Indoor sensing technologies are gaining increasing attention in the development of smart environments. Wi-Fi-based human activity recognition, which exploits channel state information (CSI), enables accurate detection of human movements by analyzing signal fluctuations caused by activity, even in complex indoor settings. This passive, device-free approach leverages the ubiquity of Wi-Fi signals, eliminating the need for wearable devices and improving user convenience. However, current Wi-Fi-based sensing systems face several limitations, including suboptimal real-time performance, inefficient resource utilization, and the absence of dedicated hardware platforms. To address these challenges, this article presents a wireless sensing device based on printed circuit board technology, integrated with a CSI-driven system for recognizing indoor human behavior. Experimental results across diverse scenarios demonstrate high recognition accuracy, underscoring the proposed system's potential to improve the efficiency and practicality of wireless sensing technologies in smart environments.

Industrial wireless sensor networks (IWSNs) play a critical role in enabling real-time monitoring and intelligent automation in modern industrial applications. However, maintaining reliable communication and efficient data transmission in dynamic and interference-prone environments remains a significant challenge. To address these limitations, this article proposes a graph neural network (GNN)-driven networking approach for IWSNs, designed to enhance communication robustness and optimize data processing. Our approach incorporates a minimum capacity constraint and a trainable slack parameter, enabling adaptive network configuration in response to changing conditions. By modeling the network topology as a graph, we formulate a device-centric joint node selection and power allocation (JNP) strategy, leveraging GNNs for real-time decision-making. Simulations benchmark the proposed method against state-of-the-art methods, showing up to a 70% average increase in fifth percentile rate across various network conditions. These results highlight the effectiveness of the proposed JNP strategy in improving IWSN performance for industrial applications.

Federated Learning (FL) offers a pioneering distributed learning paradigm that enables devices/clients to build a shared global model that can be obtained through frequent model transmissions between clients and a central server, causing high latency, energy consumption, and congestion over backhaul links. To overcome these drawbacks, Hierarchical Federated Learning (HFL) has emerged, which organizes clients into multiple clusters and utilizes edge nodes (e.g., edge servers) for intermediate model aggregations between clients and the central server. Current research on HFL mainly focus on enhancing model accuracy, latency, and energy consumption in scenarios with a stable/fixed set of clients. However, addressing the dynamic availability of clients – a critical aspect of real-world scenarios – remains underexplored. This study delves into optimizing client selection and client-to-edge associations in HFL under intermittent client participation so as to minimize overall system costs (i.e., delay and energy), while achieving fast model convergence. We unveil that achieving this goal involves solving a complex NP-hard problem. To tackle this, we propose a stagewise methodology that splits the solution into two stages, referred to as Plan A and Plan B. Plan A focuses on identifying long-term clients with high chance of participation in subsequent model training rounds. Plan B serves as a backup, selecting alternative clients when long-term clients are unavailable during model training rounds. This stagewise methodology offers a fresh perspective on client selection that can enhance both HFL and conventional FL via enabling low-overhead decision-making processes. Through evaluations on diverse datasets, we show that our methodology outperforms existing benchmarks on crucial factors such as model accuracy and system costs.

MOTIVATION: Proteins carry out most biological processes via interactions with other proteins, known as protein-protein interactions (PPIs). Accurately predicting PPIs is crucial for understanding protein function, yet existing methods often fall short in capturing their complex and hierarchical nature. RESULTS: We propose PF-AGCN, an adaptive graph convolutional network that leverages two distinct graph structures: a function graph representing hierarchical Gene Ontology term relationships and a protein graph modeling direct interactions between proteins. Unlike traditional graph attention networks, PF-AGCN preserves the original biological structures while dynamically learning new relationships, ensuring the retention of essential biological information. Additionally, our framework integrates a protein language model with stacked dilated causal convolutional neural networks, enabling the synergistic fusion of global sequence semantics and local structural patterns. Extensive experiments on a comprehensive protein dataset across three evaluation facets demonstrate PF-AGCN's superior prediction accuracy. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/smyang107/PFAGCN.

