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

协作研究：大规模传感器网络性能评估的数学框架。Baldwin该基金为创建和分析大规模无线传感器网络（WSNs）的数学模型提供资金。 无线传感器网络是通过无线传输介质连接的传感设备的集合，用于在没有人为干预的情况下感测和传达有关对象、其周围环境及其交互的信息。 当信息具有时间敏感性时，网络？的性能对于满足服务质量保证至关重要。然而，评估网络性能是困难的，因为数据的位置通常不是先验已知的，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当这些储备耗尽时失效，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递送。 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。 如果成功的话，本研究的结果将导致基本原则，有效的算法和优化方案，将提高无线传感器网络的设计，分析，控制和实现。 使用一个基于嵌入式网络的模型，将研究具体的性能参数，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（及时回答的查询比例）。 网络的敏感性？的性能查询截止日期分布，传感器故障和网络？的环境也将进行评估。 最后，网络优化？的参数进行调查，以确保在各种业务环境中的弹性。

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.