CAREER: Capacity-Driven Design of Large-Scale Wireless Sensor Networks
CAREER: Capacity-Driven Design of Large-Scale Wireless Sensor Networks
批准号:
0238035
负责人:
Mingyan Liu
金额:
$42.19万
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
项目状态:
已结题
起止时间:
2003-09-01 至 2010-08-31
中文摘要
拟议的研究中心是用于数据收集的大规模无线传感器网络的设计方法,该方法使用基本容量限制研究作为指导方针。其动机是,大型传感器网络可能由数千或数万个传感器密集组成,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,并基于对最终可伸缩性限制的良好理解。本提案旨在弥合基本极限研究与实际协议设计之间的差距。提出的研究目标是:(1)得出对大型数据采集传感器网络应用至关重要的容量限制;(2)开发实用的分布式算法,以这些限制为指导,并可以接近这些限制;(3)在真实的传感器试验台设置中检验这些算法的实际可实现性能。因此,提出的研究分为三个部分:基本容量限制,分布式算法和测试平台实验。所提出的研究的一个中心主题是弥合基本限制的理论可实现性与实际网络设计的最优性之间的差距。因此,提出的设计方法是通过新颖的建模技术通过容量限制研究驱动的。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,在多对一通信环境中定义为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)或预先指定百分比的传感器死亡之前可交付的最大数据量。对这两个容量概念的研究对组织网络内的通信有直接影响,例如,是否应该使用集群,集群应该有多大,应该有多少个数据收集基地台以及应该把它们放在哪里。这项研究将从简单的理想化场景发展到越来越现实和复杂的场景。在分布式算法下,将容量分析应用于高效数据分发、最优聚类和高效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无线传感器测试平台,用于实施和测量目的。拟议的研究有很强的教育方面,并涉及与密歇根大学的两个研究中心合作。将寻求与无线集成微系统(WIMS)中心、密歇根大学NSF ERC和密歇根大学Wu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正在为自动化系统开发的智能信息电子代理传感器整合到我们的传感器测试平台中。与他们的合作将允许PI将设计方法应用于具有现实物理设备的不同应用环境。
英文摘要
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.
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