Visible light communication (VLC) technology has emerged as a promising solution to address the stringent requirements of indoor industrial communication scenarios, such as the dynamic capacity requirements of smart factory. However, the inevitable deployment of ultra-dense VLC access points introduces new challenges for VLC user equipments, including difficulties related to interference control, resource allocation, and intercell handover. Motivated by these, this article proposes a user-centric networking strategy tailored for indoor VLC systems. The proposed algorithm initiates by tackling system-wide interference mitigation through the use of spectral clustering to partition the network, thereby minimizing intersubnetwork interference. Subsequently, orthogonal subchannel allocation within each subnetwork is employed, along with subchannel multiplexing across subnetworks. Simulations demonstrate the efficacy of our proposed methods, showcasing superior performance in terms of achievable rates compared to benchmarks.

Hierarchical Federated Learning (HFL) extends conventional Federated Learning (FL) by introducing intermedi ate aggregation layers, enabling distributed learning in geograph ically dispersed environments, particularly relevant for smart IoT systems, such as remote monitoring and battlefield operations, where cellular connectivity is limited. In these scenarios, UAVs serve as mobile aggregators, dynamically connecting terrestrial IoT devices. This paper investigates an HFL architecture with energy-constrained, dynamically deployed UAVs prone to communication disruptions. We propose a novel approach to minimize global training costs by formulating a joint optimization problem that integrates learning configuration, bandwidth allocation, and device-to-UAV association, ensuring timely global aggregation before UAV disconnections and redeployments. The problem accounts for dynamic IoT devices and intermittent UAV con nectivity and is NP-hard. To tackle this, we decompose it into three subproblems: (i) optimizing learning configuration and bandwidth allocation via an augmented Lagrangian to reduce training costs; (ii) introducing a device fitness score based on data heterogeneity (via Kullback-Leibler divergence), device-to UAV proximity, and computational resources, using a TD3-based algorithm for adaptive device-to-UAV assignment; (iii) developing a low-complexity two-stage greedy strategy for UAV redeployment and global aggregator selection, ensuring efficient aggregation despite UAV disconnections. Experiments on diverse real-world datasets validate the approach, demonstrating cost reduction and robust performance under communication disruptions.

Unmanned aerial vehicles (UAVs) have attracted massive attentions in Internet of Vehicles (IoV) networks to support the communications among roadside units (RSUs) and IoV users. In UAV-assisted IoV systems, UAVs and RSUs generally work within the same frequency band to improve the system spectral utilization efficiency, limited by the lack of spectrum resources, which, however, can cause mutual interference. To cope with the interference and increase the capacity of UAV-assisted IoV systems, this article considers to distribute part of the data traffic from the ground IoV system to the unlicensed spectrum. Specifically, we consider a heterogeneous communication scenario, in which a UAV-assisted IoV system and a Wi-Fi system coexist well: the RSUs can properly occupy unlicensed spectrum to increase the capacity of the UAV-assisted IoV system while mitigating interference between the UAVs and RSUs, without affecting the Wi-Fi system’s communication performance. We then propose a joint power control and time allocation scheme for the UAV-assisted IoV system over licensed and unlicensed spectrum. Joint optimization method is used to obtain the optimal power and time allocation strategy to maximize the overall system capacity. Simulation results and comprehensive analysis have demonstrated the superior performance of the proposed scheme, as compared to the conventional and state-of-art resource allocation strategies.

Given the exponential surge in data traffic and the proliferation of connected smart devices, traditional radio frequency (RF)-based wireless communication systems have to confront mounting challenges of spectrum scarcity and access congestion, particularly for networks operated in low-frequency bands. Visible light communication (VLC) technology has emerged as a promising solution, but it has own limitations, including coverage constraints and limited uplink capability, necessitating hybrid systems that leverage VLC and RF. This paper focuses on an indoor hybrid VLC-RF system extending VLC to Wi-Fi’s public spectrum, enabling VLC’s uplink via RF while enhancing system capacity. Yet, integrating VLC-RF with Wi-Fi introduces new challenges due to the coexistence of VLC-RF with existing Wi-Fi systems. To address these challenges, we propose an intelligent coexistence approach, dynamically adjusts duty cycles to ensure fairness and performance optimization between VLC-RF and Wi-Fi. Moreover, a spectrum multiplexing algorithm is introduced in the coexistence approach to enable the hybrid VLC-RF system’s multiplexing transmission on public spectrum, while preserving Wi-Fi system transmission integrity without interference, thereby further optimizing resource utilization. Extensive simulations on a meticulously constructed system-level platform validate our approach, showcasing its efficacy in enhancing system performance while maintaining equitable transmission between hybrid VLC-RF and Wi-Fi systems.

