NeTS: Small: Collaborative Research: Large Scale Networks and Information Flow: From Emergent Behavior to Algorithm Design

本课题通过应用不同的随机网络模型，研究使用分布式通信算法的复杂大规模空间网络的性能。虽然之前关于随机空间网络的大部分工作都集中在拓扑方面和集中式算法设计上，但本项目的重点是开发分布式算法，并描述这种网络必须满足的所需不变网络属性，以允许有效地实现分布式算法。项目的预期结果包括(1)ef&amp；#64257的去中心化算法；客户路径导航，(2)分散的信息算法&amp；#64258；在随机网络中，(3)分散的信息传播算法，(4)随机网络中的分布式能量管理，(5)分布式拓扑控制以保持基于渗透的连通性。该项目将(1)增加对社会日益依赖的复杂信息系统的理解，(2)为实用网络架构和协议提供设计指南，(3)通过出版物传播科学技术成果，(4)进一步发展数学（概率论）和统计物理等相关学科，(5)通过计划中的随机网络联合课程影响本科和研究生教育。夏季系列讲座，(6)通过暑期研究项目提高本科生的研究经验，(7)鼓励妇女和少数民族参与网络研究。

This project studies the performance of complex large-scale spatial networks that use distributed communication algorithms by applying different random network models. While much of the previous work on random spatial networks has focused on topological aspects and on centralized algorithm design, the emphasis of this project is on developing distributed algorithms and on characterizing desired invariant network properties that such networks must satisfy to allow for the efficient implementation of distributed algorithms. Expected results from the project include (1) decentralized algorithms for efficient path navigation, (2) decentralized algorithms for information flow in random networks, (3) decentralized algorithms for information dissemination, (4) distributed energy management in random networks, and (5) distributed topology control for maintaining percolation-based connectivity. This project will (1) increase the understanding of complex information systems upon which society increasingly depends, (2) lead to design guidelines for practical network architectures and protocols, (3) disseminate the scientific and technological findings through publications, (4) further the development of related disciplines such as mathematics (probability) and statistical physics, (5) impact undergraduate and graduate education through a planned joint course on random networks, and a summer lecture series, (6) enhance research experiences for undergraduates through summer research projects, and (7) encourage the participation of women and minorities in networking research.