Search Results (7,116)

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

In wireless power transfer (WPT) systems, magnetically coupled wireless power transfer has become a major research focus due to its advantages such as long transmission distance, strong tolerance to misalignment, and high power transfer capability. It is also widely applied in vehicle wireless power transfer systems. From the perspective of practical engineering applications, this paper investigates the problem of system parameter variations caused by changes in inductance and load, in combination with magnetically coupled structures. During actual system operation, misalignment of the coupling mechanism leads to variations in mutual inductance, while the load resistance may also fluctuate. These parameter changes result in alterations to the overall output characteristics of the system, which are detrimental to stable system operation. Moreover, adopting a dual-side communication control strategy is susceptible to interference from the system’s power circuitry. To address these issues, this paper proposes a novel state variable modeling method and designs a state observer based on the extended Kalman filter (EKF) algorithm to estimate the secondary-side parameters, thereby enabling observation of the voltage across the load at the receiver side. The state observer is configured with two operating modes to monitor variations in mutual inductance and load resistance. The observer outputs are compared with the actual load-side voltage, and the effectiveness of the proposed state observer is verified. Full article

Accurate node localization in wireless sensor networks (WSNs) is challenging under variable signal propagation and strict energy constraints. This paper presents a hybrid localization framework that combines a deep neural network (DNN) with particle swarm optimization (PSO) to improve accuracy while reducing energy consumption. The DNN learns the non-linear mapping from received signal strength indicator (RSSI) measurements to node coordinates, mitigating propagation effects. PSO jointly optimizes key DNN hyperparameters and selects a minimal subset of anchor nodes that preserve localization performance, thereby lowering communication overhead. Simulation results on 200-node networks show that the proposed DNN–PSO achieves a mean localization error (MLE) of 0.87 m, outperforming a standard DNN (1.32 m) and classical multilateration (3.84 m). The optimized anchor selection reduces per-cycle energy consumption by 23% (239 mJ to 184 mJ) while maintaining sub-meter accuracy. Performance remains stable across diverse propagation conditions and scales well with increasing network size. These results indicate that the proposed approach provides an effective accuracy–energy trade-off for resource-constrained IoT/WSN deployments requiring reliable localization. Full article

Underwater imaging is generally subject to complex degradation issues such as color distortion, contrast degradation, and detail blurring due to the selective absorption and scattering of light wavelengths by water. Existing deep learning methods have limitations in the collaborative optimization of local details and global color. To address this issue, this paper proposes a multi-scale enhancement network based on global and local feature fusion. By integrating the advantages of CNN and Transformer, it achieves joint optimization of global color correction and local detail enhancement. Specifically, MGLFNet extracts global and local features of the image through the global and local feature fusion block in the core component of the multi-scale convolution–Transformer block and performs dynamic fusion. Meanwhile, to extract features at different scales to enhance performance, we design a multi-scale convolution feed-forward network. Through the action of the fusion module and the feed-forward network, a color-rich and detail-clear enhanced image is obtained. A large number of experimental results show that MGLF-Net outperforms comparison methods in both qualitative and quantitative evaluations of visual quality, with PSNR and SSIM values of 25.37 and 0.918 on the UIEB dataset, respectively, as well as low memory usage and computational resource requirements. In addition, detailed ablation experiments prove the effectiveness of the core components of the model. Full article

In the Internet of Vehicles (IoV), connected vehicles generate high-load perception tasks with large-scale and multimodal sensitive data, imposing strict requirements on latency, computing, and privacy. Existing solutions still suffer from high task service latency and privacy risks. To address these issues, this paper proposes an integrated framework that jointly considers multi-flow task offloading, adaptive privacy preservation, and latency-aware resource incentive mechanism. Specifically, we propose a Location-Aware and Trust-based (LA-Trust) dual-node task offloading algorithm based on deep reinforcement learning (DRL), which treats pre-partitioned subtasks as multiple parallel flows and enables flow-level collaborative offloading optimization across neighboring nodes, allows subtask data uploading and processing to proceed concurrently, and incorporates node security into decision making. To further enhance privacy protection, a Distribution-Aware Local Differential Privacy (DA-LDP) algorithm is designed to adaptively inject artificial noise according to data heterogeneity, balancing privacy protection and task execution accuracy. In addition, a Delay-Cost Reverse Auction (DC-RA) algorithm is proposed to further reduce latency by introducing wireless channel modeling between idle vehicles and edge nodes into the incentive mechanism. Experimental results show that the proposed framework improves task execution accuracy by 38% and reduces offloading cost, delay, incentive cost, and auction communication latency by 64.41%, 64.64%, 19%, and 44%, respectively, while more than 60% of tasks are offloaded to high-trust nodes. Full article

