research has diverse application areas such as health monitoring (location-aware patient care), security (first responders), and consumer electronics (location awareness in indoor malls).This research involves investigation into a new approach to localization in sensor networks where exact distance measurements are not required ? sensors need only comparative and ordinal distance measurements. This approach eliminates the need for strict clock synchronization between devices, or exact knowledge of operating conditions and environmental constants. It also eliminates the need for specialized hardware or external devices, making it possible to explore localization-based WSN applications on mobile phones and tablets in cross-platform environments. The research methodology involves the application of convex optimization to solve a Euclidean distance embedding problem. While such an approach has been used in areas such as image denoising and pattern recognition, it has never been proposed for localization in a WSN. Specific research topics include using optimization-based sensor localization, and parameter and function estimation via distributed computation. A testbed of Android devices is used to validate results.