Accurate CSI feedback is crucial for Massive MIMO systems, yet it remains challenging in resource-constrained IoT scenarios due to strict pilot overhead constraints. Under such extreme data sparsity, conventional data-driven methods often fail to generalize. To address this, this paper proposes a Physics-Aware Multi-Feature fusion approach (PAMF), a deep learning framework that systematically integrates data-driven learning with wireless propagation physics. The framework includes dedicated feature extractors based on spatial, frequency-domain, statistical, and physics-based methods, along with a deep residual reconstruction network. A key innovation lies in its dual-level physical constraint mechanism, which incorporates domain knowledge at both the feature and loss levels to ensure physically plausible channel estimates. By leveraging multi-modal feature representations and physics-aware optimization, PAMF effectively recovers the complete channel matrix from sparse pilot signals, which not only improves feature discrimination, but also leads to greater robustness particularly under the dynamic conditions typical of urban mobile networks. Experimental results demonstrate that the proposed method consistently outperforms existing approaches across diverse datasets including MIMO configurations of various scales, different modulation schemes, and real-world Wi-Fi CSI Specifically, on real-world Wi-Fi data, PAMF achieves an NMSE of 0.1002, approximately 62% lower than the ChannelNet baseline (0.2669). Overall, this study contributes a practical and physical-aware framework for channel estimation, paving the way for more reliability and efficiency next-generation wireless systems, with direct implications for large-scale IoT deployments.

Packet sampling is crucial for managing datacenter networks, serving fault diagnosis, traffic measurement, and intrusion detection functions. However, traditional sampling techniques, such as those based on sketches or ports, either lack packet–level granularity or provide insufficient visibility, leading to functional performance degradation. Recent research has employed the software-defined networking (SDN) model to enable flow-based packet sampling. However, these approaches often introduce substantial control and computation overhead, limiting their scalability. This paper presents Viper, a novel priority-based, high-visibility per-flow packet sampling mechanism tailored to address these challenges. Specifically, Viper leverages existing priority-based traffic scheduling mechanisms to prioritize shorter flows over longer ones. Then, a logical centralized controller orchestrates sampling policies for packets of different priorities. In-depth analysis indicates that the orchestration performed by the controller significantly impacts Viper’s performance. Consequently, we model this process as a nonlinear optimization problem, seeking to maximize the utility of sampling. Then, we propose an online primal–dual interior–point algorithm to address this optimization problem and prove the algorithm’s convergence, optimality, and efficiency. Experimental results show that Viper increases visibility by 3.83% to 8.3%, with negligible control overhead and a substantial reduction in sampling load by at least 20.51%.

The dynamic nature of tasks and environments in Internet of Things (IoT) systems require deep learning models to continuously retrain on evolving data to ensure their effectiveness. Existing continual learning (CL) methods aim to mitigate catastrophic forgetting, where the model loses knowledge of previous tasks when learning new ones. However, these methods often ignore the memory resource competition caused by the parallel execution of multiple applications, which limits the realworld IoT application of CL in resource-constrained edge devices. In this article, we propose EDCL, a novel approach that enhances the training efficiency and model accuracy of CL methods while ensuring the uninterrupted operation of high-priority inference programs. Specifically, we first implement a custom batch sampler that can dynamically load batches and measure the memory usage and training time recorded via offline profiling. In the online stage, by monitoring the resource consumption of high-priority programs, EDCL can dynamically select batch policies that meet resource constraints and facilitate efficient training. Additionally, we propose an adaptive hierarchical buffer swap method to enhance the model’s ability to retain previously learned knowledge and mitigate forgetting. Extensive experiments show that EDCL effectively balances training efficiency and model accuracy while preventing high-priority inference programs from failing due to memory contention, demonstrating promising performance compared to baselines.

