权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CNS Core: Small: Wireless Network Control in Uncooperative and Adversarial Environments

CNS 核心：小型：不合作和对抗环境中的无线网络控制

基本信息

批准号：
1907905
负责人：
Eytan Modiano
金额：
$ 50万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907905&HistoricalAwards=false
关键词：
CNS Core Small Wireless Network

项目摘要

Recent growth in mobile and media-rich applications has greatly increased the demand for wireless capacity, straining wireless networks. This dramatic increase in demand poses a challenge for current wireless networks, and calls for new network algorithms that make better use of scarce wireless resources. Moreover, modern communication networks frequently operate in unfriendly environments, where some of the users may be uncooperative, or even malicious, and try to disrupt network services. This project develops network control algorithms that operate effectively in adversarial environments that increasingly characterize realistic network settings, thus leading to dramatic improvement in network performance and enabling emerging wireless applications.This project develops a new optimization framework for networks where some of the nodes, as well as the external dynamics (e.g., link rates, exogenous arrivals), may be uncooperative and exhibit adversarial or even malicious behavior. This novel framework envisions network control algorithms that are "secure-by-design", in the face of adversarial dynamics. Moreover, the developed control algorithms will have a "robust optimization" flavor, in the sense that they will be designed from the outset to perform well under worst-case conditions, while maintaining nearly optimal performance under normal conditions. The research agenda includes the following tasks: (i) Network Optimization in Uncooperative Environments: Use techniques from model-based reinforcement learning to develop control algorithms for networks where a subset of nodes are uncontrollable and use some unknown stationary control policy. (ii) Network Optimization in Adversarial Environments: Develop online learning algorithms for maximizing throughput and network utility in networks where uncontrollable nodes can take arbitrary and possibly non-stationary actions. (iii) Network Optimization in Malicious Environments: Characterize the network's performance in overflow due to adversarial flow injections, develop optimal flow injection policies for the adversary, and network control algorithms to mitigate the effect of such adversarial flow injections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

最近移动和富媒体应用程序的增长大大增加了对无线容量的需求，使无线网络紧张。这种需求的急剧增长对当前的无线网络提出了挑战，并要求新的网络算法更好地利用稀缺的无线资源。此外，现代通信网络经常在不友好的环境中运行，其中一些用户可能不合作，甚至恶意，并试图破坏网络服务。该项目开发的网络控制算法可在对抗环境中有效运行，这些环境日益成为现实网络设置的特征，从而显著提高网络性能并使新兴无线应用成为可能。该项目为网络开发了一个新的优化框架，其中一些节点以及外部动态（例如，链接率，外生到达）可能不合作并表现出对抗甚至恶意行为。这个新颖的框架设想了“设计安全”的网络控制算法，面对对抗的动态。此外，开发的控制算法将具有“鲁棒优化”的味道，从某种意义上说，它们将从一开始就被设计成在最坏情况下表现良好，同时在正常条件下保持近乎最佳的性能。研究议程包括以下任务：(i)非合作环境下的网络优化：利用基于模型的强化学习技术，为节点子集不可控的网络开发控制算法，并使用一些未知的平稳控制策略。（ii）对抗环境中的网络优化：开发在线学习算法，在不可控节点可能采取任意和可能的非平稳行动的网络中最大化吞吐量和网络效用。（iii）恶意环境下的网络优化：描述由于对抗性流量注入而导致的网络溢出性能，为对手制定最佳的流量注入策略，以及网络控制算法，以减轻这种对抗性流量注入的影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。