Publisher Copyright: © 2026 IEEE. EC/HE/101071179/EU//SUSTAIN EC/HE/101099491/EU//HOLDEN

Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing.$\bf{NearLink}$is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink's communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.

Weighted multidimensional scaling (WMDS), an algorithm extending multidimensional scaling (MDS), has been utilized in a broad spectrum of localization. However, there are still two unsolved theoretical questions: 1) The estimator provided by MDS depicts a relative placement of targets which require further Procrustes analysis to recover the actual placement, referred to as the absolute placement. This fact produces a question: Does the estimator of WMDS is still relative? If not, what underlying mechanisms assert the absoluteness? 2) It has been proved that WMDS attains the Cramér–Rao lower bound (CRLB) when the ranging distribution is Gaussian. Does it hold for a general distribution? If not, what restrictions on distributions are required? Motivated by such theoretical incompleteness, this article offers an in-depth theoretical analysis on WMDS in the scheme of range-based localization. With regard to question 1), we reveal the mechanisms that assert the estimator of WMDS always represents exactly the actual locations and prove that the absoluteness is introduced at the moment of variable separation and all the subsequent matrix equation transformations preserve the absoluteness. As for question 2), the functional behaviors and maximum condition of CRLB are examined under a general ranging distribution. Then, via the comparison to the variance of the WMDS estimator, the stationary condition on ranging distributions for WMDS to attain the CRLB is provided. Extensive simulations are performed to validate the theoretical conclusions numerically. And there exists conformity between numerical and theoretical results.

The device-free localization (DFL) has promising application prospects in intrusion detection, emergency rescue, and smart homes, because it does not require the target to carry any auxiliary positioning equipment. Radio tomographic imaging (RTI) is one of the most potential DFL techniques and has many advantages over other methods. However, in passive ultrahigh frequency radio frequency identification scenario, there are few researches and many problems to be solved. The difficult but urgent matter is how to identify the locations of multiple targets from many false targets and artifacts. This paper proposes a novel method based on cross-sectional scan (CSS), gray value distribution analysis (GVDA), and naive Bayes classifier to solve this problem. The CSS obtains the gray value distributions of the local maximum pixel in an RTI reconstructed image. Then, the GVDA extracts several characteristic parameters from gray value distributions, such as the size, height, and shape of the peak. Finally, the naive Bayes classifier utilizes these series of characteristics to judge whether local maximum pixels are false targets or real targets. The method can also recognize the number of targets that are very close to each other. Simulation and experimental results show that this method can accurately determine the locations and the number of targets.