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

EAGER：Ad-Hoc 无线网络中的定位：融合 Dempster-Shafer 理论和支持向量机的研究

• 批准号: 1309658
• 负责人: Vijaya Kumar Devabhaktuni
• 金额: $ 15万
• 依托单位: University of Toledo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1309658&HistoricalAwards=false
• 关键词: EAGER; Localization; Ad; Hoc; Wireless

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.

目标：该项目的目标是对现实网络环境中无线即席网络的准确无线节点定位进行探索性调查，其中多路径或阴影造成的信号阻塞是主要问题。为了解决此类实际挑战，该项目探索了一种新的可靠的定位技术，该技术以数学方式融合了从接收信号强度 (RSS) 和到达时间差 (TDOA) 测量得出的范围估计。 智力优点：智力优点在于设计和实现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 SVM 的信号传播模型可能有助于提高距离测量的准确性。该提案具有高风险，因为它应用于不可预测和动态的实时环境。如果成功，将在将RSS和TDOA信号信息准确映射到距离方面获得高额回报，并在无需升级任何硬件的情况下提供精确的节点位置。 更广泛的影响：更广泛的影响体现在几个方面。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。节点定位挑战将从跨学科的角度进行研究，结合计算机科学与通信、测绘工程和应用数学领域。最后，拟议的研究将导致（i）为包括少数族裔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）促进跨学科的 NSF 研究。