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

本计画提供随机无线网路之理论架构分析、工程设计规则与大规模协同实施与部署之指导。该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，并有可能将研究结果转化为广泛的复杂网络应用，包括交通运输，灾难恢复，医疗保健和其他部门。此外，一个重要的教育目标紧密结合拟议的研究是招募和教育下一代的网络工程师。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，该项目在节点合作的情况下对大规模RWN进行端到端性能分析。它的目的是估计和优化性能的多层设计的RWN，以满足所需的服务质量之前，网络部署。该项目通过三个任务解决了多跳协作RWN建模的新挑战：1）将马尔可夫链与点过程集成用于跨层建模; 2）基于RWN的随机性，动态性和网络协作特性识别RWN的最佳条件; 3）模型验证和网状网络的案例研究。与以往的研究中，合作无线网络或随机网络分别建模，本项目集成马尔可夫链和点过程建模大规模的合作无线网络。由于多层协作行为的复杂性、网络随机性和可扩展性，这具有挑战性。这种建模方法对于自组织RWN是至关重要的，因为许多网络应用都倾向于这种合作，以提高其服务质量和可靠性，降低其功耗和干扰，并增加其空间或频率重用。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.