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的增长极大地增加了对无线容量的需求，给无线网络带来压力。需求的急剧增长对当前的无线网络提出了挑战，并需要新的网络算法来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以在对抗性环境中有效运行，这些环境越来越具有现实网络设置的特征，从而显着提高网络性能并支持新兴的无线应用。该项目为网络开发了一种新的优化框架，其中一些节点也由于外部动态（例如链接速率、外源到达）可能不合作并表现出对抗性甚至恶意行为。这种新颖的框架设想了面对对抗动态时“设计安全”的网络控制算法。此外，所开发的控制算法将具有“鲁棒优化”的风格，即它们从一开始就被设计为在最坏情况条件下表现良好，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）不合作环境中的网络优化：使用基于模型的强化学习技术为节点子集不可控并使用一些未知的固定控制策略的网络开发控制算法。 (ii) 对抗环境中的网络优化：开发在线学习算法，以最大化网络中的吞吐量和网络效用，其中不可控的节点可以采取任意且可能不稳定的操作。 (iii) 恶意环境中的网络优化：表征由于对抗性流注入而导致的网络溢出性能，为对手制定最佳的流注入策略，以及网络控制算法以减轻此类对抗性流注入的影响。该奖项反映了 NSF 的法定使命通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。