CAREER: Distributed Inference-Making via Crowdsensing

职业:通过群体感知进行分布式推理

基本信息

  • 批准号:
    2302197
  • 负责人:
  • 金额:
    $ 48.98万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-10-15 至 2026-05-31
  • 项目状态:
    未结题

项目摘要

Crowdsensing systems allow human crowd participants carrying smart devices to contribute sensing measurements from built-in sensors in their devices toward a distributed inference-making task. Some crowdsensing systems also employ human crowd participants as humans-as-sensors where humans themselves observe a phenomenon and contribute subjective inferences obtained. The human-powered nature of crowdsensing makes the performance of such systems to become dependent, in addition to aspects of signal processing for extracting information about a phenomenon from observations made, on factors pertinent to human nature. This makes optimization of crowdsensing performance to require jointly addressing signal processing and human aspects, which this project aims to address by converging interdisciplinary perspectives. The project will significantly advance human-powered societal-scale distributed sensing technologies that can sustain smarter, safer, and more resilient communities as well as will advance human-in-the-loop signal processing capabilities. The project also aims to significantly enhance student engagement, learning, recruitment, and retention through an elaborate plan on integrating research and educational activities as well as will enhance participation of underrepresented populations, including women and minorities. The project develops models, analytical approaches, and optimization techniques that address signal processing aspects jointly with traits pertinent to the nature of human crowd participants to lay the foundations of crowdsensing-based distributed inference-making systems. Specifically, the project aims to: 1) produce novel game theoretic market-based crowdsensing mechanisms that jointly address signal processing and selfishness aspects to enable optimal information acquisition from selfish human crowd participants, under factors such as participatory cost uncertainties, resource constraints, privacy concerns, and dependency structures, in a competitive market environment while providing optimal (monetary or nonmonetary) incentives to induce their desired participation; 2) produce prospect theoretic models and methods for optimally employing cognitively biased human crowd participants for distributed inference-making tasks; 3) analytically unravel the impact of attacks from malicious human crowd participants, who can challenge the integrity of the contributed data, and develop mitigation techniques; and, 4) produce a crowdsensing testbed that supports a variety of smart devices and applications for analysis and performance evaluation of crowdsensing techniques under real-world operating conditions.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.
人群感知系统允许携带智能设备的人类人群参与者将来自其设备中的内置传感器的感测测量结果贡献给分布式推理任务。一些群体感测系统还采用人类群体参与者作为人类传感器,其中人类自己观察现象并贡献所获得的主观推断。人群感知的人力性质使得这种系统的性能除了用于从所进行的观察中提取关于现象的信息的信号处理的方面之外,还取决于与人性有关的因素。这使得优化众测性能需要共同解决信号处理和人类方面的问题,该项目旨在通过融合跨学科的观点来解决这一问题。该项目将显著推进人力驱动的社会规模分布式传感技术,这些技术可以维持更智能、更安全、更有弹性的社区,并将推进人在环信号处理能力。该项目还旨在通过一项关于整合研究和教育活动的详细计划,大大提高学生的参与、学习、招聘和保留,并将提高包括妇女和少数民族在内的代表性不足的人口的参与。该项目开发模型,分析方法和优化技术,解决信号处理方面与人类群体参与者的性质相关的特征,为基于众测的分布式推理系统奠定基础。具体而言,该项目旨在:1)产生新颖的基于博弈论市场的群体感知机制,其联合解决信号处理和自私方面,以在诸如参与成本不确定性、资源约束、隐私问题和依赖结构的因素下,实现从自私的人类群体参与者的最佳信息获取,在竞争激烈的市场环境中,(货币或非货币)激励措施,以促使他们按预期参与; 2)产生前景理论模型和方法,用于最佳地使用认知偏差的人类群体参与者进行分布式推理任务; 3)分析来自恶意人群参与者的攻击的影响,这些参与者可以挑战贡献数据的完整性,并开发缓解技术;并且,在本发明中,4)制作一个支持各种智能设备和应用的人群感知测试平台,用于在真实的环境下对人群感知技术进行分析和性能评估-该奖项反映了NSF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。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Game and Prospect Theoretic Hardware Trojan Testing
博弈与前景理论硬件木马测试
Hardware Trojan Testing with Hierarchical Trojan Types Under Cognitive Biases
认知偏差下分层木马类型的硬件木马测试
Cyber Deception under Strategic and Irrationality Considerations
战略与非理性考量下的网络欺骗
Distributed Detection with Multiple Sensors in the Presence of Sybil Attacks
Spectrum Overselling: An Optimal Auction Design Perspective
频谱超卖:最佳拍卖设计视角
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Swastik Brahma其他文献