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

Honey bees are very important to the ecological environment and human society, contributing significantly to biodiversity and global food security, with an estimated annual impact of $15 billion in crop pollination in the USA. Over 62% of honey bee colony decline has been observed between June 2024 and February 2025. This study investigates bee stress level monitoring due to external disturbances like mechanical vibrations by measuring internal air temperature, relative humidity, and CO₂ gas concentration levels of beehives. A new wireless sensor board for real-time monitoring of honey bee colonies was designed, built, and validated. The board incorporates NDIR-based SCD30 and SCD41 sensors for CO₂, temperature, and humidity monitoring, integrated with a custom-designed two-layer printed circuit board and a Particle Argon^TM microprocessor for Wi-Fi communication. The developed board was tested and validated with live beehives in summer and winter of 2024 and 2025. The experimental study results showed the adequacy of the built sensor board. Bee colony responses on the applied stimuli (knocks) show that bees responded with a temperature increase of over 5 °C, CO₂ concentration increase by 3000 to over 10,000 ppm, and, at the same time, relative humidity drop by about 10% inside beehives. Full article

Reliable data transmission over noisy channels requires effective error-correcting codes. While classical algebraic constructions, such as Bose–Chaudhuri–Hocquenghem (BCH) codes, remain industry standards, structured alternatives based on discrete wavelet transforms offer potential benefits in terms of implementation complexity and error resilience. This study presents a comparative analysis of BCH and wavelet-based linear block codes, focusing on their error-correction capability and overall performance under realistic wireless channel conditions. This work evaluates both coding schemes across five channel models: additive white Gaussian noise (AWGN), Rayleigh fading, sinusoidal attenuation, multiplicative Gaussian noise, and a composite Rayleigh-plus-sinusoid channel. Performance is assessed using bit error rate (BER), frame error rate (FER), and decoding reliability across a range of signal-to-noise ratios. Results show that wavelet codes achieve error-correction performance comparable to or slightly better than BCH in most channels. Notably, they demonstrate a consistent advantage in scenarios with periodic or slow-varying interference, outperforming BCH starting from the 1.5 dB SNR threshold where the wavelet code achieves a BER reduction of up to 48% and a 37.5% improvement in FER, significantly enhancing decoding reliability in structured noise environments. These findings indicate that wavelet-based codes are not only viable but, in specific practical environments characterized by structured noise, represent a superior alternative for robust and reliable communication systems. Full article

Optimizing drug administration remains a central challenge in the development of modern therapies, especially in the context of conditions that require spatiotemporal control of active substance release. In this context, hydrogels have been intensively investigated as polymeric platforms for drug delivery, through their three-dimensional hydrophilic structure, tunable properties, and compatibility with biological environments. This analysis presents an integrated approach to hydrogels used in drug administration, addressing the physicochemical fundamentals, the constitutive polymeric materials, and the mechanisms of response to relevant physiological stimuli. Recent experimental studies have been discussed, which highlight the use of hydrogels based on natural, synthetic, and hybrid polymers for controlled and targeted release, in correlation with various administration routes, including oral, injectable, transmucosal, and topical ones. Advanced functionalization strategies that allow adaptive responses to pH, temperature, glucose, enzymes, and reactive oxygen species are also analyzed. Furthermore, emerging directions integrating hydrogels with biosensors, microdevices, and wireless communication systems for real-time monitoring and on-demand release are highlighted. Overall, the analysis emphasizes the role of smart hydrogels as multifunctional platforms for complex therapeutic strategies while also underlining the current challenges associated with clinical translation and long-term performance. Full article

This paper proposed an ultra-miniaturized four-port dual-band multi-input multi-output (MIMO) antenna designed for wireless biomedical implantable devices, including wireless capsule endoscopy (WCE) and cardiac leadless pacemakers. The antenna supports operation in the wireless medical telemetry service (WMTS) band of 1.395–1.4 GHz and the industrial, scientific, and medical (ISM) band of 2.4–2.4835 GHz for wireless power transfer and data telemetry applications. Miniaturization is achieved through a partial meandered structural configuration, yielding an overall size of 8 × 6.4 × 0.5 mm³. The antenna is encapsulated within implantable biomedical devices containing batteries, sensors, and electronic components, and evaluated in both homogeneous and realistic heterogeneous body phantoms, including the large intestine and heart. The full-wave electromagnetic simulation results demonstrate good performance, including reflection coefficients of −31.19 dB and −30.07 dB, gains of −27.5 dBi and −17.5 dBi, −10 dB impedance bandwidths of 170 MHz and 370 MHz, mutual coupling below 20 dB, and fractional bandwidths of 12.2% and 15.1% at 1.4 GHz and 2.45 GHz, respectively. Specific absorption rate (SAR) analysis satisfies implantation safety limits. Link budget analysis confirms reliable communication over distances more than 20 m in both frequency bands with high-data rates up to 100 Mbps. MIMO channel parameters such as envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and total active reflection coefficient (TARC) confirm the usefulness of the proposed MIMO antenna. Consequently, the proposed MIMO antenna emerges as a highly promising candidate with, ultra-miniaturization, isolation, multiband operation ability with omnidirectional-like radiation pattern characteristics for several biomedical implants in wireless health monitoring systems. Full article

