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

本项目为随机无线网络（RWN）的大规模协同实现和部署提供理论框架分析、工程设计规则和指导方针。该研究提高了对新兴无线网络的理解，通过满足未来数据容量需求和服务质量目标，为RWN研究界做出了贡献，并有可能将研究结果转化为广泛的复杂网络应用，涵盖交通运输、灾难恢复、医疗保健和其他领域。此外，与所提出的研究紧密相关的一个重要教育目标是招募和教育下一代网络工程师。该项目的研究结果通过各种公共和学术场所传播。具体而言，本项目对存在节点协作的大规模RWN进行端到端性能分析。它的目的是在网络部署前对多层RWN的性能进行估计和优化，以满足业务质量要求。本课题通过三个方面的工作解决了多跳协同RWN建模的新挑战：1)将马尔可夫链与点过程相结合进行跨层建模；2)基于RWN的随机性、动态性和网络协同性的最优条件辨识；3)网状网络的模型验证和实例研究。与以往的研究不同，该项目将马尔可夫链和点过程结合起来，对大规模的协作无线网络或随机网络进行建模。由于多层协作行为的复杂性、网络的随机性和可扩展性，这是一个具有挑战性的问题。这种建模方法对于自组织RWN至关重要，因为许多网络应用程序支持这种合作，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用。该项目的成功将显著推进RWN的设计和部署，提高下一代无线网络的服务质量、容量和覆盖范围。

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.