NeTS: EAGER: A Cross-layer End-to-End Performance Modeling Approach for Large-Scale Random Wireless Networks with Node Cooperative Behavior

NetS：EAGER：具有节点协作行为的大规模随机无线网络的跨层端到端性能建模方法

• 批准号: 1451629
• 负责人: Honggang Wang
• 金额: $ 10.96万
• 依托单位: University of Massachusetts, Dartmouth
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451629&HistoricalAwards=false
• 关键词: NeTS; EAGER; Cross; layer; End

This project provides theoretical framework analyses, engineering design rules, and guidelines for large-scale cooperative implementation and deployment of Random Wireless Networks (RWN). The research advances the understanding of emerging wireless networks and contributes to the RWN research community by meeting the future data capacity demand and the goals of quality of service, and has the potential to transform the findings to a broad range of complex network applications spanning transportation, disaster recovering, healthcare, and other sectors. In addition, an important education objective tightly coupled with the proposed research is to recruit and educate the next generation of network engineers. The findings from this project are disseminated via various public and academic venues.Specifically, this project conducts end-to-end performance analysis of large scale RWN in the presence of node cooperation. It aims at estimating and optimizing the performance of multi-layer-designed RWN to meet the required quality of service prior to network deployment. The project addresses new challenges in modeling multi-hop cooperative RWN through three tasks: 1) integration of Markov Chain with point process for cross-layer modeling; 2) identification of the optimal condition of RWN based on its randomness, dynamics and network cooperative characteristics; and 3) model validation and a case study in mesh networks. Unlike previous studies where cooperative wireless networks or random networks are modeled separately, this project integrates Markov chain and point process for modeling large scale cooperative RWN. This is challenging due to the complexity of cooperative behavior of multiple layers, network randomization and scalability. This modeling approach is crucial for self-organized RWN, because many network applications favor this cooperation in order to improve their quality of service and reliability, reduce their power consumption and interference, and increase their spatial or frequency reuse. The success of this project can significantly advance RWN design and deployment, improve quality of service, capacity, and coverage of the next generation wireless networks.

该项目提供了理论框架分析，工程设计规则和指南，用于大规模合作实施和部署随机无线网络（RWN）。该研究通过满足未来的数据容量需求和服务质量的目标来促进对新兴无线网络的理解，并为RWN研究社区做出了贡献，并有可能将发现转换为跨越广泛的复杂网络应用程序，涵盖运输，灾难恢复，医疗保健，医疗保健和其他领域。此外，一个重要的教育目标与拟议的研究紧密相结合，是招募和教育下一代网络工程师。该项目的发现是通过各种公共和学术场所传播的。特别是，该项目在淋巴结合作的情况下对大规模RWN进行了端到端的性能分析。它旨在估算和优化多层设计的RWN的性能，以满足网络部署之前所需的服务质量。该项目通过三个任务解决了多跳合作RWN的新挑战：1）马尔可夫链与跨层建模的点过程集成； 2）基于其随机性，动力学和网络合作特征来识别RWN的最佳条件； 3）模型验证和网络网络中的案例研究。与以前的研究无线网络或随机网络分别建模的研究不同，该项目集成了Markov链和用于建模大型合作RWN的点过程。由于多个层的合作行为，网络随机化和可扩展性的复杂性，这是具有挑战性的。这种建模方法对于自组织的RWN至关重要，因为许多网络应用程序都有利于这种合作，以提高其服务质量和可靠性，减少其功耗和干扰，并增加其空间或频率重复使用。该项目的成功可以大大提高RWN设计和部署，提高下一代无线网络的服务质量，容量和覆盖范围。