Collaborative Research: NECO: A Mathematical Framework for the Performance Evaluation of Large-Scale Sensor Networks

合作研究：NECO：大规模传感器网络性能评估的数学框架

• 批准号: 0831707
• 负责人: Jeffrey Kharoufeh
• 金额: $ 30万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0831707&HistoricalAwards=false
• 关键词: Collaborative; Research; NECO; Mathematical; Framework

Collaborative Research:A Mathematical Framework for the Performance Evaluation of Large-Scale Sensor NetworksJeffrey P. Kharoufeh and Rusty O. Baldwin This grant provides funding for creating and analyzing mathematical models of large-scale wireless sensor networks (WSNs). A WSN is a collection of sensing devices linked via a wireless transmission medium for the purpose of sensing and conveying information about objects, their surroundings, and their interactions, without human intervention. When the information is time sensitive, the network?s performance capabilities are critical to meeting quality-of-service guarantees. However, assessing network performance is difficult because the location of the data is generally not known a priori, or it may not exist in the network at all, sensors have limited energy reserves and fail when these reserves are exhausted, sensors may fail due a harsh operating environment, and communication channel quality may vary with time, impacting the timely delivery of critical data. These characteristics distinguish sensor networks from other communication networks and significantly complicate the task of modeling and predicting their performance over time. If successful, the results of this research will lead to fundamental principles, effective algorithms and optimization schemes that will enhance the design, analysis, control and realization of WSNs. Using a queueing network-based model, specific performance parameters will be investigated including the network energy expenditure, average time required to respond to queries and the query success rate (the proportion of queries that are answered on time). The sensitivity of the network?s performance to query deadline distributions, sensor failures and the network?s environment will also be assessed. Finally, optimization of the network?s parameters will be investigated to ensure resilience in various operating environments.

协作研究：大规模传感器网络性能评估的数学框架杰弗里·P·哈鲁菲和鲁斯蒂·O·鲍德温这笔赠款为创建和分析大规模无线传感器网络(WSN)的数学模型提供资金。WSN是通过无线传输介质链接的传感设备的集合，目的是在没有人工干预的情况下感知和传达关于对象及其周围环境和交互的信息。当信息是时间敏感的时候，S的网络性能能力是实现服务质量保证的关键。然而，评估网络性能是困难的，因为数据的位置通常是先验未知的，或者它可能根本不存在于网络中，传感器具有有限的能量储备并且在这些储备耗尽时发生故障，传感器可能由于恶劣的操作环境而失效，并且通信信道质量可能随时间而变化，从而影响关键数据的及时传递。这些特征将传感器网络与其他通信网络区分开来，并使建模和预测其随时间推移的性能的任务变得非常复杂。如果研究成功，本文的研究成果将为无线传感器网络的设计、分析、控制和实现提供基本原理、有效的算法和优化方案。使用基于排队网络的模型，将调查具体的性能参数，包括网络能量消耗、响应查询所需的平均时间和查询成功率(及时回答查询的比例)。还将评估网络？S性能对查询截止时间分布、传感器故障和网络？S环境的敏感度。最后，将研究网络参数的优化，以确保S在各种运行环境下的弹性。