Device-free Wi-Fi localization is a promising technology to localize users who do not carry smart devices. The basic idea is to separate and analyze the signals reflected off human body from the multi-path signals. However, previous works could only localize moving users, because they can not distinguish the signals reflected off static users or objects like walls and furniture. This paper presents the Respiration Localization system,RespLoc, which for the first time enables device-free Wi-Fi localization system for static users. To recognize static users, the key insight is that people can breathe but objects cannot. However, it is non-trivial to extract user’s location from the respiration signal, because the respiration signal is significantly weaker than the regular motion signal. To this end, we propose the equivalent analysis method. Instead of using traditional signal separation, which suffers from severe noise due to residual signal components, we propose to construct an equivalent signal with the following properties: First, the equivalent signal follows the same variation law as the respiration signal; Second, the equivalent signal is not affected by irrelevant static signals. Based on this equivalent signal, we are able to resolve location features from the multi-path signals directly without separating them. We implementRespLocon commodity Wi-Fi devices, and extensive experimental results demonstrate thatRespLoccan localize static users with a median error of 0.89 meters.

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.

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.

Millimeter-wave (mmWave) 5G-V2X communications face significant challenges in real-time beam alignment within high-mobility vehicular networks. While environmentaware beam prediction methods mitigate channel estimation overhead, their efficacy is severely compromised by modality data loss stemming from lighting variations, adverse weather, or sensor failures. To address this issue, we propose a Cross-modal Losstolerant Beam Prediction model (CLBP). CLBP robustly fuses RGB camera and LiDAR data, employing a novel cross-modal attention mechanism to achieve resilient feature alignment across these heterogeneous modalities. Furthermore, a Branch Features Dynamic Fusion (BFDF) module adaptively reweights modality features, suppressing noise from degraded inputs and promoting effective information propagation to enhance resilience. To facilitate realistic evaluation, we introduce a Data-Conditioned Missingness Mechanism (DCMM), which augments the DeepSense 6G V2V dataset with meticulously simulated sensor failure scenarios. Experimental results demonstrate CLBP's superior performance, achieving 94.48% Top-5 beam prediction accuracy even under 10% modality loss, and a 29% reduction in average power loss compared to baseline methods. These findings demonstrate CLBP's significant robustness in dynamic vehicular environments and its capacity to maintain consistent, high-performance beam prediction despite challenging data imperfections.

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%.

The Wi-Fi sensing technique holds great promise for future smart homes, thanks to the widespread use of Wi-Fi devices. With this technique, we can deduce the behavior of the target based on the channel state information (CSI), which is obtained during Wi-Fi communication. However, existing Wi-Fi sensing technologies are not compatible with standard communication technologies. This is because Wi-Fi sensing usually relies on capturing CSI from high-frequency communication packets, whereas regular IoT communication does not consistently maintain such high communication rates. To achieve precise sensing even with a low packet rate, we introduce LowDetrack, an indoor human detection and tracking system at ultra-low packet rates with Wi-Fi. In particular, we utilize compressed sensing to supplement missing data compared to existing systems that rely on linear interpolation or neural networks. To detect and track the target, our insights are twofold: 1) We combine compressed sensing and Fresnel zone to a theoretical model for accurately obtaining the reflection path change rate, which can be converted into the actual velocity of the target; 2) We investigate the mapping relationship between the dynamic frequency composition ratios in different links, which can provide navigation for velocity direction and correct direction recognition errors. We implement LowDetrack on commercial off-the-shelf Wi-Fi and realize human detection and tracking, where the median tracking error is 0.76m at the packet rate of 25 Hz.

Given privacy and network load concerns, employing on-device multimodal neural networks (MNNs) for IoT data is a growing trend. However, the high computational demands of MNNs clash with limited on-device resources. MNNs involve input and model redundancies during inference, wasting resources to process redundant input components and run excess model parameters. Model Redundancy Elimination (MRE) reduces redundant parameters but cannot bypass inference for unnecessary input components. Input Redundancy Elimination (IRE) skips inference for redundant input components but cannot reduce computation for the remaining parts. MRE and IRE independently fail to meet the diverse computational needs of multimodal inference. To address these issues, we aim to combine the advantages of MRE and IRE to achieve a more efficient inference. We propose anadaptivemultilevelredundancyelimination framework (AMRE), which supports both IRE and MRE.AMREfirst establishes a collaborative inference mechanism for IRE and MRE. We then propose a multifunctional, lightweight policy model that adaptively controls the inference logic for each instance. Moreover, a three-stage training method is proposed to ensure the performance of collaborative inference inAMRE. We validateAMREin three scenarios, achieving up to 52.91% lower latency, 56.79% lower energy cost, and a slight accuracy gain compared to state-of-the-art baselines.

