NeTS: Small: Large Scale Sensor Network Routing using Conformal Geometry

NeTS：小型：使用共形几何的大规模传感器网络路由

• 批准号: 1016829
• 负责人: Jie Gao
• 金额: $ 45万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016829&HistoricalAwards=false
• 关键词: NeTS; Small; Large; Scale; Sensor

This project develops efficient routing schemes for large scale multi-hop wireless sensor networks with a complex shape. As sensor networks grow large in size, terrain features or non-uniform energy usage may leave the network with holes. Efficient routing becomes difficult as some knowledge of how to "get around" the holes is needed.The novelty of this project is to use conformal geometry to compute a proper embedding of the network such that simple, distributed greedy routing schemes achieve desirable properties. Conformal geometry shows that any surface can be deformed to three canonical shapes: the sphere, the plane and the disk. Thus, one can "regulate" any sensor field shape to be of a canonical, simple form. The complexity of the routing problem and the domain specifics are encapsulated in the embedding such that routing decision becomes trivial. The conformal mapping of a sensor network is computed using Ricci curvature flow, an intrinsically distributed computation routine that can easily incorporate the dynamic changes of wireless links. This project focuses on conformal mapping and the companion greedy routing solutions that guarantee delivery, achieve good load balancing, facilitate in-network storage and data-centric routing, are resilient to network failures, and generalize to both 2D and 3D networks.This project explores the unique cross-disciplinary area of wireless networking and differential geometry. Although the main thrust of this proposal is theoretical, it is expected that the project can also provide practical routing solutions for large scale sensor networks.

该项目为形状复杂的大规模多跳无线传感器网络开发了高效的路由方案。随着传感器网络规模的扩大，地形特征或能量使用不均匀可能会给网络留下漏洞。该方案的创新之处在于使用保角几何来计算网络的适当嵌入，从而使简单、分布式的贪婪路由方案获得所需的特性。保角几何表明，任何曲面都可以变形为三种规范形状：球面、平面和圆盘。因此，人们可以将任何传感器场形状“调节”为规范的、简单的形式。路由问题的复杂性和域细节被封装在嵌入中，使得路由决策变得微不足道。传感器网络的保角映射是使用Ricci曲率流计算的，Ricci曲率流是一种本质上分布式的计算程序，可以很容易地结合无线链路的动态变化。该项目专注于保角映射和相应的贪婪路由解决方案，这些解决方案可保证交付、实现良好的负载平衡、促进网络内存储和以数据为中心的路由、对网络故障具有恢复能力，并可推广到2D和3D网络。该项目探索无线网络和微分几何的独特跨学科领域。虽然该方案的主旨是理论上的，但预计该项目也可以为大规模传感器网络提供实用的路由解决方案。