This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing. We formulate an optimization problem to jointly optimize the mapping between task components and vehicles, and transmission powers of UAVs, while addressing the trade-off between i) completion time of tasks, ii) energy consumption of UAVs, and iii) data exchange cost among vehicles. We show that this problem is a mixed-integer non-linear programming, and thus NP-hard. We subsequently reveal that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology. Accordingly, we propose a decoupling approach by segregating template searching from transmission power allocation, where atemplatedenotes a mapping between task components and vehicles. For template search, we introduce a low-complexity algorithm for isomorphic subgraphs extraction. For power allocation, we develop an algorithm using$p$-norm and convex optimization techniques. Extensive simulations demonstrate that our approach outperforms baseline methods in various network settings.

This paper considers unmanned aerial vehicle (UAV)-assisted infrastructure-to-vehicle (I2V) communication employing UAVs as relays to increase the throughput between a roadside unit (RSU) and a vehicular user equipment (VUE). We investigate the UAV position planning problem under both single UAV and multiple cooperative UAVs scenarios while considering the mobility of the VUE, aiming to maximize the data rate of the system. We first consider using a single UAV and prove that the single UAV position planning can be formulated as a convex optimization problem, and then obtain the optimal position of the UAV. Next, we investigate the multiple cooperative UAVs scenario and formulate the joint power control and position planning problem to improve the data rate of the system under a fixed total power consumption. Numerical simulations are provided to verify our theoretical results. Our findings highlight the effects of important system parameters, such as height, transmit power, and the number of UAVs, on the optimal UAV positioning and system performance.

Internet of Underwater Things (IoUT) consists of numerous sensor nodes distributed in an underwater area for sensing, collecting, processing information, and sending related messages to the data processing center. However, the characteristics of the underwater environment will bring strict limitations on communication coverage and power scarcity to IoUT networks. Applying cooperative communications to IoUT networks can expand the communication range and alleviate power shortages. In this article, we investigate the cooperative communication problem in a power-limited cooperative IoUT system and propose a reinforcement learning-based underwater relay selection strategy. Specifically, we first determine the optimal transmit powers of the source node and the selected underwater relay to maximize the end-to-end signal-to-noise ratio of the system. Then, we formulate the underwater cooperative relaying process as a Markov process and apply reinforcement learning to obtain an effective underwater relay selection strategy. The simulation results show that the performance of the proposed scheme outperforms that of the equal transmit power settings under the same conditions. In addition, the proposed deep Q-network-based underwater relay selection strategy improves the communication efficiency compared with the Q-learning-based strategy, and the number of iterations needed for convergence can be effectively reduced.

With the increasing demand of vehicular data transmission, the utilization of cellular resources in low frequency bands is facing great challenges to meet the growing throughput requirements of cellular vehicle-to-everything (C-V2X) users. To solve this problem, we expand certain aspects of the vehicular business to the unlicensed spectrum, which enables C-V2X users to access unlicensed channels fairly and thus will greatly increase system capacity. Moreover, this approach also introduces coexistence issues between C-V2X users and unlicensed users. In this paper, a C-V2X and Wi-Fi coexistence scheme based on reinforcement learning is proposed while considering the system throughput and fairness. A Q-learning algorithm is utilized to determine the optimal duty cycle selection strategy in a multi-unlicensed-channels scenario. Simulation results show that compared with existing coexistence schemes, the proposed scheme can improve throughput performance considerably while ensuring fairness.