Liquid sensing in ubiquitous contexts plays an essential role in various scenarios. Recently, some wireless sensing systems have been proposed for liquid identification. However, existing works usually require specific equipment or capture the signals penetrating a target, limiting the deployability of liquid sensing. In large-scale scenarios, multiple devices are usually required to expand the coverage area due to the RFID reader antenna's reading range limitation. To enlarge the sensing range and make the liquid sensing method can be adopted in real moving scenarios, in this paper, we presentMobileLiquidIDentification (MLiquID), a liquid sensing system that can recognize the type of liquid in a mobile manner with commercial off-the-shelf (COTS) RFID devices. This mobile process leads to continuous variation in location, so the major challenge in this paper is how to extract signal features from the superimposed information of movement and material. The key insight is to regard movement as an opportunity to acquire data from different perspectives instead of a challenge to hinder feature extraction. We construct a Phase-RSS model by analyzing the influence of moving and liquid on the phase and RSS signals. First, we propose a method to calculate the distance from the tag to the reader antenna. Second, we explore an identification method to identify liquid type by extracting signal features Phase-RSS coefficient$C_{P-R}$and Maximum Response Distance (MRD). Experimental results demonstrate an average accuracy of 96.80% in identifying 10 common liquids, which shows the great potential of MLiquID for mobile liquid sensing.

Integrated Energy Systems (IES) play a crucial role in addressing energy supply-demand imbalances and enhancing energy utilization efficiency. This paper presents Energy Management Decision Transformer (EMDT), a novel approach aimed at improving decision-making efficiency within IES. Unlike traditional methods that rely on precise modeling, EMDT employs the causal transformer framework to streamline decision-making processes. Methodologically, we propose a conditional sequence modeling approach, leveraging pre-collected datasets to extract insights from historical data and introducing reward paradigms and training techniques to optimize IES operations. Through extensive evaluation over thousands of operational days within IES, we demonstrate the effectiveness of the EMDT system in enhancing the stability of energy utilization systems and its ability to adapt to fluctuating demands and price conditions. This research contributes to advancing the field of energy management by providing a versatile and adaptive framework for decision-making in complex energy systems.

Vectorized reconstruction and topological cognition of road structures are crucial for autonomous vehicles to handle complex scenes. Traditional frameworks rely heavily on high-definition (HD) maps, which place significant demands on storage, computation, and manual labor. To overcome these limitations, we introduce a lightweight Road Cognition and Automated Labeling (RCAL) system. It leverages lightweight road data captured from mass-produced vehicles to vectorize road elements and cognize their topology. RCAL compiles multi-trip data on cloud servers for enhanced accuracy and coverage, addressing the limitations of single-trip data. In the field of element extraction, we proposed a pivotal point priority sampling strategy that can balance the contradiction between road scale and processing efficiency. Additionally, traffic flow is utilized to enhance the accuracy of road topology cognition. With its impressive automation, reliability, and efficiency, RCAL stands as an advanced solution in the field. Our evaluations on the intersection dataset from the real world confirm that RCAL not only achieves comparable precision to traditional HD map labeling systems but also substantially reducing resource costs.

Multi-access Edge Computing (MEC) is an emerging computing architecture to release the resource burden of the centralized cloud and reduce the mobile application latency. Services management and MEC requests routing is a major problem in MEC systems. Existing works mainly focus on the one-hop centralized request routing strategies. However, the centralized one-hop routing method is not suitable enough since the MEC network is a distributed system, and the number of MEC requests increases dramatically. In this paper, we have proposed an online problem. In such problem, we jointly consider the mobile edge service management and the distributed multi-hop requests routing in an MEC network in which the MEC requests randomly generate. We prove that such problem is NP-Hard even in the off-line scenario. Furthermore, we propose an approximation algorithm to manage the MEC services and two distributed online algorithms to route MEC requests. The approximation ratio and competitive ratio of these algorithms have been analyzed. Experiments are carried out to evaluate the performance of the algorithms and simulation results imply that these algorithms are effective and efficient.