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

该项目为随机无线网络(RWN)的大规模协同实施和部署提供了理论框架分析、工程设计规则和指导方针。这项研究通过满足未来的数据容量需求和服务质量目标，促进了对新兴无线网络的理解，并为RWN研究界做出了贡献，并有可能将这些发现转化为横跨交通、灾难恢复、医疗保健和其他部门的广泛复杂网络应用。此外，与拟议研究紧密结合的一个重要教育目标是招募和培养下一代网络工程师。该项目的研究成果通过各种公共和学术场所传播，具体而言，该项目在节点合作的情况下对大规模RWN进行端到端的性能分析。它的目的是评估和优化多层设计的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来解决多跳协作RWN建模方面的新挑战：1)将马尔科夫链与点过程相结合进行跨层建模；2)根据RWN的随机性、动态性和网络协作特性识别RWN的最优条件；3)模型验证和Mesh网络中的实例研究。不同于以往对协作无线网络或随机网络单独建模的研究，本课题将马尔科夫链和点过程结合起来对大规模协作无线网络进行建模。由于多层协作行为的复杂性、网络的随机性和可扩展性，这是具有挑战性的。这种建模方法对于自组织RWN来说是至关重要的，因为许多网络应用都倾向于这种合作，以提高它们的服务质量和可靠性，降低它们的功耗和干扰，增加它们的空间或频率重用。该项目的成功将大大推进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.