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IEEE 96th Vehicular Technology Conference (VTC-Fall)

作者： Zhang, Hengxi Tang, Huaze Hu, Yuanquan Wei, Xiaoli Wu, Chenye Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781665454681

The utilization of heterogeneous end devices such as the low earth orbit (LEO) satellite, unmanned aerial vehicles (UAVs) and ground users (GUs) deployed at different altitudes, known as the space-air-ground integrated network (SAGI-Net), can be quite promising towards a bunch of advanced applications. Whereas, the energy efficiency of the SAGI-Net communication system is a key criterion needed to be improved urgently in consideration that the inappropriate communication routing will undoubtedly cause a huge communication energy cost of the system especially with a large number of communication devices inside. In this paper, we proposed a novel communication routing selection model for the SAGI-Net system and established a heterogeneous multi-agent reinforcement learning (HMF-MARL) framework to optimize the communication energy efficiency of this system, where the mean-field theory was introduced to enhance the ability of classic MARL method while still maintaining a relatively low computational complexity. The experiment results show that the capacity of the heterogeneous multi-agent system has been improved by nearly 80% using the proposed HMF-MARL method compared with the classic MARL one, which hopefully shows the potential value on the implementation of the SAGI-Net system in the future.

关键词： SAGI-Net heterogeneous mean field MARL communication routing selection computational complexity

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FireHunter: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling

FireHunter: Toward Proactive and Adaptive Wildfire Suppressi...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Chen, Xuecheng Xiao, Zijian Cheng, Yuhan Hsia, Chen-Chun Wang, Haoyang Dang, Fan Xu, Jingao Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Ubiquitous Data Enabling Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China RISC V Int Open Source Lab Beijing Peoples R China Pengcheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350384482;9798350384475

Multi-robot systems are adept at handling complex tasks in large-scale, dynamic, and cold-start scenarios such as wildfire control. This paper introduces FireHunter to tackle the challenge of coordinating fire monitoring and suppression tasks simultaneously in unpredictable environments. FireHunter utilizes a confidence-aware assessment method to identify optimal locations and a priority graph-based algorithm to coordinate robots efficiently. It effectively manages the dynamic planning inclinations for sensing and operational tasks, ensuring real-time information collection and timely environmental intervention. Experimental results from simulation show that FireHunter reduces fire expansion ratio by 59% compared to state-of-the-art solutions.

关键词： Multi-robot System Collaborative Sensing

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QUEST: Quality-informed Multi-agent Dispatching System for Optimal Mobile Crowdsensing

QUEST: Quality-informed Multi-agent Dispatching System for O...

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IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Zhang, Xiao-Ping Chen, Xinlei Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Beijing Peoples R China Tsinghua Univ Global Innovat Exchange Beijing Peoples R China Johns Hopkins Univ Dept Civil & Syst Engn Baltimore MD USA Pengcheng Lablatory Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

ISBN: (纸本)9798350383515;9798350383508

We address the challenges in achieving optimal Quality of Information (QoI) for non-dedicated vehicular Mobile Crowdsensing (MCS) systems, by utilizing vehicles not originally designed for sensing purposes to provide real-time data while moving around the city. These challenges include the coupled sensing coverage and sensing reliability, as well as the uncertainty and time-varying vehicle status. To tackle these issues, we propose QUEST, a QUality-informed multi-agEnt diSpaTching system, that ensures high sensing coverage and sensing reliability in non-dedicated vehicular MCS. QUEST optimizes QoI by introducing a novel metric called ASQ (aggregated sensing quality), which considers both sensing coverage and sensing reliability jointly. Additionally, we design a mutual-aided truth discovery dispatching method to estimate sensing reliability and improve ASQ under uncertain vehicle statuses. Real-world data from our deployed MCS system in a metropolis is used for evaluation, demonstrating that QUEST achieves up to 26% higher ASQ improvement, leading to a reduction of reconstruction map errors by 32-65% for different reconstruction algorithms.

