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integrated sensing, lighting and communication based on visible light communication: A review

DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Liang, Chenxin Li, Jiarong Liu, Sicong Yang, Fang Dong, Yuhan Song, Jian Zhang, Xiao-Ping Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Xiamen Univ Sch Informat Dept Informat & Commun Engn Xiamen 361005 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Tsinghua Univ Dept Elect Engn Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Ryerson Univ Dept Elect & Comp Engn Toronto ON M5B2K3 Canada

As wireless communication rapidly evolves and the demand for intelligent connectivity grows, the need for precise sensing integrated with efficient communication becomes paramount. While traditional integrated Sensing and Communication (ISAC) methods have laid foundational groundwork, they grapple with environmental limitations and significant propagation losses. Visible Light Communication (VLC) emerges as a trans formative solution characterized by its high-speed transmission, minimal latency, cost-efficiency, and seamless installation. This paper introduces the Lighting, Sensing, and Communication (LiSAC) concept for VLC and systematically reviews the technical aspects, such as channel characteristics, modulation techniques, and system design. Specifically, this paper presents the evolution of the LiSAC system, its integration with other communication technologies, its applications in various fields, and its challenges. At the end of this paper, we outlooked LiSAC in the future, in which high-quality communication will integrate pinpoint sensing accuracy.

关键词： Visible light communication Visible light sensing integrated sensing and communication

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SAFARI: Sparsity-Enabled Federated Learning With Limited and Unreliable Communications

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第5期23卷 4819-4831页

作者： Mao, Yuzhu Zhao, Zihao Yang, Meilin Liang, Le Liu, Yang Ding, Wenbo Lan, Tian Zhang, Xiao-Ping Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Beijing 100190 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Tsinghua Univ Inst AI Ind Res AIR Beijing 100190 Peoples R China Shanghai AI Lab Shanghai 200241 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China George Washington Univ Dept Elect & Comp Engn Washington DC 20052 USA Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

Federated learning (FL) enables edge devices to collaboratively learn a model in a distributed fashion. Many existing researches have focused on improving communication efficiency of high-dimensional models and addressing bias caused by local updates. However, most FL algorithms are either based on reliable communications or assuming fixed and known unreliability characteristics. In practice, networks could suffer from dynamic channel conditions and non-deterministic disruptions, with time-varying and unknown characteristics. To this end, in this paper we propose a sparsity-enabled FL framework with both improved communication efficiency and bias reduction, termed as SAFARI. It makes use of similarity among client models to rectify and compensate for bias that results from unreliable communications. More precisely, sparse learning is implemented on local clients to mitigate communication overhead, while to cope with unreliable communications, a similarity-based compensation method is proposed to provide surrogates for missing model updates. With respect to sparse models, we analyze SAFARI under bounded dissimilarity. It is demonstrated that SAFARI under unreliable communications is guaranteed to converge at the same rate as the standard FedAvg with perfect communications. Implementations and evaluations on the CIFAR-10 dataset validate the effectiveness of SAFARI by showing that it can achieve the same convergence speed and accuracy as FedAvg with perfect communications, with up to 60% of the model weights being pruned and a high percentage of client updates missing in each round of model updates.

关键词： Training Reliability Computational modeling Servers Convergence Data models Federated learning Distributed networks federated learning unreliable communication model sparsification

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When Vision Meets Touch: A Contemporary Review for Visuotactile Sensors From the Signal Processing Perspective

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2024年第3期18卷 267-287页

作者： Li, Shoujie Wang, Zihan Wu, Changsheng Li, Xiang Luo, Shan Fang, Bin Sun, Fuchun Zhang, Xiao-Ping Ding, Wenbo Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Natl Univ Singapore Dept Mat Sci & Engn Singapore 117575 Singapore Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Kings Coll London Ctr Robot Res London WC2R 2LS England Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Tactile sensors, which provide information about the physical properties of objects, are an essential component of robotic systems. The visuotactile sensing technology with the merits of high resolution and low cost has facilitated the development of robotics from environment exploration to dexterous operation. Over the years, several reviews on visuotactile sensors for robots have been presented, but few of them discussed the significance of signal processing methods to visuotactile sensors. Apart from ingenious hardware design, the full potential of the sensory system toward designated tasks can only be released with the appropriate signal processing methods. Therefore, this paper provides a comprehensive review of visuotactile sensors from the perspective of signal processing methods and outlooks possible future research directions for visuotactile sensors.

