CAREER: Design and Analysis of Performance-Critical Wireless Sensor Networks: A Fusion-Centric Approach

职业：性能关键型无线传感器网络的设计和分析：以融合为中心的方法

• 批准号: 0954039
• 负责人: Guoliang Xing
• 金额: $ 42.47万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0954039&HistoricalAwards=false
• 关键词: CAREER; Design; Analysis; Performance; Critical

Wireless Sensor Networks (WSNs) have the potential to monitor the physical world at an unprecedented spatio-temporal scale. Recently, WSNs have been deployed for many emerging performance-critical applications such as monitoring important infrastructures (power grid and bridges) and detecting natural hazards (volcanoes and earthquakes). However, deeply integrated with the physical world, WSNs often suffer from significant performance variations caused by uncertainties including environmental noise, dynamics of physical phenomena, and network deployment inaccuracy. This project develops a principled network design and analysis approach to performance assurance of WSNs. In contrast to existing heuristics-based solutions, this approach adopts data fusion, an advanced information processing scheme, to enable sensors to efficiently collaborate in delivering predictable network performance. This project has four aims: 1) A framework for analyzing spatio-temporal sensing performance based on established data fusion models. The analysis captures fundamental relationship between spatio-temporal coverage defined by event detection and false alarm probabilities, fusion models, network density, and physical uncertainties including noise and deployment inaccuracy. 2) Data fusion schemes that exploit mobile sensors to reconfigure the capability of a network in response to physical dynamics. 3) A unified fusion and communication architecture abstraction that allows developers to implement and optimize network protocols using group-level primitives. 4) A model-driven medium access control protocol that can achieve predictable throughput and delay in collaborative data processing.This project will have impact in numerous critical applications that require stringent sensing and communication performance. The results of this project will be integrated into outreach activities, curriculum development, and student mentoring.

无线传感器网络（WSNs）具有在前所未有的时空尺度上监测物理世界的潜力。最近，无线传感器网络已经部署在许多新兴的性能关键应用中，如监测重要基础设施（电网和桥梁）和探测自然灾害（火山和地震）。然而，由于无线传感器网络与物理世界深度融合，环境噪声、物理现象的动态变化以及网络部署的不准确性等不确定性因素往往会导致无线传感器网络的性能发生显著变化。本项目开发了一种原则性的网络设计和分析方法来保证无线传感器网络的性能。与现有的基于启发式的解决方案相比，这种方法采用了数据融合，一种先进的信息处理方案，使传感器能够有效地协作，提供可预测的网络性能。本项目有四个目标：1)基于已建立的数据融合模型，构建时空感知性能分析框架。该分析捕获了由事件检测和误报概率、融合模型、网络密度和物理不确定性（包括噪声和部署不准确性）定义的时空覆盖之间的基本关系。2)利用移动传感器重新配置网络能力以响应物理动态的数据融合方案。3)统一的融合和通信架构抽象，允许开发人员使用组级原语实现和优化网络协议。4)一种模型驱动的媒介访问控制协议，该协议可以在协同数据处理中实现可预测的吞吐量和延迟。该项目将对许多需要严格传感和通信性能的关键应用产生影响。这个项目的成果将被整合到外展活动、课程开发和学生辅导中。