EARS: Cloud-based Oblivious Spectrum Mapping and Allocation

EARS：基于云的不经意频谱映射和分配

• 批准号: 1642973
• 负责人: John Shea
• 金额: $ 40.3万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1642973&HistoricalAwards=false
• 关键词: EARS; Cloud; based; Oblivious; Spectrum

Dynamic spectrum access (DSA) techniques offer the potential to use the radio spectrum more efficiently by allowing radios to sense which parts of the radio spectrum are not currently being used by the licensed users or other opportunistic users. However, detecting the presence of transmissions in a particular band is difficult because of randomness in the radio propagation environment and because radio signals can be received at very low power levels. Sensing the radio spectrum can be made more accurate by collecting and processing the sensing information from multiple radios. However, in combining this information, the locations and characteristics of the sensing radios may be revealed to the other radios or to various companies involved in collecting and combining the information. This research project will develop novel techniques to protect the privacy of users involved in sensing the radio spectrum as part of a DSA system. The techniques are built on top of secure computing primitives, such as garbled circuits, which uses cryptographic techniques to allow users to compute a result without any of the parties being able to know the other parties inputs to the computation. Although complete privacy is not possible in a spectrum sensing system, this project aims to develop systems that achieve k-anonymity, in which a user's location and capabilities may only be reduced to one of k possibilities. A significant challenge in developing such techniques is that many secure computing primitives require high computational complexity, and thus cannot be implemented on many DSA devices, such as future generations of cellular phones. Thus, the project will develop privacy-preserving spectrum sensing techniques that have sufficiently low complexity to be implemented on such devices. One of the approaches to achieving this is to partition the computation between the devices and a cloud-computing server. These techniques will be implemented on a software-defined radio testbed that interfaces with a commercial cloud computing resource to allow testing using real radio signals and real computing platforms.

动态频谱接入（DSA）技术通过允许无线电设备感测无线电频谱的哪些部分当前未被许可用户或其他机会用户使用来提供更有效地使用无线电频谱的可能性。然而，由于无线电传播环境中的随机性并且由于可以以非常低的功率电平接收无线电信号，因此检测特定频带中的传输的存在是困难的。通过收集和处理来自多个无线电的感测信息，可以使感测无线电频谱更加准确。然而，在组合该信息时，感测无线电的位置和特性可能被透露给其他无线电或参与收集和组合信息的各种公司。该研究项目将开发新技术，以保护参与感知无线电频谱的用户的隐私，作为DSA系统的一部分。这些技术建立在安全计算原语（例如乱码电路）之上，其使用密码技术来允许用户计算结果，而任何一方都不能知道其他方对计算的输入。虽然在频谱感知系统中不可能实现完全的隐私，但该项目旨在开发实现k-匿名的系统，其中用户的位置和能力可能仅减少到k种可能性之一。开发这种技术的一个重大挑战是，许多安全计算原语需要高计算复杂度，因此不能在许多DSA设备上实现，例如未来几代的蜂窝电话。因此，该项目将开发隐私保护频谱感知技术，这些技术具有足够低的复杂性，可以在此类设备上实现。实现这一点的方法之一是在设备和云计算服务器之间划分计算。 这些技术将在一个软件定义的无线电测试平台上实现，该平台与商业云计算资源接口，允许使用真实的无线电信号和真实的计算平台进行测试。

项目成果

An adjacency matrix approach to delay analysis in temporal networks

• DOI: 10.1109/milcom.2017.8170866
• 发表时间: 2017-10
• 期刊: MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)
• 作者: J. Shea;J. Macker

Reinforcement Learning for Mixed Cooperative/Competitive Dynamic Spectrum Access

混合合作/竞争动态频谱访问的强化学习

• DOI: 10.1109/dyspan.2019.8935725
• 发表时间: 2019
• 期刊: Proceedings of the 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN
• 作者: Bowyer, Caleb;Greene, David;Ward, Tyler;Menendez, Marco;Shea, John;Wong, Tan
• 通讯作者: Wong, Tan

Privacy-Preserving Localization using Enclaves

使用 Enclave 进行隐私保护本地化

• DOI: 10.1109/uemcon53757.2021.9666706
• 发表时间: 2021
• 期刊: Electronics & Mobile Communication Conference (UEMCON’21
• 作者: Khan, Arslan;Choi, Joseph;Tian, Dave;Ward, Tyler;Butler, Kevin;Traynor, Patrick;Shea, John;Wong, Tan
• 通讯作者: Wong, Tan

A Deep Q-Learning Dynamic Spectrum Sharing Experiment

深度 Q-Learning 动态频谱共享实验

• DOI: 10.1109/icc42927.2021.9500983
• 发表时间: 2021
• 期刊: ICC 2021 - IEEE International Conference on Communications Proceedings
• 作者: Shea, John M.;Wong, Tan F.
• 通讯作者: Wong, Tan F.

Identifying Bottleneck Nodes using Packet Delay Statistics

使用数据包延迟统计识别瓶颈节点

• DOI: 10.1109/milcom47813.2019.9021045
• 发表时间: 2019
• 期刊: Proceedings of the IEEE Military Communications Conference (MILCOM
• 作者: Marker, Joshua;Shea, John M.;Wong, Tan F.;Graves, Eric;Yu, Paul L.
• 通讯作者: Yu, Paul L.