The increasing densification of cell-free massive multiple-input multiple-output (MIMO) networks makes access point switch on/off (ASO) a key mechanism for improving energy efficiency in future wireless systems. While reinforcement learning (RL) has been explored for ASO, differences in modeling assumptions and evaluation scope leave open questions regarding robustness and scalability. In this work, ASO is investigated from an explicit energy-efficiency perspective using a RL framework based on Proximal Policy Optimization (PPO). The policy learns state-dependent AP activation under partial observability using compact per-access point (AP) large-scale fading statistics and power parameters, without requiring instantaneous small-scale channel state information or combinatorial search, enabling practical online implementation. A comprehensive evaluation is conducted under a unified and reproducible simulation framework across three cell-free deployment scenarios of increasing size that preserve AP density while incorporating realistic channel and power consumption models. Performance is assessed through both average and distribution-based metrics. Numerical results show that the PPO-based policy consistently outperforms random activation and the all-on baseline, achieving energy-efficiency improvements of up to 66% and nearly 50%, respectively, while activating a comparable number of APs. Moreover, the learned policy maintains robust performance as the network scales, reducing the likelihood of highly energy-inefficient operating regimes. Full article

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications. Full article

Here, we develop a digital community management application (APP) and an energy prediction and analysis system for smart communities. The system integrates the internet of things (IoT) technology and multiple prediction models to improve the intelligence and automation of community energy management. The developed APP has the following functions: user classification, announcement notification, express delivery management, GPS positioning navigation, calendar, and energy forecast. The hardware architecture of the system consists of a voltage/current sensing module, a Wireless Fidelity (Wi-Fi) module, and an Arduino platform, allowing real-time feedback and display of power consumption data. The energy forecasting part proposes a two-layer hybrid model architecture. This architecture combines Seasonal Trend decomposition using Loess (STL) time series decomposition, extreme gradient boosting (XGBoost), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to predict residential electricity consumption trends over the next 3 years. The results of the model prediction are verified using the data on Taiwan’s electricity consumption. The model accurately predicts the average monthly residential electricity consumption with a relative error of 5.8%, an acceptable energy management accuracy. This system integrates APP applications and efficient prediction models, demonstrating its great potential in smart community energy management and enhanced resident interaction. Full article

The rapid chilling and aging stages following pork slaughter represent a critical window for determining final physicochemical quality and flavor development. To address the destructive nature of conventional meat quality assessment methods and the limitations of rigid spectral probes when applied to irregular biological surfaces, this study developed and validated a wireless monitoring system integrating a flexible visible/near-infrared (VIS/NIR) sensing array with ensemble learning algorithms. The proposed system enables non-destructive, continuous monitoring of pork quality during cold-chain storage. A DeepEnsemble regression model based on a stacking framework was constructed by integrating Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to predict pH, moisture content, and total amino acid concentration. During a 26 h dynamic aging experiment, the proposed model achieved coefficients of determination (R²) of 0.9019, 0.9687, and 0.9600 for pH, moisture content, and total amino acids, respectively, with prediction performance exceeding that of individual regression models. The wireless transmission module maintained stable data communication under low-temperature and high-humidity conditions (−20 °C and 0–4 °C), with packet loss rates below 0.1%. These results indicate that the proposed system can effectively capture the dynamic evolution of pork quality during aging and provides a practical non-destructive approach for intelligent pork quality evaluation, cold-chain monitoring, and digital management of meat supply chains. Full article

Multi-hop networks’ performance strongly depends on relay node placement, which affects delay, throughput, and coverage. This work introduces a dual-layer protocol combining Slotted ALOHA for node-to-relay communication and TDMA for relay-to-gateway transmission. Using a Java-based simulator, we evaluate three relay placement strategies—random, square grid, and hexagonal grid—considering metrics such as delay, throughput, packet collisions, and coverage. Results show that the hexagonal grid offers superior performance, reducing collisions, minimizing delay, and expanding coverage. A fallback mechanism for out-of-range nodes and sensitivity analysis of different backoff values are also included. The study quantifies the benefits of structured relay placement for LoRaWAN and wireless sensor networks, while also identifying challenges for realistic deployments. These findings provide guidelines for designing scalable and reliable IoT networks and highlight directions for future work involving irregular placements and dynamic routing. The simulation results are intended to provide comparative, trend-based insights under conservative modeling assumptions, rather than absolute performance predictions. Full article