Collaborative Research:CISE-MSI:DP:CNS:Adaptive Multi-Tiered, Multi-Task Base Station Infrastructure For Communication-Denied Environments

合作研究:CISE-MSI:DP:CNS:用于通信被拒绝环境的自适应多层、多任务基站基础设施

基本信息

  • 批准号:
    2318725
  • 负责人:
  • 金额:
    $ 35.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-10-01 至 2026-09-30
  • 项目状态:
    未结题

项目摘要

Nowadays, cellular networks have emerged as the preeminent communication technology, primarily due to their profound impact on modern society. 5G and beyond offer distinctive features, such as high transmission rate, ultra-reliable low-latency communication, massive machine-type communication, and edge and cloud computing. Nevertheless, the performance of these networks can be susceptible to fluctuations in demand or unanticipated physical damage to the underlying infrastructure - i.e., terrestrial base stations (TBSs) - through natural or man-made disasters. Recent advancements in artificial intelligence, control systems, and autonomy within the realm of cyber-physical systems have facilitated the utilization of autonomous unmanned aerial vehicles (UAVs) and autonomous vehicles to establish temporary cellular networks. These networks rely on flying base stations (FBSs) or vehicular base stations (VBS) in large-scale disasters or events where TBSs are either unavailable or unable to provide the required coverage and quality of service (QoS). In addition to their communication services, these autonomous vehicles can offer diverse functionalities, including rapid mapping of expansive areas, operations in hazardous zones, emergency deliveries, and search-and-rescue missions. However, despite the conceptual, scientific, and engineering advancements achieved thus far, the FBS and VBS infrastructures have not yet reached their full maturity.The principal objective of this project is to develop an adaptive, multi-objective, multitier, and multi-task infrastructure consisting of aerial and vehicular base stations (MTBS). The aim is to improve the coverage and QoS of cellular systems while simultaneously facilitating various services, including situational awareness, package delivery, and target tracking, particularly in scenarios where the number of available vehicles is limited. The research and education agenda is framed around three thrusts: (1) performing fundamental research aimed at developing models and algorithmic tools for developing and implementing an adaptive, multi-tiered collection of UAVs and VBSs, to provide communication infrastructure in challenging environments, such as in a disaster zone; (2) developing trajectory-planning models in MTBS systems to deliver a realistic solution for effectively utilizing the base stations in critical missions wherein a small number of vehicles are available; and (3) evaluating the performance of the proposed models using testbed environments. The testbed and open-source code packages developed in this project will allow educators and field professionals to deploy them in both learning and training environments.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.
如今,蜂窝网络已成为卓越的通信技术,主要是因为它们对现代社会产生了深远的影响。 5G及以上技术具有高传输速率、超可靠低时延通信、海量机器类通信、边缘计算和云计算等显着特征。然而,这些网络的性能可能容易受到需求波动或自然或人为灾害对底层基础设施(即地面基站(TBS))的意外物理损坏的影响。人工智能、控制系统和网络物理系统领域的自主性的最新进展促进了利用自主无人机(UAV)和自动驾驶车辆来建立临时蜂窝网络。在大规模灾难或事件中,TBS 不可用或无法提供所需的覆盖范围和服务质量 (QoS),这些网络依赖于飞行基站 (FBS) 或车载基站 (VBS)。除了通信服务之外,这些自动驾驶车辆还可以提供多种功能,包括快速绘制广阔区域的地图、危险区域的操作、紧急交付以及搜救任务。然而,尽管迄今为止在概念、科学和工程方面取得了进步,FBS 和 VBS 基础设施尚未完全成熟。该项目的主要目标是开发一种自适应、多目标、多层和多任务的系统基础设施包括空中和车载基站(MTBS)。其目的是提高蜂窝系统的覆盖范围和服务质量,同时促进各种服务,包括态势感知、包裹递送和目标跟踪,特别是在可用车辆数量有限的情况下。研究和教育议程围绕三个重点:(1)进行基础研究,旨在开发模型和算法工具,以开发和实施自适应、多层无人机和 VBS 集合,以在具有挑战性的环境中提供通信基础设施,例如在灾区; (2) 开发 MTBS 系统中的轨迹规划模型,以提供现实的解决方案,以便在可用车辆数量较少的关键任务中有效利用基站; (3) 使用测试平台环境评估所提出模型的性能。该项目中开发的测试台和开源代码包将允许教育工作者和现场专业人员将其部署在学习和培训环境中。该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优势和更广泛的评估进行评估,被认为值得支持。影响审查标准。

项目成果

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Mehdi Sookhak其他文献

Sort-then-insert: A space efficient and oblivious model aggregation algorithm for top-k sparsification in federated learning
Sort-then-insert:一种空间高效且不经意的模型聚合算法,用于联邦学习中的 top-k 稀疏化
  • DOI:
    10.1016/j.future.2024.04.022
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Yongzhi Wang;Pengfei Gui;Mehdi Sookhak
  • 通讯作者:
    Mehdi Sookhak
A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation
一种新颖的用于水流模拟的深度学习堆叠长短期记忆方法
  • DOI:
    10.3390/su132313384
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Majid Mirzaei;Haoxuan Yu;Adnan Dehghani;H. Galavi;V. Shokri;Sahar Mohsenzadeh Karimi;Mehdi Sookhak
  • 通讯作者:
    Mehdi Sookhak
Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions
城市地区地表温度空间降尺度的 TsHARP 方法评估
  • DOI:
    10.1016/j.uclim.2022.101265
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    F. Sattari;M. Hashim;Mehdi Sookhak;S. Banihashemi;A. B. Pour
  • 通讯作者:
    A. B. Pour
Internet of everything, networks, applications, and computing systems (IoENACS)
万物互联、网络、应用程序和计算系统 (IoENACS)
A Digital Twin Environment for 5G Vehicle-to-Everything: Architecture and Open Issues
5G 车联网数字孪生环境:架构和开放问题

Mehdi Sookhak的其他文献

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