关键词： internet of Things Mobile sensing and applications Mobile Crowdsensing Data Quality

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SOScheduler: Toward Proactive and Adaptive Wildfire Suppression via Multi-UAV Collaborative Scheduling

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IEEE intERNET OF THINGS JOURNAL 2024年第14期11卷 24858-24871页

作者： Chen, Xuecheng Xiao, Zijian Cheng, Yuhan Hsia, Chen-Chun Wang, Haoyang Xu, Jingao Xu, Susu Dang, Fan Zhang, Xiao-Ping Liu, Yunhao Chen, Xinlei Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen 518000 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518000 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Johns Hopkins Univ Civil & Syst Engn Baltimore MD 21218 USA Tsinghua Univ Global Innovat Exchange Beijing 100084 Peoples R China Int Open Source Lab RISC V Shenzhen 518000 Guangdong Peoples R China Pengcheng Lab Shenzhen 518066 Guangdong Peoples R China

Multiunmanned aerial vehicle (UAV) systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAV. To solve this problem, this article proposes SOScheduler, a collaborative multi-UAV scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatiotemporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAV in an efficient manner. Experiments on real multi-UAV testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.

关键词： Wildfires Monitoring Dynamic scheduling Task analysis Sensors Autonomous aerial vehicles Wireless sensor networks multirobot systems robot sensing systems

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Theoretical modeling of contact-separation mode triboelectric nanogenerators from initial charge distribution

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ENERGY & ENVIRONMENTAL SCIENCE 2024年第6期17卷 2228-2247页

作者： Zhao, Hongfa Wang, Hao Yu, Hongyong Xu, Qinghao Li, Xiaosa Guo, Jing Shao, Jiajia Wang, Zhong Lin Xu, Minyi Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 100085 Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Triboelectric nanogenerators (TENGs) have demonstrated remarkable capabilities in energy harvesting and self-powered sensing, while TENG theories have set the foundation for their further development. This study presents a systematic theoretical analysis for contact-separation mode TENGs, including theoretical models, analysis methods and validation approaches. Specifically, three infinite plate models are proposed to cover typical forms of contact-separation mode TENGs. According to the physics of charge conservation and electrostatic equilibrium, the distribution of initial charges on tribo-charge layers, and redistribution of all charges on the TENG are determined, based on which the electric field, charge transfer process, conduction current in the external circuit and displacement current inside TENGs are mathematically deduced. TENGs' characteristics and figure of merit can be directly obtained from the proposed theory, which are consistent with phenomena in practice. This validates the theoretical derivations, and better explains TENG performances in practice. Furthermore, applicability is discussed by comparing the finite plate models and infinite plate models, and more accurate schematic diagrams depicting the working principle of TENGs are created. Additionally, an electrodynamic model with a solving approach is proposed that can be applied for TENGs of any size. Extended analysis for different applications is performed, demonstrating the practicability of the proposed theory for TENGs. This work may provide a solid and practical theoretical foundation for TENG development. A systematic theoretical analysis for contact-separation mode TENGs is presented, including theoretical models, analysis methods, validation approaches, and extended analysis for applications.

关键词： Nanogenerators

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STEV: Stretchable Triboelectric E-skin enabled Proprioceptive Vibration Sensing for Soft Robot

STEV: Stretchable Triboelectric E-skin enabled Proprioceptiv...

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IEEE international Conference on Robotics and Automation (ICRA)

作者： Wang, Zihan Lei, Kai-Chong Tang, Huaze Li, Shoujie Dai, Yuan Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Shenzhen Int Grad Uate Sch Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tencent Robot X Lab Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

ISBN: (纸本)9798350323658

Vibration perception is essential for robotic sensing and dynamic control. Nevertheless, due to the rigorous demand for sensor conformability and stretchability, enabling soft robots with proprioceptive vibration sensing remains challenging. This paper proposes a novel liquid metal-based stretchable e-skin via a kirigami-inspired design to enable soft robot proprioceptive vibration sensing. The e-skin is fabricated into 0.1mm ultrathin thickness, ensuring its negligible influence on the overall stiffness of the soft robot. Moreover, the working mechanism of the e-skin is based on the ubiquitous triboelectrification effect, which transduces mechanical stimuli without external power supply. To demonstrate the practicability of the e-skin, we built a soft gripper consisting of three soft robotic fingers with proprioceptive vibration sensing. Our experiment shows that the gripper can accurately distinguish the grain category (six grains with the same mass, 99.9% accuracy) and the packaging quality (100% accuracy) by simply shaking the gripped bottle. In summary, a soft robotic proprioceptive vibration sensing solution is proposed;it helps soft robots to have a more comprehensive awareness of their self-state and may inspire further research on soft robotics.

