EAGER: Localization in Ad-Hoc Wireless Networks: Investigation into Fusing Dempster-Shafer Theory and Support Vector Machines

目的：本项目的目的是探索性地研究在现实网络环境中无线AS HOC网络中无线节点的准确定位，在现实网络环境中，由于多径或阴影引起的信号阻塞是一个主要问题。为了解决这些实际挑战，该项目探索了一种新的可靠的定位技术，该技术在数学上融合了从接收信号强度(RSS)和到达时间差(TDOA)测量得出的距离估计。智能优势在于设计和实现了一种高效的混合算法，将Dempster-Shafer理论与最先进的机器学习工具称为支持向量机(SVM)相结合。假设基于支持向量机的信号传播模型可能有助于提高距离测量的精度。该提议涉及高风险，因为它适用于不可预测和动态的实时环境。如果成功，在准确地将RSS和TDOA信号信息映射到距离中，并提供精确的节点位置而无需硬件升级等方面，将会有很高的回报。更广泛的影响：更广泛的影响反映在几个方面。这项探索性研究将使依靠无线网络应用蓬勃发展的美国商业企业受益，特别是在紧急服务和野生动物栖息地监测等战略领域。节点本地化挑战将从结合计算机科学和通信、地球信息学工程和应用数学领域的跨学科角度进行研究。最后，拟议的研究将导致(I)为包括少数群体和代表性不足群体在内的学生创建新的跨学科研究生课程，以及(Ii)促进跨学科国家科学基金会的研究。

Objective:The objective of this project is to perform exploratory investigation into accurate wireless node localization for wireless as hoc networks in realistic network environments, where signal obstruction due to multipath or shadowing is a major concern. To address such practical challenges, this project explores a new and reliable localization technique that mathematically fuses range estimates derived from Received Signal Strength (RSS) and Time Difference Of Arrival (TDOA) measurements.Intellectual merit:The intellectual merit lies in the design and implementation of an efficient, hybrid algorithm integrating Dempster-Shafer theory with a state-of-the-art machine-learning tool referred to as Support Vector Machine (SVM). The hypothesis is that SVM-based signal propagation models will possibly help improve the accuracy of range measurements. The proposal entails high-risk as it is applied to real-time environments that are unpredictable and dynamic. If successful, there will be a high-reward in terms of accurately mapping RSS and TDOA signal information into distances, and offering precise node-position with no hardware upgrades whatsoever.Broader impacts:The broader impacts are reflected in several aspects. This exploratory research will benefit US-based business enterprises that thrive on wireless network applications, particularly in such strategic areas as emergency services and wildlife habitat monitoring. The node localization challenge will be investigated from an interdisciplinary perspective combining the fields of Computer Science and Communications, Geomatics Engineering, and Applied Mathematics. Finally, the proposed research will lead to (i) Creation of new interdisciplinary graduate curriculum for students including minorities and underrepresented groups and (ii) Promotion of interdisciplinary NSF research.