This paper presents an energy-efficient transmission framework for federated learning (FL) in industrial Internet of Things (IIoT) environments with strict latency and energy constraints. Machinery subnetworks (SNs) collaboratively train a global model by uploading local updates to an edge server (ES), either directly or via neighboring SNs acting as decode-and-forward relays. To enhance communication efficiency, relays perform partial aggregation before forwarding the models to the ES, significantly reducing overhead and training latency. We analyze the convergence behavior of this relay-assisted FL scheme. To address the inherent energy efficiency (EE) challenges, we decompose the original non-convex optimization problem into sub-problems addressing computation and communication energy separately. An SN grouping algorithm categorizes devices into single-hop and two-hop transmitters based on latency minimization, followed by a relay selection mechanism. To improve FL reliability, we further maximize the number of SNs that meet the roundwise delay constraint, promoting broader participation and improved convergence stability under practical IIoT data distributions. Transmit power levels are then optimized to maximize EE, and a sequential parametric convex approximation (SPCA) method is proposed for joint configuration of system parameters. We further extend the EE formulation to the imperfect channel state information (ICSI). Simulation results demonstrate that the proposed framework significantly enhances convergence speed, reduces outage probability from 10−2in single-hop to 10−6and achieves substantial energy savings, with the SPCA approach reducing energy consumption by at least 2× compared to unaggregated cooperation and up to 6× over single-hop transmission.

Respiratory rate is an important vital sign that can be used to determine human physiological state. In recent years, Wi-Fi-based contactless respiration monitoring has drawn significant attention due to the prevalence of wireless local area network (WLAN) infrastructure. Most existing approaches to respiration monitoring perform well in controlled environments, without the presence of additional moving individuals in the area of interest. A few recent studies have attempted to reduce the impact of other people moving in the vicinity of the target individual. However, these approaches exhibit notable limitations, such as restricting the number of interfering individuals to one, or requiring a direct wired connection between the Wi-Fi transmitter and receiver for synchronization. To address these issues, in this study, we develop a contactless respiration monitoring system using commodity Wi-Fi devices, which we name RoSense. Through a series of empirical studies, we observe that the channel state information (CSI) for subcarriers is significantly affected by the presence of interfering individuals, but a small subset retain relatively clear signal patterns linked to the target’s respiration. Leveraging these findings, RoSense employs a signal power-based subcarrier selection strategy to identify high-quality subcarriers. The selected subcarriers are then aligned to enhance signal gain and fused to complement the weaker periodic parts. Additionally, RoSense periodically detects the quality of subcarriers, selecting the most effective subcarriers to maximize the contribution of high-quality ones. Extensive experiments were performed in real-world settings with 10 volunteers to verify the feasibility and effectiveness of RoSense. Our results demonstrate that RoSense is able to achieve robust respiration monitoring by suppressing the impact of interfering individuals.

Wi-Fi-based device-free localization (DFL) will be an integral part of many emerging applications, such as smart healthcare and smart homes. One popular approach to DFL in Wi-Fi makes use of fingerprinting based on channel state information (CSI). Unfortunately, high-quality fingerprints cannot easily be obtained in many real-world environments due to the complicated, time-varying and multipath conditions which exist. Additionally, existing methods struggle to update the DFL models in a real-time manner to track changes of environment. To address these issues, an online data-driven modelling DFL framework is designed for robustness enhancement. Specifically, the raw CSI data is first augmented with the hidden layer parameters of an online deep neural network to strengthen the pair-to-pair mappings between signal variations and a target’s location. The radio map created with the augmented fingerprints can be updated with new sequential data collected from other domains, such as different times and layouts of the same environment. Subsequently, a novel online DFL model is established using these augmented fingerprints, which itself can be updated with new sequential data from other domains without the need for retraining. A forgetting mechanism is considered to mitigate the effects of outdated data on the localization performance. To validate our new framework, a comprehensive set of experiments have been performed in various environments for different scenarios. The experimental results verify the robustness and responsive tracking ability of the proposed online data-driven modelling DFL framework.

In recent years, WiFi-based device-free localization (DFL) has attracted attentions due to the rapid development of location-based applications. The localization performance of data-driven DFL models highly relies on the quality of the fingerprint. However, it is difficult to obtain high-quality fingerprints in cluttered environments due to various uncertainties, such as environmental dynamics. To mitigate effects of uncertainties, this article proposes a variance-constrained local–global modeling method to enhance the localization performance. To be specific, the collected channel state information data from a specific environment are first divided into several groups depending on their statistical characteristics using the clustering method, and the local–global modeling mechanism is then implemented based on the extreme learning machine, a kind of noniterative single hidden layer feedforward neural network, to achieve the good representation of the whole environment. During the local–global modeling process, the proposed method is to simultaneously minimize the output weights of the neural network, training errors of the DFL model, and intragroup variances of the grouped data, which makes the created DFL model robust in cluttered environments and not sensitive to uncertainties. Comprehensive experiments in several scenarios, including different indoor environments, device positions and heights, target's orientations, and body shapes, are performed. Experimental results indicate that the proposed method could achieve better localization performance than selected baseline methods, demonstrating the effectiveness of the proposed variance-constrained local–global modeling mechanism in DFL.