Sign Language recognition is practically important in various scenarios such as smart home, medical rehabilitation, and intelligent industry. Compared with wireless sensing and computer vision methods, data glove-based methods have gained a plenty of attention, because they can perform well even in the environments with multi-path noise or visual occlusion. However, existing data glove-based methods usually require complex calibration and laborious dataset collection, and suffer from accumulated error. To address these challenges, we introduce a robust sign language recognition system with cross-domain generation, called SmartGlove, the first approach to achieve robust sign language recognition. To avoid complex calibration process, we propose a customized feature set that can enable user-insensitive and unintentional system calibration. To avoid the labor cost in training data collection, we propose a cross-domain data transformation technique to generate training data in target domain. To eliminate the accumulated error of sentence recognition, we utilize a context-based calibration method considering correlation among adjacent words. We implement SmartGlove with COTS devices, and extensive experiments reveal that SmartGlove achieves accuracy exceeding 97.11% for 30 sign language words, with an average recognition time of 47 milliseconds per word. Furthermore, the system recognizes 30 common sign language sentences with accuracy of 97.17%.

With the wide application of multi-modal fusion sensing in scenarios such as autonomous driving and human-computer interaction, the privacy security and communication burden caused by massive data uploading need to be solved urgently. Federated Learning (FL) has received significant attention as a privacy-preserving distributed machine learning paradigm. Recent Multi-Modal Federated Learning (MMFL) focuses on addressing modal heterogeneity to enhance accuracy and speed up convergence. However, it overlooks the huge communication overhead in updating complex multi-modal network models, especially in edge environments with limited bandwidth. At the same time, the state-of-the-art communication-efficient FL methods are not customized to the MMFL characteristics. In this paper, we propose the Adaptive Quantization framework for Multi-modal Federated Learning (AQMFL). AQMFL implements decision-level multi-modal fusion locally by using parallel training and model ensemble, supporting its adaptation to modal heterogeneity and flexible deployment. AQMFL can adaptively allocate the number of quantization levels of gradient according to the modal contribution and the heterogeneous communication ability of nodes, which speeds up the system convergence and achieves a better balance between accuracy and communication efficiency. Compared with the classical baselines, AQMFL can reduce the total communication overhead by up to 50.47% and the total training time by up to 52.11% while maintaining the accuracy.

A wide range of scenarios, such as warehousing, and smart manufacturing, have used RFID mobile robots for the localization of tagged objects. The state-of-the-art RFID-robot based localization works are based on the premise of stable speed. However, in reality this assumption can hardly be guaranteed because Commercial-Off-The-Shelf (COTS) robots typically have inconsistent moving speeds, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which accurately locates targets when the robot moving speed varies or is even unknown. We propose an optimized unwrapping method to maximize the use of data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of speed inconsistency. To increase the flexibility, we further optimize the system and propose SILoc$+$, which enables the system to achieve localization with part of the data, keeping speed inconsistency-immune. Extensive experiments demonstrate that SILoc and SILoc$+$can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

Mobile robot-assisted book inventory such as book identification and book order detection has become increasingly popular in smart library, replacing the manual book inventory which is time-consuming and error-prone. The existing systems are either computer vision (CV)-based or RFID-based, however several limitations are inevitable. CV-based systems may not be able to identify books effectively due to low accuracy of detecting texts on book spine. RFID tags attached to books can be used to identify a book uniquely. However, in high tag density scenarios such as library, tag coupling effects of adjacent tags may seriously affect the accuracy of tag reading. To overcome these limitations, this paper presents a novel RFID and CV fusion system for Book Inventory using mobile robot (RC-BI). RFID and CV are first used individually to obtain book order, then the information will be fused by the sequence based matching algorithm to remove ambiguity and improve overall accuracy. Specifically, we address three technical challenges. We design a deep neural network (DNN) model with multiple inputs and mixed data to filter out interference of RFID tags on other tiers, and propose a video information extracting schema to extract book spine information accurately, and use strong link to align and match RFID- and CV-based timestamp vs. book-name sequences to avoid errors during fusion. Extensive experiments indicate that our system achieves an average accuracy of 98.4% for tier filtering and an average accuracy of 98.9% for book order, significantly outperforming the state-of-the-arts.

