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Recent progresses and challenges in smart contact lens

With advances in the Internet of things, contact lenses not only are limited to vision correction, but also have become an emerging field of the smart wearable device.And with the proposal of the concept of “meta universe”, augmented reality (AR) technology has once again become the mainstream development direction of science and technology.The latest research progress of key technologies of smart contact lenses in physiological signal monitoring, intraocular pressure monitoring, eye-movement tracking, and augmented reality, as well as the energy supply scheme of the contact lenses were summarized comprehensively.And the future development direction of smart contact lenses in intelligent health and augmented reality was looked forward.In the future, smart contact lenses will realize or surpass the functions of smartphone.Their displays can be controlled by eye movement, which enables us to browse various information, such as news, weather, biomarker level, etc.Therefore, the smart contact lens will play an important role in intelligent medical health and meta universe.

> Topic: IoT Terminals and Chips

Jiandong XU, Ruisong LI, Hao CHANG, Yi YANG, Sheng ZHANG, Tianling REN

Recent progresses and challenges in smart contact lens
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Volume 10 期 2，2026 2026年第10卷第2期

  • Topic\: Integrated Sensing\, Communication and Computing in 6G Cross\-Domain IoT

    Wu Jiaze, Ma Yinglve, Yang Yiheng, Zhao Tingyun, Lin Chenxuan, Zhang Suwan, Wu Xiping, Wang Chengxiang

    DOI：10.11959/j.issn.2096-3750.WLW25160
    摘要：As the 6G era approaches, the number of Internet of things (IoT) terminal devices has rapidly increased, leading to a significant increase in battery consumption and maintenance costs. As a result, achieving net-zero energy consumption has become one of the key technical requirements for promoting the widespread adoption of IoT terminals. Visible light communication (VLC), as an emerging green communication technology in recent years, can effectively utilize existing lighting sources to carry downlink information, while terminal devices can use solar panels to simultaneously acquire both information and energy. However, traditional VLC uplinks typically rely on infrared communication technology, which imposes higher energy demands on terminal devices, making it difficult to achieve the goal of net-zero energy consumption. Accordingly, a novel net-zero energy IoT technology solution was proposed, which integrated solar-panel-based visible light communication with retroreflector-based passive visible light communication, and a prototype was constructed. This solution aimed to achieve net-zero energy consumption for IoT terminal devices by enhancing energy harvesting efficiency and reducing wireless transmission energy consumption. Experimental results showed that the prototype could achieve a transmission distance of over 50 cm under indoor natural lighting conditions, with downlink and uplink transmission rates reaching 4 300 bit/s and 50 bit/s, respectively, making it suitable for short-range, low-rate IoT application scenarios such as smart locks. Furthermore, due to the adoption of passive uplink transmission, the required solar panel area for achieving net-zero energy consumption in terminal devices was reduced from 615 mm² to 125 mm², significantly enhancing the deployment flexibility of the terminal devices.  
    关键词：IoT;NZE;VLC;VLBC;solar energy harvesting   
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    Zhang Zhongqiang, Qiu Qi, Miao Yajie, Li Bin, Wang Ye, Shi Guangming

    DOI：10.11959/j.issn.2096-3750.WLW25161
    摘要：Unmanned aerial vehicle (UAV) is recognized as the core component of the low-altitude economy network. It is crucial to achieve efficient image transmission in air-to-ground communication. During mission execution, rapid channel fluctuations are induced by changes in the flight area, including variations in signal-to-noise ratio (SNR) and transmission rate. To adapt to these changes, a channel elastic encoding and decoding method for UAV image transmission was proposed. This method included a lightweight feature extraction module, a channel feature enhancement module, a transmission rate adaptive module, and symmetric decoding modules. The lightweight feature extraction module contained residual blocks and the mobile Mamba, which could quickly extract local detail features and global context. It could effectively obtain rich semantic features from aerial images. The feature enhancement module enhanced key semantic features according to the channel state SNR. The transmission rate adaptive module adjusted the size of transmitted features based on the channel transmission rate. Extensive experiments on the aerial image dataset (AID) demonstrate that the proposed method achieves higher transmission accuracy than the state-of-the-art methods.  
    关键词：UAV aerial image transmission;SNR adaptation;rate adaptation;semantic communication   
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    更新时间：2026-10-10

