CRII: NeTS: Towards Predictive Communications for UAV based IoT Networks

CRII：NeTS：迈向基于无人机的物联网网络的预测通信

• 批准号: 1755984
• 负责人: Abolfazl Razi
• 金额: $ 17.48万
• 依托单位: Northern Arizona University
• 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: 2018-03-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1755984&HistoricalAwards=false
• 关键词: CRII; NeTS; Towards; Predictive; Communications

More than 2 million Unmanned Aerial Vehicles (UAVs) have shipped in 2016 creating a global market revenue of $4.5 billion, and is projected to increase further, with huge potential to transform modern living in future smart & connected communities. UAVs will significantly impact our daily life by enabling new applications, and simplifying existing applications including transportation, traffic control, remote health monitoring, surveillance, border patrolling, habitat monitoring, and precision agriculture. An important drawback to commercialize UAV-based solutions in these domains is networking inefficiency. Current communication protocols are extremely inefficient in accommodating dynamic topologies of networks of UAVs. This project seeks to address this issue and develop predictive communication strategies by anticipating network topology changes. The research will thus develop cognizant communication protocols for fully autonomous UAV networks by facilitating high-throughput information exchange and eliminating the need for multiple ground control stations. This project aims to develop novel tools for predictive communication for networks of flying objects with heterogeneous maneuverability levels. The key idea is to take preventive actions via communication protocols before anticipated network partitions and/or failures. The proposed methodology relies on developing a universal model to predict motion trajectories of surrounding network nodes. To enable motion profiling, driving force of each object is modeled as a hierarchical generative model with a hidden layer shared among objects of the same type. The use of a novel merge-and-split method provides flexibility in accommodating new object classes with unseen maneuverability levels and repealing intruding objects. Novel methods based on cyclic monoids in category theory will be used to design optimal measurement patterns for network topology prediction. Finally, a predictive routing algorithm will be developed by incorporating the predicted network topology into decision making when looking for the optimal path. A concrete characterization of the tradeoff between the routing optimality and prediction uncertainties will be developed based on random matrix concentration inequalities to pave the road for practical implementation. Graph snappers and hybrid routing techniques will be studied to integrate conventional and predictive routing algorithms to accelerate the routing algorithm execution for large-scale networks.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.

2016年，全球无人机（UAV）出货量超过200万架，创造了45亿美元的全球市场收入，预计还将进一步增长，具有巨大的潜力，将改变未来智能互联社区的现代生活。无人机将通过支持新的应用和简化现有的应用，包括交通、交通控制、远程健康监测、监视、边境巡逻、栖息地监测和精准农业，对我们的日常生活产生重大影响。在这些领域中商业化基于UAV的解决方案的一个重要缺点是联网效率低下。当前的通信协议在适应UAV网络的动态拓扑结构方面效率极低。该项目旨在解决这一问题，并通过预测网络拓扑结构的变化来制定预测性通信策略。因此，该研究将通过促进高吞吐量信息交换和消除对多个地面控制站的需求，为全自主无人机网络开发认知通信协议。 该项目旨在为具有异构机动性水平的飞行物体网络开发预测通信的新工具。其关键思想是在预期的网络分区和/或故障之前通过通信协议采取预防措施。所提出的方法依赖于开发一个通用的模型来预测周围的网络节点的运动轨迹。为了实现运动分析，每个对象的驱动力被建模为具有在相同类型的对象之间共享的隐藏层的分层生成模型。使用一种新的合并和分裂的方法提供了灵活性，以适应新的对象类与看不见的机动性水平和废除入侵的对象。基于范畴论中循环幺半群的新方法将被用来设计网络拓扑预测的最优测量模式。最后，将预测网络拓扑与决策相结合，提出了一种预测路由算法，并基于随机矩阵集中不等式，给出了路由最优性与预测不确定性之间的权衡关系，为实际应用奠定了基础.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标准进行评估，被认为值得支持。

项目成果

Optimized Compression Policy for Flying Ad hoc Networks

• DOI: 10.1109/ccnc.2019.8651761
• 发表时间: 2019
• 期刊: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)
• 作者: Arnau Rovira-Sugranes;F. Afghah;Abolfazl Razi

Fire Frontline Monitoring by Enabling UAV-Based Virtual Reality with Adaptive Imaging Rate

• DOI: 10.1109/ieeeconf44664.2019.9049048
• 发表时间: 2019-11
• 期刊: 2019 53rd Asilomar Conference on Signals, Systems, and Computers
• 作者: Shafkat Islam;Qiyuan Huang;F. Afghah;P. Fulé;Abolfazl Razi

Predictive routing for wireless networks: Robotics-based test and evaluation platform

无线网络的预测路由：基于机器人的测试和评估平台

• DOI: 10.1109/ccwc.2018.8301751
• 发表时间: 2018
• 期刊: IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC
• 作者: Razi, Abolfazl;Wang, Chaoju;Almaraghi, Fahad;Huang, Qiyuan;Zhang, Yuting;Lu, Hanxiao;Rovira-Sugranes, Arnau
• 通讯作者: Rovira-Sugranes, Arnau

A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

• DOI: 10.1109/ciss.2019.8693023
• 发表时间: 2019-03
• 期刊: 2019 53rd Annual Conference on Information Sciences and Systems (CISS)
• 作者: Shafkat Islam;Abolfazl Razi

Wildfire Spread Modeling with Aerial Image Processing

• DOI: 10.1109/wowmom49955.2020.00063
• 发表时间: 2020-08
• 期刊: 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)
• 作者: Qiyuan Huang;Abolfazl Razi;F. Afghah;P. Fulé