With the wide deployment of RFID in various scenarios such as warehouse management, freight transportation, and manufacturing, tag authentication is increasingly important due to the threat of counterfeit tags. Recent physical-layer authentication approaches have demonstrated that the subtle differences in the hardware features offer a unique fingerprint to authenticate a tag. Although the state-of-the-art approaches are effective in laboratory environments, they are difficult for practical deployment because they either require complex analysis of the raw signal propagation or restrict the geometrical positions of the tags. In this paper, we propose an RFID tag authentication based on frequency- and orientation-related phase fingerprints, called FopPrint, which does not require raw signal analysis or complex geometric relationship. FopPrint uses the phase values of tags at different frequencies and orientations to construct feature matrices as physical-layer fingerprints and uses a pair of adjacent tags as identifiers of each object. FopPrint can effectively eliminate the influence of environmental factors by using the feature matrix constructed by the phase difference. We implement a prototype of FopPrint using Commercial-Off-The-Shelf (COTS) RFID devices. Extensive experimental results show that FopPrint achieves high authentication accuracy of 94% in various experimental settings.

Localization of target tagged objects on the shelf is of great significance in RFID-enabled warehousing scenarios. Compared with the RFID localization systems that use fixed reader antennas or mobile RFID-robot, the portable reader-based methods are much more cost-effective. Hence, this paper focuses on reader-portable RFID localization. However, the existing reader-portable localization systems suffer from the following limitations: (i) reader antenna is required to pass by the target tags. Thus, the tags in the corner can never be located; (ii) many reference tags need to be deployed on the shelf in advance, which considerably increases the manpower; (iii) specialized antenna is required, which limits the promotion potential. To this end, this paper proposes a Waving action-driven RFID Localization (WRL) system, which enables tag localization with a portable camera-augmented reader. In the WRL system, a user only needs to wave the camera-augmented reader before locating the target tags. Specifically, we first use a classical camera pose estimation method named PnP to recover the antenna’s movement trajectory in a pixel coordinate system. Then, WRL constructs a gridded hologram, in which camera data and RFID phase data are jointly used to calculate a probability for each grid. Intuitively, the higher probability a grid has, the more possible the target tag lies in the corresponding grid. Based on this idea, WRL calculates the target tag’s location on the shelf. We use the Commercial-Off-The-Shelf (COTS) RFID and camera devices to implement the WRL system. Extensive experiments have been conducted, and the results demonstrate that the mean localization error of WRL is less than 20cm with a confidence of about 95%.

Mobile RFID robots have been increasingly used in warehousing and intelligent manufacturing scenarios to pinpoint the locations of tagged objects. The accuracy of state-of-the-art RFID robot localization systems depends much on the stability of robot moving speed. However, in reality this assumption can hardly be guaranteed because a Commercial-Off-The-Shelf (COTS) robot typically has an inconsistent moving speed, and a small speed inconsistency will cause a large localization error. To this end, we propose a Speed Inconsistency-Immune approach to mobile RFID robot Localization (SILoc) system, which can accurately locate RFID tagged targets when the robot moving speed varies or is even unknown. SILoc employs multiple antennas fixed on the mobile robot to collect the phase data of target tags. We propose an optimized unwrapping method to maximize the use of the phase data, and a lightweight algorithm to calculate the locations in both 2D and 3D spaces based on the unwrapped phase profile. By utilizing the characteristics of tag-antenna distance and combining the phase data from multiple antennas, SILoc can effectively eliminate the side effects of moving speed inconsistency. Extensive experimental results demonstrate that SILoc can achieve a centimeter-level localization accuracy in the scenario with an inconsistent or unknown robot moving speed.

