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

目的：本项目的目的是进行探索性研究，准确的无线节点定位无线自组织网络在现实的网络环境中，由于多径或阴影的信号阻塞是一个主要问题。为了解决这样的实际挑战，该项目探讨了一种新的和可靠的定位技术，数学融合的距离估计来自接收信号强度（RSS）和到达时间差（TDOA）measurement.Intellectual优点：智力的优点在于设计和实施一个有效的，混合算法集成Dempster-Shafer理论与国家的最先进的机器学习工具，称为支持向量机（SVM）。假设基于SVM的信号传播模型可能有助于提高距离测量的准确性。该提案涉及高风险，因为它适用于不可预测和动态的实时环境。如果成功的话，在将RSS和TDOA信号信息精确映射到距离上，并提供精确的节点位置而无需任何硬件升级方面，将有很高的回报。更广泛的影响：更广泛的影响体现在几个方面。这项探索性的研究将使那些在无线网络应用方面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。节点定位的挑战将从跨学科的角度结合计算机科学和通信，地理信息工程和应用数学领域进行研究。最后，拟议的研究将导致（i）为包括少数民族和代表性不足的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NSF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.