RINGS: Resilient and Low-Latency Networks for Situation Awareness in the Factory of the Future

RINGS：用于未来工厂态势感知的弹性和低延迟网络

基本信息

批准号：
2148251
负责人：
Moe Win
金额：
$ 100万
依托单位：
Massachusetts Institute of Technology
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：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148251&HistoricalAwards=false
关键词：
RINGS Resilient Low Latency Networks

项目摘要

Timely and accurate situation awareness (e.g., knowing the state of agents such as people, objects, and vehicles) is a key enabler for automating the factory of the future (FoF). The Industry 4.0 paradigm foresees a myriad of heterogeneous agents that robustly and reliably communicate and coordinate their actions. FoF environments manifest harsh conditions for sensing and communication, resulting in information latency and data uncertainty. This may impair current state-of-the-art localization and decision-making procedures and calls for techniques that are robust to outdated and inaccurate information in FoF networks. The project aims to establish a new framework that revolutionizes the approach to localization and decision making by designing latency-aware and latency-resilient techniques for FoF environments. This project includes two main aspects: latency-aware network localization and latency-resilient decision making in multi-agent FoF networks. First, the team of researchers is developing a framework for the design of latency-aware localization techniques. These techniques are robust to disruptive events by adapting accuracy-latency tradeoffs and by fusing data obtained from heterogeneous devices. Second, the team of researchers is developing latency-resilient decision-making algorithms. These algorithms are resilient to outdated information by efficiently allocating network resources and by coordinating the agents for accelerated learning. This project will serve as a foundation for large-scale multi-agent FoF networks that are capable of operating in highly dynamic environments in the presence of latency. The methodologies developed in this project are robust to outdated and inaccurate information, paving the way to fully automated industries in the next decade.This award reflects NSF's statutory mission and has been deemed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.

及时、准确的态势感知（例如，了解人、物体和车辆等主体的状态）是未来工厂自动化 (FoF) 的关键推动因素。工业 4.0 范式预见了无数异构代理，它们能够稳健、可靠地进行通信并协调其行动。 FoF 环境表现出恶劣的传感和通信条件，导致信息延迟和数据不确定性。这可能会损害当前最先进的定位和决策程序，并需要对 FoF 网络中过时和不准确信息具有鲁棒性的技术。该项目旨在建立一个新框架，通过为 FoF 环境设计延迟感知和延迟弹性技术，彻底改变本地化和决策的方法。该项目包括两个主要方面：延迟感知网络本地化和多代理 FoF 网络中的延迟弹性决策。首先，研究团队正在开发一个用于设计延迟感知本地化技术的框架。这些技术通过调整准确性-延迟权衡并融合从异构设备获得的数据，对破坏性事件具有鲁棒性。其次，研究团队正在开发延迟弹性决策算法。这些算法通过有效地分配网络资源并协调代理来加速学习，从而能够适应过时的信息。该项目将作为大规模多代理 FoF 网络的基础，该网络能够在存在延迟的高度动态环境中运行。该项目开发的方法对于过时和不准确的信息具有鲁棒性，为未来十年的完全自动化工业铺平了道路。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

期刊论文数量（16）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

3GPP-Compliant Datasets for xG Location-Aware Networks

DOI：
10.1109/ojvt.2023.3340993
发表时间：
2024
期刊：
IEEE Open Journal of Vehicular Technology
影响因子：
6.4
作者：
Andrea Conti;Gianluca Torsoli;C. A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;M. Win
通讯作者：
Andrea Conti;Gianluca Torsoli;C. A. Gómez-Vega;Alessandro Vaccari;Gianluca Mazzini;M. Win

Beyond 5G Localization at mm Waves in 3GPP Urban Scenarios with Blockage Intelligence (Invited Paper)

超越 5G 毫米波本地化，在 3GPP 城市场景中利用阻塞智能（特邀论文）

DOI：
10.1109/plans53410.2023.10140112
发表时间：
2023
期刊：
IEEE
影响因子：
0
作者：
Torsoli, Gianluca;Win, Moe Z.;Conti, Andrea
通讯作者：
Conti, Andrea

xG-Loc: 3GPP-compliant datasets for xG location-aware networks

xG-Loc：适用于 xG 位置感知网络的符合 3GPP 的数据集

DOI：
10.21227/rper-vc03
发表时间：
2023
期刊：
IEEE DataPort
影响因子：
0
作者：
Conti, Andrea Conti;Torsoli, Gianluca Torsoli;Gómez-Vega, Carlos Antonio;Vaccari, Alessandro Vaccari;Win, Moe Z.
通讯作者：
Win, Moe Z.

UWB Localization-of-Things Via Soft Information: Network Experimentation in Indoor Environment

DOI：
10.1109/icassp49357.2023.10094631
发表时间：
2023-06
期刊：
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
影响因子：
0
作者：
C. A. Gómez-Vega;M. Win;A. Conti
通讯作者：
C. A. Gómez-Vega;M. Win;A. Conti

Soft Information-Based Localization for 5G Networks and Beyond