Recognition of human-object interactions is practically important in various human-centric sensing scenarios such as smart supermarket, factory, and home. This paper proposes an RF-Camera system by fusing RFID and Computer Vision (CV) techniques, which is the first work to recognize the human gestural interactions with physical objects in multi-subject and multi-object scenarios. In RF-Camera, we first propose a dimension reduction method to transform the subject's 3D hand trajectory captured by depth camera to a 2D image, using which the subject's gesture can be recognized. We also propose a method to extract the facial image of target subject from an image that may contain irrelevant subjects, thereby further recognizing his/her identity. Finally, we model the physical movements of the held object's tag and further predict the tag phase data, by comparing which with real phase data of each tag human-object matching can be discovered. When implementing RF-Camera, three technical challenges need to be addressed. (i) To remove noisy data corresponding to irrelevant actions from raw sensing data, we propose a state transition diagram to determine the boundary of effective data. (ii) To predict phase data of the held target tag with unknown hand-tag offset, we quantify target tag trajectory by adding a variable hand-tag vector to captured hand trajectory. (iii) To ensure high reading rates of target tags in tag-dense scenarios, we propose a CV-assisted RFID scheduling method, in which analytics on CV data can help schedule RFID readings. We conduct extensive experiments to evaluate the performance of RF-Camera. Experimental results demonstrate that RF-Camera can recognize the gestural actions, human identity and human-object matching with an average accuracy higher than 90% in most cases.

This paper studies the problem of tag localization using RFID-augmented robots, which is practically important for promising warehousing applications, e.g., automatic item fetching and misplacement detection. Existing RFID localization systems suffer from one or more of following limitations: requiring specialized devices; only 2D localization is enabled; having blind zone for mobile localization; low scalability. In this paper, we use Commercial Off-The-Shelf (COTS) robot and RFID devices to implement a Mobile RF-robot Localization (MRL) system. Specifically, when the RFID-augmented robot moves along the straight aisle in a warehouse, the reader keeps reading the target tag via two vertically deployed antennas ( Z1 and Z2) and returns the tag phase data with timestamps to the server. We take three points in the phase profile of antenna Z1 and leverage the spatial and temporal changes inherent in this phase triad to construct an equation set. By solving it, we achieve the location of target tag relative to the trajectory of antenna Z1. Based on different phase triads, we can have candidate locations of the target tag with different accuracy. Then, we propose theoretical analysis to quantify the deviation of each localization result. A fine-grained localization result can be achieved by assigning larger weights to the localization results with smaller deviations. Similarly, we can also calculate the relative location of target tag with respect to the trajectory of antenna Z2. Leveraging the geometric relationships among target tag and antenna trajectories, we eventually calculate the location of target tag in 3D space. We perform various experiments to evaluate the performance of the MRL system and results show that the proposed MRL system can achieve high accuracy in both 2D and 3D localization.

In recent years, there is an increasing demand for indoor localization services with the aim to locate people and objects inside buildings. However, localization accuracy is susceptible to inaccurate and high variant sensor measurements due to the unpredictable fluctuations of received wireless signals and the sensitivity of hardware devices. To address this issue, in this paper, we establish a new Bluetooth indoor localization system, whose architecture can be basically decomposed into two parts: the internet-of-things (IoT) framework and the localization module. Concretely, the IoT platform uses the state-of-the-art light weight Spring Boot microservice framework consisting of multi-layer structure. In the localization module, it follows the general process of trilateration but significantly distinguished from it. A set of measures are adopted to strengthen the system’s robustness when obtained measurements cannot be fully trusted. Specifically, in the first place, rather than using conventional propagation model to predict the distance between Bluetooth transmitter and receiver, we design a bran-new LSTM-based distance estimator which can better depict the nonlinearity of attenuation characteristics of radio signal. Moreover, we also employ a series of self-adaptive mechanisms, including elastic radius intersecting, multiple weighted centroid localization and self-adaptive Kalman tracking, to make the system robust against inaccurate measurements and unpredictable sudden variation of received wireless signal. A bunch of tests are conducted in both ideal lab environment and Alibaba’s large-scale warehouse, and experimental results show our indoor localization system outperforms the state-of-the-art benchmarks by a large margin in both localization accuracy and stability.