CNS Core: Small: PilotPC: Proactive Inverse Learning of Network Topology for Predictive Communication among Unmanned Vehicles

CNS 核心：小型：PilotPC：用于无人驾驶车辆之间预测通信的网络拓扑主动逆向学习

• 批准号: 2008784
• 负责人: Abolfazl Razi
• 金额: $ 47.98万
• 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: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008784&HistoricalAwards=false
• 关键词: CNS; Core; Small; PilotPC; Proactive

The use of unmanned aerial vehicles (UAVs) has become commonplace in many applications nowadays. Like many other technologies, UAVs brings comfort and efficiency to the current practice in many applications while posing threats and safety issues. This project will advance the frontier of science in developing safer and more reliable aerial monitoring platforms while offering privacy and safety. Towards this goal, this project aims to make accurate predictions in a network of unmanned aerial vehicles, by using inverse learning techniques, so as to enable safer and more efficient operations.A smart observation system will be developed to constantly monitor the behavior of autonomous agents to identify suspicious activities by measuring their deviation from the most rational behavior under given conditions. The proposed research includes several steps: (i) to develop mechanisms to simulate the target node's perception of its surrounding environment (i.e., seeing the word from the target's eyes) using deep learning methods, (ii) to predict its rational behavior(s) under given conditions and available resources by modeling the target's decision making procedure through inverse learning (reading the target's brain), and (iii) to measure the level of trust by detecting rare but critical episodes of deviating from its expected rational behavior. In particular, the mobility of UAVs and their networking activities will be investigated as two important scenarios of malicious activities. This project is expected to result in developing new tools and algorithms for the action analysis of smart nodes that will be shared with the research community.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，无人驾驶飞行器（UAV）的使用在许多应用中已经变得司空见惯。与许多其他技术一样，无人机在许多应用中为当前的实践带来了舒适和效率，同时也带来了威胁和安全问题。该项目将推进科学前沿，开发更安全、更可靠的空中监测平台，同时提供隐私和安全。为了实现这一目标，本项目将利用逆向学习技术在无人机网络中进行准确的预测，从而实现更安全、更高效的操作。将开发一个智能观测系统，以持续监视自主代理的行为，通过测量它们在给定条件下与最合理行为的偏差来识别可疑活动。所提出的研究包括几个步骤：（i）开发模拟目标节点对其周围环境的感知的机制（即，从目标的眼睛看到单词），（ii）通过逆向学习（阅读目标的大脑）对目标的决策过程进行建模来预测其在给定条件和可用资源下的理性行为，以及（iii）通过检测偏离其预期理性行为的罕见但关键的事件来测量信任水平。特别是，无人机的移动性和他们的网络活动将被调查的恶意活动的两个重要场景。该项目预计将导致开发新的工具和算法，智能节点的行动分析，将与研究界分享。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Towards Boosting Channel Attention for Real Image Denoising: Sub-band Pyramid Attention

• DOI: 10.1007/978-3-030-87361-5_25
• 发表时间: 2021
• 作者: Huayu Li;Haiyu Wu;Xiwen Chen;Hao Wang;A. Razi

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

• DOI: 10.1109/tnse.2021.3085514
• 发表时间: 2021-07-01
• 期刊: IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
• 影响因子: 6.6
• 作者: Rovira-Sugranes, Arnau;Afghah, Fatemeh;Razi, Abolfazl
• 通讯作者: Razi, Abolfazl

Boosting Belief Propagation for LDPC Codes with Deep Convolutional Neural Network Predictors

使用深度卷积神经网络预测器增强 LDPC 码的置信传播

• 发表时间: 2021
• 期刊: IEEE Consumer Communications and Networking Conference
• 作者: Chen, Xiwen;Li, Huayu;Qu, Junsuo;Razi, Abolfazl
• 通讯作者: Razi, Abolfazl