CAREER: Capacity-Driven Design of Large-Scale Wireless Sensor Networks

职业：大规模无线传感器网络的容量驱动设计

• 批准号: 0238035
• 负责人: Mingyan Liu
• 金额: $ 42.19万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0238035&HistoricalAwards=false
• 关键词: CAREER; Capacity; Driven; Design; Large

The proposed research centers on a design methodology for large-scale wireless sensor networks used for data gathering that uses fundamental capacity limit studies as guidelines. The motivation is that a large sensor network can potentially consist of thousands or tens of thousands of sensors densely populated, with strict energy and complexity constraints. Therefore the design of any protocol or algorithm should come with precision and a quantifiable measure, and be based on a good understanding of the ultimate scalability limit. This proposal aims at bridging the gap between the fundamental limit studies and practical protocol design. The goal of the proposed research is to (1) derive capacity limits critical to the large class of data gathering sensor network applications; (2) develop practical distributed algorithms that use these limits as guidelines and can approach these limits; and (3) examine the actual achievable performance of these algorithms in a real sensor testbed setting. Consequently, there are three parts to the proposed research: fundamental capacity limits, distributed algorithms, and testbed experiments. A central theme of the proposed research is to bridge the gap between the theoretical achievability of fundamental limits and the optimality of practical network designs. The proposed design methodology is thus driven by capacity limit studies via novel modeling techniques. It provides a novel and powerful tool in the study of network scalability and feasibility. Under fundamental capacity limits, will study two capacity notions: the throughput capacity, defined within the context of many-to-one communication as the maximum achievable throughput when all nodes are communicating with a single receiver via either a single hop or multiple hops; and the lifetime capacity, defined as the maximum amount of data deliverable by a sensor network until the first sensor dies (due to energy depletion) or till a pre-specified percentage of sensors die. The study of these two capacity notions has direct implications on organizing communications within a network, e.g., whether clustering should be used and how big a cluster should be, how many data collecting base stations should there be and where should they be placed. This study will progress from simple idealized scenarios to increasingly more realistic and complex. Under distributed algorithms, will apply the capacity analysis to the design of distributed algorithms of energy efficient data dissemination, optimal clustering and efficient sensor sleep schedules. These algorithms will be designed to approach or approximate network capacities. Under the proposed research will also develop an experimental wireless sensor testbed for implementation and measurement purposes. The proposed research has a strong education aspect and involves collaboration with two research centers at the University of Michigan. Will seek close collaboration with the Wireless Integrated Micro-systems (WIMS) Center, an NSF ERC at the University of Michigan, and University of Michigan's Wu Manufacturing Research Center (WuMRC). Will incorporate state-of-the-art MEMS sensors currently being developed at WIMS and intelligent infotronics agent sensors being developed for automation systems by WuMRC into our sensor testbed. Collaboration with them will allow the PI to apply design methodology to different application contexts with realistic physical devices.

拟议的研究中心的设计方法用于大规模的无线传感器网络的数据收集，使用基本的容量限制的研究作为指导方针。 其动机是大型传感器网络可能由数千或数万个密集分布的传感器组成，具有严格的能量和复杂性限制。因此，任何协议或算法的设计都应该具有精确性和可量化的度量，并基于对最终可扩展性限制的良好理解。 该建议旨在弥合基本极限研究和实际协议设计之间的差距。 建议的研究的目标是（1）获得关键的大类数据收集传感器网络应用的容量限制;（2）开发实用的分布式算法，使用这些限制作为指导方针，并可以接近这些限制;（3）检查这些算法在真实的传感器测试平台设置的实际可实现的性能。 因此，有三个部分的建议的研究：基本容量限制，分布式算法，和试验台实验。 所提出的研究的一个中心主题是弥合基本极限的理论可验证性与实际网络设计的最优性之间的差距。 因此，所提出的设计方法是通过新的建模技术的容量限制研究。 它为研究网络的可扩展性和可行性提供了一种新颖而有力的工具。在基本容量限制下，将研究两个容量概念：吞吐量容量，定义在多对一通信的上下文中，当所有节点通过单跳或多跳与单个接收器通信时，可实现的最大吞吐量;以及寿命容量，定义为传感器网络在第一个传感器死亡（由于能量耗尽）之前或在预定百分比的传感器死亡之前可传递的最大数据量。对这两个容量概念的研究对网络内的通信组织有直接影响，例如，是否应该使用集群以及集群应该有多大，应该有多少数据收集基站以及它们应该放置在哪里。这项研究将从简单的理想化的情况下，越来越现实和复杂。在分布式算法下，将容量分析应用于能量有效的数据分发、最优聚类和高效的传感器睡眠调度的分布式算法设计。 这些算法将被设计为接近或近似网络容量。 根据拟议的研究，还将开发一个实验性的无线传感器测试平台，用于实施和测量目的。 拟议的研究具有很强的教育方面，涉及与密歇根大学的两个研究中心的合作。将寻求与无线集成微系统（WIMS）中心，密歇根大学的NSF ERC和密歇根大学的吴制造研究中心（WuMRC）的密切合作。将把WIMS目前正在开发的最先进的MEMS传感器和WuMRC为自动化系统开发的智能信息电子代理传感器纳入我们的传感器测试平台。 与他们的合作将使PI能够将设计方法应用于具有真实物理设备的不同应用环境。