EARS: SpecSense: Bringing Spectrum Sensing to the Masses

EARS：SpecSense：将频谱传感带给大众

• 批准号: 1642965
• 负责人: Samir Das
• 金额: $ 80万
• 依托单位: SUNY at Stony Brook
• 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=1642965&HistoricalAwards=false
• 关键词: EARS; SpecSense; Bringing; Spectrum; Sensing

With the explosion of mobile data, there is a growing realization that the radio frequency spectrum must be treated as an important resource that is in limited supply. Policy makers and researchers alike are promoting various forms of spectrum sharing models to improve spectrum utilization. Just like any other resource with mismatched demand and supply, all steps towards better utilization of radio spectrum have also increased the need for large scale spectrum monitoring. This serves two key purposes: (i) it helps identify available spectrum opportunities, making spectrum sharing systems more effective, (ii) it can help us develop deeper understanding of spectrum usage and demand over time and space. Large-scale spectrum monitoring can feed into multitudes of 'spectrum-aware' applications forming an entire ecosystem of spectrum data, analytics and apps. The proposed project develops an end-to-end enabling platform called SpecSense to support this vision. SpecSense (i) crowdsources spectrum monitoring using low-cost, low-power custom-designed hardware, and (ii) provides necessary library and interface support for spectrum-aware apps via a central spectrum server/database platform. This project is expected to foster interest in spectrum data marketplaces facilitated by crowdsourced spectrum sensing. This can engender commercial interests in various aspects of the spectrum data ecosystem. In many fields, e.g., healthcare, education, Internet-of-Things, there is a tremendous need for mobile bandwidth and innovation is stunted due to a lack of bandwidth. Success in this project will drive such innovations. The project will also contribute to various educational activities for students with a range of academic preparations.This project addresses several of the core intellectual challenges in developing SpecSense, viz., (1) Exploration of FPGA-based sensors where sensing algorithms are built into the FPGA, with accompanying tools to automatically implement and optimize these algorithms so that they provide the desired trade-off between power and performance; (2) Novel interpolation techniques to estimate spectrum occupancy in both spatial and temporal domains; (3) Algorithms to support optimized selection of sensors to minimize overall sensing cost; (4) Development of an end-to-end testbed and evaluation over a range of spectrum-aware applications. The project team has a range of expertise in topics relevant to the proposal, such as automated hardware design, digital signal processing, detection and estimation, wireless networking, networking algorithms, and networked systems design.

随着移动的数据的爆炸式增长，人们越来越认识到，必须将无线电频谱视为供应有限的重要资源。政策制定者和研究人员都在推广各种形式的频谱共享模式，以提高频谱利用率。就像任何其他需求和供应不匹配的资源一样，更好地利用无线电频谱的所有步骤也增加了对大规模频谱监测的需求。这有两个主要目的：（i）有助识别可用的频谱机会，使频谱共用系统更有效;（ii）有助我们更深入了解频谱在时间和空间上的使用和需求。 大规模频谱监测可以为众多“频谱感知”应用提供支持，从而形成一个完整的频谱数据、分析和应用生态系统。拟议的项目开发了一个名为SpecSense的端到端支持平台，以支持这一愿景。SpecSense（i）使用低成本、低功耗的定制设计硬件进行众包频谱监控，以及（ii）通过中央频谱服务器/数据库平台为频谱感知应用程序提供必要的库和接口支持。预计该项目将促进对众包频谱感知促进的频谱数据市场的兴趣。这可能会在频谱数据生态系统的各个方面产生商业利益。在许多领域，例如，医疗保健、教育、物联网，对移动的带宽存在巨大需求，并且由于缺乏带宽，创新受到阻碍。该项目的成功将推动此类创新。该项目还将为学生的各种教育活动提供一系列的学术准备。该项目解决了开发SpecSense的几个核心智力挑战，即，(1)探索基于FPGA的传感器，其中感测算法内置于FPGA中，伴随有自动实现和优化这些算法的工具，使得它们提供功率和性能之间的期望的权衡;（2）新颖的插值技术，以估计空间域和时间域中的频谱占用;（3）算法，以支持传感器的优化选择，以最小化总体感测成本;（4）开发端到端测试平台，并对一系列频谱感知应用进行评估。项目团队在与提案相关的主题方面拥有一系列专业知识，例如自动化硬件设计、数字信号处理、检测和估计、无线网络、网络算法和网络系统设计。

项目成果

Spectrum Patrolling With Crowdsourced Spectrum Sensors

• DOI: 10.1109/tccn.2019.2939793
• 发表时间: 2020-03
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: A. Bhattacharya;Ayon Chakraborty;Samir R Das;Himanshu Gupta;P. Djurić

Efficient Localization of Multiple Intruders in Shared Spectrum System

• DOI: 10.1109/ipsn48710.2020.00025
• 发表时间: 2020-04
• 期刊: 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
• 作者: Caitao Zhan;Himanshu Gupta;A. Bhattacharya;Mohammad Ghaderibaneh

SpecSense: Crowdsensing for efficient querying of spectrum occupancy

• DOI: 10.1109/infocom.2017.8057113
• 发表时间: 2017-05
• 期刊: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications
• 作者: Ayon Chakraborty;Md. Shaifur Rahman;Himanshu Gupta;Samir R Das

ProCSA: Protecting Privacy in Crowdsourced Spectrum Allocation

• DOI: 10.1007/978-3-030-29959-0_27
• 发表时间: 2019-09
• 作者: Max Curran;Xiao Liang;Himanshu Gupta;Omkant Pandey;Samir R Das

Multiple Transmitter Localization under Time-Skewed Observations

• DOI: 10.1109/dyspan.2019.8935739
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)
• 作者: Mohammad Ghaderibaneh;Mallesham Dasari;Himanshu Gupta