On Development of a Game‐Theoretic Model for Deception‐Based Security
基于欺骗的安全博弈论模型的开发
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Satyaki Nan;Swastik Brahma;Charles A. Kamhoua;L. Njilla
  • 通讯作者:
    L. Njilla
Spectrum Bargaining: A Model for Competitive Sharing of Unlicensed Radio Spectrum
频谱讨价还价:未经许可的无线电频谱竞争性共享模型
Towards the design of prospect-theory based human decision rules for hypothesis testing
设计基于前景理论的人类决策规则以进行假设检验
Behavioral Cyber Deception: A Game and Prospect Theoretic Approach
行为网络欺骗:博弈与前景理论方法
  • DOI:
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Satyaki Nan;Swastik Brahma;Charles A. Kamhoua;Nandi O. Leslie
  • 通讯作者:
    Nandi O. Leslie
Portfolio theory based sensor selection in Wireless Sensor Networks with unreliable observations
具有不可靠观测的无线传感器网络中基于组合理论的传感器选择

Swastik Brahma的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Swastik Brahma', 18)}}的其他基金

CAREER: Distributed Inference-Making via Crowdsensing
职业:通过群体感知进行分布式推理
  • 批准号:
    2047701
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Continuing Grant
Targeted Infusion Project: Infusion of Cyber Physical System Education and Research Training in the Undergraduate Curriculum in the College of Engineering at TSU
有针对性的注入项目:将信息物理系统教育和研究培训注入托国立工程学院本科课程
  • 批准号:
    1912414
  • 财政年份:
    2019
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Standard Grant

相似国自然基金

Graphon mean field games with partial observation and application to failure detection in distributed systems
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目

相似海外基金

CAREER: Efficient Large Language Model Inference Through Codesign: Adaptable Software Partitioning and FPGA-based Distributed Hardware
职业:通过协同设计进行高效的大型语言模型推理:适应性软件分区和基于 FPGA 的分布式硬件
  • 批准号:
    2339084
  • 财政年份:
    2024
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Continuing Grant
Distributed Machine Learning Methodology and System for Real-time Inference with Large-scale Point Clouds Towards Mobility Innovation
利用大规模点云进行实时推理的分布式机器学习方法和系统,迈向移动创新
  • 批准号:
    23H00464
  • 财政年份:
    2023
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Grant-in-Aid for Scientific Research (A)
Collaborative Research: CIF: Small: A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers
合作研究:CIF:小:网络中分布式信息处理、模拟和推理的新范式:小数定律的承诺
  • 批准号:
    2241057
  • 财政年份:
    2022
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Small: A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers
合作研究:CIF:小:网络中分布式信息处理、模拟和推理的新范式:小数定律的承诺
  • 批准号:
    2132815
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Standard Grant
Collaborative Research: Aggregated Monte Carlo: A General Framework for Distributed Bayesian Inference in Massive Spatiotemporal Data
合作研究:聚合蒙特卡罗:海量时空数据中分布式贝叶斯推理的通用框架
  • 批准号:
    2220840
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Standard Grant
Collaborative Research: CIF: Small: A New Paradigm for Distributed Information Processing, Simulation and Inference in Networks: The Promise of Law of Small Numbers
合作研究:CIF:小:网络中分布式信息处理、模拟和推理的新范式:小数定律的承诺
  • 批准号:
    2132843
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Standard Grant
CAREER: Distributed Inference-Making via Crowdsensing
职业:通过群体感知进行分布式推理
  • 批准号:
    2047701
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Continuing Grant
Efficient Distributed DNN Training and Inference
高效的分布式 DNN 训练和推理
  • 批准号:
    543833-2019
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Collaborative Research and Development Grants
Statistical inference for distributed datasets
分布式数据集的统计推断
  • 批准号:
    RGPIN-2016-06296
  • 财政年份:
    2021
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Discovery Grants Program - Individual
Efficient Distributed DNN Training and Inference
高效的分布式 DNN 训练和推理
  • 批准号:
    543833-2019
  • 财政年份:
    2020
  • 资助金额:
    $ 48.98万
  • 项目类别:
    Collaborative Research and Development Grants
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了