Visibility and Interactive Information Sharing in Collaborative Sensing Systems

协作传感系统中的可见性和交互式信息共享

基本信息

批准号：
1809327
负责人：
Gustavo de Veciana
金额：
$ 45万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809327&HistoricalAwards=false
关键词：
Visibility Interactive Information Sharing Collaborative

项目摘要

Visibility and Interactive Information Sharing in Collaborative Sensing SystemsSelf-driving vehicles and mobile robots have the potential to deliver transformative technological and societal changes. In order to make autonomous decisions, nodes need to have a reasonable degree of situational awareness achieved through recognition and tracking of entities in dynamic environments. This may not be possible in partially occluded environments, where individual nodes may have limited visibility, unless nodes participate in collaborative sensing, i.e., share sensed information. Sharing raw/processed real-time sensing data with centralized resources in the cloud or at the network edge poses potentially high communication/computational burdens, particular in safety critical settings requiring low latency. This motivates the need to study distributed collaborative sensing frameworks leveraging powerful algorithms for tracking and deep learning models for reliable recognition/classification tasks. Of particular interest is a characterization of what collaborating sensors can ``see'' in occluded environments and how one should realize information sharing in resource constrained settings to fairly optimize what nodes ``know'', i.e., their situational awareness. The proposed research effort will advance the state-of-the-art in collaborative sensing systems which are expected to benefit the field and society more broadly, through planned efforts in education innovation, achieving diversity, engaging the community and industry, and disseminating results to a wider public.This proposal centers on the study of collaborative sensing in obstructed/dynamic environments, such as might be used to enable self-driving vehicles and autonomous robots. The central challenge is to achieve an unprecedented level of real-time situational awareness based on distributed sensing resources in a possibly communication and/or computationally constrained setting. The proposed research integrates three research thrusts. The first is the advancement of the fundamental understanding what is visible to sets of distributed sensing units in stochastic environments. This work will leverage stochastic geometric models and analysis to provide robust quantitative performance assessment of `visibility' for typical random environments. The performance limits determined in this research thrust will inform what a distributed system can ``know" in resource constrained settings. The second thrust is the development of fundamental underpinnings of distributed collaborative sensing with a focus on the optimization of interactive information sharing and/or adaptation to changing environmental contexts so as to jointly maximize situational awareness amongst autonomous yet collaborating nodes. We will provide new approaches driven by structural properties of the optimization problems (e.g., submodularity) and interactive information sharing protocols to facilitate distributed object recognition and tracking. The third thrust is the development of a scaled-down platform for controlled and reproducible experimentation of alternative collaborative sensing system designs. The last thrust is not only geared at providing platform to advance the research but is also an activity to engage a substantial number of undergraduates and a springboard to our educational efforts.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.

可见性和交互式信息共享在协作感应系统自动驾驶的车辆和移动机器人中有可能提供变革性的技术和社会变化。为了做出自主决策，节点需要通过在动态环境中识别和跟踪实体实现合理程度的情境意识。在部分遮挡的环境中，这可能是不可能的，在某种环境中，单个节点的可见性可能有限，除非节点参与协作感应感，即共享感知的信息。与云中的集中资源或网络边缘共享原始/处理过的实时传感数据，构成了潜在的高通信/计算负担，尤其是在需要较低延迟的安全关键环境中。这促使需要研究分布的协作感应框架，利用强大的算法来跟踪和深度学习模型，以实现可靠的识别/分类任务。特别有趣的是，在封闭环境中可以进行协作传感器可以``See''的表征，以及如何在资源约束设置中实现信息共享，以公平地优化节点``知道''，即他们的情境意识。拟议的研究工作将推进协作感应系统中的最先进的工作，预计通过计划的教育创新，实现多样性，吸引社区和行业，并将结果传播给更广泛的公共公众。该提案中心在障碍/动态环境中的协作研究中心，诸如可自动驾驶的启动和自动化的启示。核心挑战是基于在可能的通信和/或计算约束的设置中基于分布式感应资源的实时情境意识的前所未有的水平。拟议的研究结合了三项研究。首先是基本理解随机环境中的一组分布式传感单元可见的基础。这项工作将利用随机几何模型和分析来为典型的随机环境提供“可见性”的强大定量性能评估。在这项研究推力中确定的绩效限制将告知分布式系统可以在资源约束的设置中``知道''。第二个力量是开发分布式协作感知的基本基础的基础，重点是优化交互式信息共享和/或适应环境环境的适应环境环境，从而在上情境中提供了新的构图，从而使自己最大程度地提高了自动化的态度。优化问题的属性（例如，supporlibleity）和交互式信息共享协议，以促进分布式对象识别和跟踪。第三个推力是缩小的平台，用于控制和重现的实验，以替代替代性协作系统，而不是为我们提供了一个启动的弹性。努力。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准来评估值得支持的。

项目成果

期刊论文数量（19）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

On Submodularity of Quadratic Observation Selection in Constrained Networked Sensing Systems

约束网络传感系统中二次观测选择的子模性

DOI：
10.23919/acc.2019.8814899
发表时间：
2019
期刊：
2019 American Control Conference (ACC
影响因子：
0
作者：
Ghasemi, Mahsa;Hashemi, Abolfazl;Topcu, Ufuk;Vikalo, Haris
通讯作者：
Vikalo, Haris

Evolutionary Subspace Clustering: Discovering Structure in Self-expressive Time-series Data

DOI：
10.1109/icassp.2019.8682405
发表时间：
2019-05
期刊：
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
Abolfazl Hashemi;H. Vikalo
通讯作者：
Abolfazl Hashemi;H. Vikalo

Optimizing Timely Coverage in Communication Constrained Collaborative Sensing Systems

DOI：
10.1109/tcns.2022.3203806
发表时间：
2020-06
期刊：
2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT)
影响因子：
0
作者：
J. Rahal;G. Veciana;Takayuki Shimizu;Hongsheng Lu
通讯作者：
J. Rahal;G. Veciana;Takayuki Shimizu;Hongsheng Lu

Service Placement for Real-Time Applications: Rate-Adaptation and Load-Balancing at the Network Edge