Recent advances in WiFi-based device-free localization (DFL) mainly focus on stationary scenarios and ignore the environmental dynamics, hindering the large-scale implementation of the DFL technique. In order to enhance the localization performance in nonstationary environments, in this article, a novel multidomain collaborative extreme learning machine (MC-ELM)-based DFL framework is proposed. Specifically, the whole environment is first divided into several subdomains depending on the distributions of the collected data using a clustering algorithm, and a corresponding number of local DFL models are then built to represent these subdomains separately. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models in a global optimization manner. The created MC-ELM-based DFL model also can be incrementally updated with sequentially coming data without retraining to track the environmental dynamics. Extensive experiments in several indoor environments demonstrate the robustness and generalization of the proposed MC-ELM-based DFL framework.

In the past decade, great progresses have been made in WiFi-based device-free localization (DFL). However, some challenging issues still hinder the large-scale implementation of DFL techniques, mainly including fingerprint vanishing and environmental dynamics. In order to enhance the localization performance in cluttered environments, in this article, a modified hierarchical framework for DFL is designed, which consists of several functional modules. Specifically, the collected data are first divided into several subsets in the spatiotemporal separation module. Next, the raw data are mapped to another feature space to mitigate the effects of fingerprint vanishing with the help of a deep neural network. In the distributed modeling module, local DFL models are built to separately represent the subsets. Additionally, probability distributions of local DFL models are calculated to estimate and control the effects of the noise. Finally, a global DFL model is built by integrating all the local DFL models with the embedding of the probability distribution information of those local DFL models. In this manner, the localization performance in cluttered environments could be significantly enhanced by the proposed hierarchical framework. Comprehensive experiments in several indoor environments demonstrate the robustness and generalization performance of the proposed hierarchical framework.

Ubiquitous WiFi signals not only provide fundamental communications for a large number of Internet of Things devices, but also enable to estimate target’s location in a contactless manner. However, most of the existing device-free localization (DFL) methods only utilize the time dynamics of the received WiFi signals, leading to inaccurate DFL in the cluttered indoor environments. Because different layouts of environments and deployments of WiFi devices cause the different mathematical distributions of the data collected from the cluttered indoor environments. In this article, a multiple kernel representation-based extreme learning machine (ELM) is proposed, named integrated multiple kernel ELM (IMK-ELM), for strengthening the localization performance in the cluttered indoor environments utilizing spatiotemporal information. In the proposed IMK-ELM-based DFL, the whole data set is first divided into several subsets depending on their mathematical distributions through the$K$-means clustering algorithm, and then a corresponding number of local DFL models are built for all the subsets to capture both the time dynamics and spatial properties of the data. Finally, a global DFL model is achieved by seamlessly integrating all the local DFL models due to the consistency mechanism. In addition, the Fresnel zone sensing theory is utilized for helping understand and explain the essence of indoor DFL. Comprehensive experiments indicate that the proposed IMK-ELM-based DFL outperforms state-of-the-art methods in the cluttered indoor environments.

Device-free localization (DFL) is becoming one of the attractive techniques in wireless sensing field, due to its advantage that the target does not need to be attached to any electronic device. However, most of the existed approaches for DFL can only obtain satisfactory localization performance in specific small area, they cannot function well when implemented to complex and large area. In order to tackle this issue, in this article, a hierarchical framework is developed for large-scale DFL based on data and knowledge twin driven integration, which consists of two phases, including the offline training phase and the online localization phase. In the offline training phase, the complex and large monitoring area is first divided into some subdomains by the K-means clustering algorithm, and then training corresponding number of broad learning (BL)-based DFL models for each subdomain using the Fresnel phase difference as the fingerprints. Meanwhile, a class-specific cost regulation extreme learning machine (CCR-ELM) classifier is also trained for determining the attribution of the reference points, which can alleviate the impacts of imbalanced data distribution on classification results. In the online localization phase, the attributions of the testing points are first judged through the trained CCR-ELM classifier, after that, estimating the target's location in the corresponding subdomains using BL-based DFL models. The validity of the proposed hierarchical framework is evaluated both in small and larger areas, respectively.

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.