关键词： Bottles

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Towards efficient communications in federated learning: A contemporary survey

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JOURNAL OF THE FRANKLIN INSTITUTE 2023年第12期360卷 8669-8703页

作者： Zhao, Zihao Mao, Yuzhu Liu, Yang Song, Linqi Ouyang, Ye Chen, Xinlei Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen Guangdong Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Guangdong Peoples R China AsiaInfo Technol Beijing Peoples R China RISC V Int Open Source Lab Shenzhen Guangdong Peoples R China

In the traditional distributed machine learning scenario, the user's private data is transmitted between clients and a central server, which results in significant potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a particular distributed machine learning procedure with privacy protection mechanisms, which can achieve multi-party collaborative computing without revealing the original data. However, in practice, FL faces a variety of challenging communication problems. This review seeks to elucidate the relationship between these communication issues by methodically assessing the development of FL communication research from three perspectives: communication efficiency , communication environment , and communication resource allocation . Firstly, we sort out the current challenges existing in the communications of FL. Second, we have collated FL communications-related papers and described the overall development trend of the field based on their logical relationship. Ultimately, we discuss the future directions of research for communications in FL.& COPY;2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

关键词： Data privacy

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Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS

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HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS 2024年第1期11卷 1-15页

作者： Zhao, Baining Wang, Xuzhe Zhang, Tianyu Shi, Rongye Xu, Fengli Man, Fanhang Chen, Erbing Li, Yang Li, Yong Sun, Tao Chen, Xinlei Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Shenzhen Univ Dept Math Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China

Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

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WSTac: interactive Surface Perception based on Whisker-Inspired and Self-Illuminated Vision-Based Tactile Sensor

WSTac: Interactive Surface Perception based on Whisker-Inspi...

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ACM international Joint Conference on Pervasive and Ubiquitous Computing / ACM international Symposium on Wearable Computing (UbiComp/ISWC)

作者： Lei, Kai Chong Sou, Kit Wa Chan, Wang Sing Yan, Jiayi Ping, Siqi Peng, Dengfeng Ding, Wenbo Zhang, Xiao-Ping Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China Shenzhen Univ Key Lab Optoelect Devices & Syst Minist Educ & Guangdong Prov Coll Phys & Optoelect Engn Shenzhen Peoples R China RISC V Int Open Source Lab Shenzhen Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9798400702006

Modern Visual-Based Tactile Sensors (VBTSs) use cost-effective cameras to track elastomer deformation, but struggle with ambient light interference. Solutions typically involve using internal LEDs and blocking external light, thus adding complexity. Creating a VBTS resistant to ambient light with just a camera and an elastomer remains a challenge. In this work, we introduce WStac, a self-illuminating VBTS comprising a mechanoluminescence (ML) whisker elastomer, camera, and 3D printed parts. The ML whisker elastomer, inspired by the touch sensitivity of vibrissae, offers both light isolation and high ML intensity under stress, thereby removing the necessity for additional LED modules. With the incorporation of machine learning, the sensor effectively utilizes the dynamic contact variations of 25 whiskers to successfully perform tasks like speed regression, directional identification, and texture classification. Videos are available at: https://***/view/wstac/.

关键词： Vision-Based Tactile Sensor Mechanoluminescence Sensor Data Processing interactive Perception

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AQUILA: Communication Efficient Federated Learning With Adaptive Quantization in Device Selection Strategy

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第6期23卷 7363-7376页

作者： Zhao, Zihao Mao, Yuzhu Shi, Zhenpeng Liu, Yang Lan, Tian Ding, Wenbo Zhang, Xiao-Ping Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing 100190 Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing 100190 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 201210 Peoples R China George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA RISC V Int Open Source Lab Shenzhen 518055 Guangdong Peoples R China Shanghai Artificial Intelligence Lab Shanghai 201210 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typically arising from the transmission of large-scale models. Existing adaptive quantization methods, designed to mitigate these overheads, operate under the impractical assumption of uniform device participation. Additionally, these methods are limited in their adaptability due to the necessity of manual quantization level selection and often overlook biases inherent in local devices' data, thereby affecting the robustness of the global model. In response, this paper introduces AQUILA (adaptive quantization in device selection strategy), a novel adaptive framework devised to effectively handle these issues, enhancing the efficiency and robustness of FL. AQUILA integrates a sophisticated device selection method that prioritizes the quality and usefulness of device updates. Utilizing the exact global model stored by devices enables a more precise device selection criterion, reduces model deviation, and limits the need for hyperparameter adjustments. Furthermore, AQUILA presents an innovative quantization criterion, optimized to improve communication efficiency while assuring model convergence. Our experiments demonstrate that AQUILA significantly decreases communication costs compared to existing methods, while maintaining comparable model performance across diverse non-homogeneous FL settings, such as Non-IID data and heterogeneous model architectures.

关键词： Convergence Communication efficiency federated learning optimization

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