关键词： Visuotactile perception sensor design signal processing applications Visuotactile perception sensor design signal processing applications

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Mean-Field-Aided Multiagent Reinforcement Learning for Resource Allocation in Vehicular Networks

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IEEE intERNET OF THINGS JOURNAL 2023年第3期10卷 2667-2679页

作者： Zhang, Hengxi Lu, Chengyue Tang, Huaze Wei, Xiaoli Liang, Le Cheng, Ling Ding, Wenbo Han, Zhu Tsinghua Univ Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity O(n(N)) with n denoting the dimension of action space and N denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to O(n(2)) while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

关键词： Joint resource allocation mean-field game (MFG) theory multiagent reinforcement learning (MARL) vehicular networks

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StreamingTag: A Scalable Piracy Tracking Solution for Mobile Streaming Services

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14530-14543页

作者： Dang, Fan Jin, Xinqi Fu, Qi-An Li, Lingkun Peng, Guanyan Chen, Xinlei Liu, Kebin Liu, Yunhao Tsinghua Univ Global Innovat Exchange Beijing 100084 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Beijing Jiaotong Univ Sch Software Beijing 100044 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen 518057 Guangdong Peoples R China Pengcheng Lab Shenzhen 518066 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Fuzhou Fuyao Inst Adv Study Fuzhou 350109 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.

关键词： Streaming media Watermarking Fingerprint recognition Copyright protection Servers Mobile computing Accuracy video streaming piracy tracing dynamic time warping video watermarking collusion

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A triboelectric gait sensor system for human activity recognition and user identification

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NANO ENERGY 2023年 112卷

作者： Li, Jiarong Xie, Zixuan Wang, Zihan Lin, Zenan Lu, Chengyue Zhao, Zihao Jin, Yuchao Yin, Jihong Mu, Shilong Zhang, Chaobo Gui, Weihua Liang, Xiaojun Wang, Jiyu Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China Cent S Univ Sch Automat Changsha 410083 Peoples R China

Floor-based sensing systems to monitor human activities and identify users are essential for smart homes and intelligent buildings. A low-cost, easy-to-fabricate, and flexible gait sensor system based on triboelectric nanogenerator (TENG) is presented in this paper, which can transform gait movements, even in the low-frequency form, into electrical impulses without an external power source. To realize this, a TENG-based gait sensor unit with an optimized structure design is proposed to enhance the sensing sensitivity. A sensing insole module is formed by arranging the sensor units according to the foot pressure distribution. The sensor distribution is then explored and improved by comparative studies of gait recognition performance, which increases the recognition efficiency and the possible application in edge computing scenarios. Furthermore, a deep learning network is developed based on long short-term memory (LSTM) and residual units to extract deep features from multichannel time-series gait data to boost recognition performance. Experimental results demonstrate that the proposed gait sensor system can be utilized for human activity recognition and user identification with accuracies of 97.9 % and 99.4 %, respectively. Finally, a gait-sensing-based fitness exercise monitoring system is constructed that can estimate calorie expenditure and distinguish between standard and non-standard fitness activities with an accuracy of 97.2 %. This work can be extended to various application scenarios such as security surveillance, health monitoring, and intelligent control, which provides a new ubiquitous self-powered sensing solution for the internet of Things (IoT).

关键词： Triboelectric nanogenerator Self-powered sensor Gait recognition system Deep learning network Activity monitoring User identification

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Reprint of: Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications

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DIGITAL SIGNAL PROCESSING 2022年 125卷

作者： Wang, Zihan Li, Jiarong Jin, Yuchao Wang, Jiyu Yang, Fang Li, Gang Ni, Xiaoyue Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Beijing 518055 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Duke Univ Dept Mech Engn & Mat Sci Durham NC 27708 USA Tsinghua Univ RISC V Int Open Source Lab Beijing 518055 Peoples R China

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as "cross-sensing " and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Wearable technologies Ubiquitous sensing Human-machine interaction Health and safety Security