    Han Zilong, Li Yafei, Wang Yuqi, Zhang Yuehao, Zheng Liming

    DOI：10.11959/j.issn.2096-3750.WLW25189
    摘要：Wireless radio frequency backscatter is characterized by shared spectrum, low power consumption, and low cost, and it has important application value in the terminal devices and repeater markets of communication and sensing. Mobile communication technology has always been a hotspot of development. The transition from 5G to 6G is not only about speed but also about evolving from the Internet of things to the Internet of intelligent things. Both mobile communication technology and Internet of things technology face the problem of spectrum scarcity, there is also demand for low-power energy-saving design, creating intersections with wireless radio frequency backscatter communication. Firstly, by exploring research hotspots such as mobile communication and Internet of things technologies, the wireless radio frequency backscatter communication was introduced in this study. Secondly, through reviewing seminal papers on the environmental backscatter communication, recent research, and fundamental principles, a clear developmental trajectory was mapped out and the current state was categorized across different dimensions. Then, based on this developmental trajectory and classification, existing issues and partial solutions were identified. Finally, combining the analysis of the full text, the next phase of development trends were predicted in this study.  
    关键词：backscatter;wireless radio frequency;review;mobile communication technology;internet of things   
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    更新时间：2026-10-10

    Xu Changchang, Zhang Qi, Zhao Jie, Zhang Jun

    DOI：10.11959/j.issn.2096-3750.WLW25095
    摘要：Cell-free massive multiple-input multiple-output (CF-mMIMO) systems, which eliminate inter-cell interference and enhance spectral efficiency, are considered as a key enabling technology for 6th generation mobile communications (6G). Meanwhile, reconfigurable intelligent surface (RIS), featuring low power consumption, programmability, and flexible deployment, offers significant potential for improving channel quality and extending coverage. In this paper, a RIS-assisted CF-mMIMO system was investigated, and a joint optimization problem involving power control, precoding, and RIS phase adjustment was formulated to maximize energy efficiency (EE). To tackle this non-convex multi-variable problem, an alternating optimization framework was proposed. Specifically, the precoding and RIS phase subproblems were decoupled and solved using the Lagrangian dual transform and fractional programming, while the power control subproblem was addressed via Newton method. Simulation results demonstrate that the proposed scheme achieves faster convergence and significantly outperforms the conventional uniform power allocation methods. Moreover, it simultaneously improves both energy efficiency and communication rate, effectively enabling the joint optimization of spectrum efficiency (SE) and EE.  
    关键词：CF-mMIMO;RIS;power control optimization;EE   
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    更新时间：2026-10-10

  • Topic\: Embodied Intelligence and Robotic IoT

    Rao Chong, Jiang Xikang, Guo Jiahang, Li Lei, Zhang Lin

    DOI：10.11959/j.issn.2096-3750.WLW25112
    摘要：Ultra-wideband (UWB) radar enables people counting and vital signs monitoring with wireless sensing. However, existing methods rely on statistical features or lightweight neural networks, which have limited ability to migrate and generalize across multiple tasks. In view of the powerful cross-modal learning capability demonstrated by large language model (LLM), a UWB radar multi-task learning framework based on LLM was proposed. This approach successfully mapped the temporal features of radar signals into the embedding space of LLM, and by integrating the mixture of experts (MoE) mechanism and low-rank adaptation (LoRA) strategy, UWB-LLM performed parameter-efficient fine-tuning (PEFT) for multi-task learning. Experiments were conducted on both self-collected and publicly available datasets for people counting task as well as respiration, electrocardiogram (ECG), and continuous blood pressure estimation tasks, respectively. Compared with the state-of-the-art algorithms, UWB-LLM achieves average accuracy improvements of 37.62% for people counting and 7.47%, 18.16%, and 14.70% for the three vital sign estimation tasks.  
    关键词：LLM;UWB radar;people counting;vital signs monitoring;multi-task learning;MoE   
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    更新时间：2026-10-10
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Quarterly,started in 2017
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