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Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications

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DIGITAL SIGNAL PROCESSING 2021年 116卷 103091-103091页

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as "cross-sensing" and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Wearable technologies Ubiquitous sensing Human-machine interaction Health and safety Security

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Triboelectric-nanogenerator-enabled mechanical modulation for infrared wireless communications

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ENERGY & ENVIRONMENTAL SCIENCE 2022年第7期15卷 2983-2991页

作者： Wang, Zihan Jin, Yuchao Lu, Chengyue Wang, Jiyu Song, Ziwu Yang, Xu Cao, Yidan Zi, Yunlong Wang, Zhong Lin Ding, Wenbo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Inst Mat Res Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shun Hing Inst Adv Engn Hong Kong Peoples R China Chinese Acad Sci Beijing Inst Nanoenergy & Nanosyst Beijing 101400 Peoples R China Univ Chinese Acad Sci Sch Nanosci & Technol Beijing 100049 Peoples R China CUSTech Inst Technol Wenzhou 325024 Zhejiang Peoples R China Georgia Inst Technol Sch Mat Sci & Engn Atlanta GA 30332 USA RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Optical wireless communications (OWC), featuring a wide optical bandwidth and robust resistance to radio-frequency interference (RFI), can benefit the development of the internet of Things (IoT). Making IoT devices ubiquitous calls for decentralized and on-demand power sources for wireless communications. However, existing visible-light-based self-powered OWC systems are complex on the receiver side, they are easily affected by ambient light, and they can disturb dark environments. In this paper, we realized self-powered infrared (IR) wireless communications via integrating a triboelectric nanogenerator (TENG) and an IR transmitter. With customized mechanical modulation protocols, both the frequency and amplitude features of the IR signal, enabled by different mechanical motion or mechanical structures of TENG devices, can be leveraged for conveying information. The concept of this TENG-enabled wireless infrared communications framework and the results from this study may pave the way for a new generation of self-powered communications in a sustainable manner.

关键词： Radio interference

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A Highly Sensitive Triboelectric Vibration Sensor for Machinery Condition Monitoring

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ADVANCED ENERGY MATERIALS 2022年第37期12卷 2201132-2201132页

作者： Zhao, Hongfa Shu, Mingrui Ai, Zihao Lou, Zirui Sou, Kit Wa Lu, Chengyue Jin, Yuchao Wang, Zihan Wang, Jiyu Wu, Changsheng Cao, Yidan Xu, Xiaomin Ding, Wenbo Tsinghua Univ Shenzhen Int Grad Sch Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Key Lab Marine IntelliSense & Computat Southern Marine Sci & Engn Guangdong Lab Guangzho Shenzhen 518055 Peoples R China Natl Univ Singapore Dept Mat Sci & Engn 9 Engn Dr 1 Singapore 117575 Singapore Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Inst Mat Res Shenzhen Geim Graphene Ctr Shenzhen 518055 Peoples R China RISC V Int Open Source Lab Shenzhen 518055 Peoples R China

Vibration sensors are involved extensively in a variety of applications. Especially in the era of the internet of Things, developing self-powered vibration sensors has become a very meaningful yet challenging problem. This study investigates a highly sensitive self-powered vibration sensor based on the triboelectric nanogenerator (VS-TENG) for machinery condition monitoring. By introducing a stacked structure comprising foamed aluminum, and a fluorinated ethylene propylene film with gold-plated electrode protected by two indium tin oxid layers, The VS-TENG can detect the vibrations with frequencies ranging from 1 to 2000 Hz and can detect vibrations of low amplitude (approximately sub-mu m, by calculation). The output performance and characteristics of the TENG under various vibration frequencies, accelerations, and amplitudes are analyzed systematically. The VS-TENG is successfully used to monitor the operating conditions of mechanical gear systems, reaching a recognition accuracy of 99.78%. Furthermore, it can also be utilized for vibration detection in other areas such as the air compressor, heat gun, hollow tile recognition, etc., which means a solid progress toward the practical applications of TENGs in self-powered vibration detection.

关键词： machinery condition monitoring self-powered sensors triboelectric nanogenerators vibration

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