Search Results (391)

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Wearable and skin-mounted electronics demand encapsulation designs that simultaneously provide strong barrier performance, mechanical reliability, and transferability under ultrathin conditions. In this study, a residual stress-balanced transferable encapsulation platform was developed by integrating a urethane-based copolymer superstrate [p(IEM-co-HEMA)] with inorganic thin films. The polymer, deposited via initiated chemical vapor deposition (iCVD), offered over 90% optical transmittance, low RMS roughness (1–3 nm), and excellent solvent resistance, providing a stable base for inorganic barrier integration. An ALD Al₂O₃/ZnO nano-stratified barrier initially delivered effective moisture blocking, but tensile stress accumulation imposed a critical thickness of 30 nm, where the WVTR plateaued at ~2.5 × 10⁻⁴ g/m²/day. To overcome this limitation, a 40 nm e-beam SiO₂ capping layer was added, introducing compressive stress via atomic peening and stabilizing Al₂O₃ interfaces through Si–O–Al bonding. This stress-balanced design doubled the critical thickness to 60 nm and reduced the WVTR to 3.75 × 10⁻⁵ g/m²/day, representing an order-of-magnitude improvement. OLEDs fabricated on this ultrathin platform preserved J–V–L characteristics and efficiency (~4.5–5.0 cd/A) after water-assisted transfer and on-skin deformation, while maintaining LT80 lifetimes of 140–190 h at 400 cd/m² and stable emission for over 20 days in ambient storage. These results demonstrate that the stress-balanced encapsulation platform provides a practical route to meet the durability and reliability requirements of next-generation wearable optoelectronic devices. Full article

This paper discusses whether the principal response features of composites subjected to fatigue loadings, including residual strength and lifetime statistics under variable amplitude (VA) loadings, can be resolved based on constant amplitude (CA) fatigue life data. The approach is based on the strength-residual strength-life equal-rank assumption (SRSLERA), providing a statistical correspondence between the static strength, residual strength, and fatigue life distribution functions under CA loadings. Under VA loadings, the strength degradation progression and then the fatigue lifetime are calculated by dividing the loading spectrum into a sequence of CA block loadings of given extents (including one cycle), and assuming that the strength at the end of a generic block loading equals the strength at the start of the consecutive one, namely the equivalent residual strength assumption (ERSA). The consequences of SRSLERA and ERSA are first discussed by re-elaborating a series of uniaxial, statistically sound CA residual strength and fatigue life data obtained under different loading ratios, R, ranging from pure tension to mixed tension–compression to pure compression. It is shown that the static strength Weibull’s shape and scale parameters, as well as the fatigue formulation parameters recovered under pure compression or tension loadings, represent the fingerprint of composite materials subjected to fatigue and characterize their uniqueness. The residual strength statistics, fatigue probability density functions (PDFs), and constant life diagram (CLD) construction are theoretically reported. Then, based on ERSA, the statistical lifetimes under VA loadings and the cycle-by-cycle damage progressions of block repeated loadings are analyzed, and a residual strength-based damage rule is compared to Miner’s rule. Full article

Quantifying uncertainty in complex systems is a central problem in reliability analysis and engineering applications. In this work, we develop an information-theoretic framework for analyzing linear consecutive k-out-of-n:G systems using the cumulative residual Tsallis entropy (CRTE). A general analytical expression for CRTE is derived, and its behavior is investigated under various stochastic ordering relations, providing insight into the reliability of systems governed by continuous lifetime distributions. To address challenges in large-scale settings or with nonstandard lifetimes, we establish analytical bounds that serve as practical tools for uncertainty quantification and reliability assessment. Beyond theoretical contributions, we propose a nonparametric CRTE-based test for dispersive ordering, establish its asymptotic distribution, and confirm its statistical properties through extensive Monte Carlo simulations. The methodology is further illustrated with real lifetime data, highlighting the interpretability and effectiveness of CRTE as a probabilistic entropy measure for reliability modeling. The results demonstrate that CRTE provides a versatile and computationally feasible approach for bounding analysis, characterization, and inference in systems where uncertainty plays a critical role, aligning with current advances in entropy-based uncertainty quantification. Full article

The research work presents an artificial intelligence-driven, energy-aware data aggregation and routing protocol for wireless sensor networks (WSNs) with the primary objective of extending overall network lifetime. The proposed scheme leverages reinforcement learning in conjunction with deep Q-networks (DQNs) to adaptively optimize both Cluster Head (CH) selection and routing decisions. An adaptive clustering mechanism is introduced wherein factors such as residual node energy, spatial proximity, and traffic load are jointly considered to elect suitable CHs. This approach mitigates premature energy depletion at individual nodes and promotes balanced energy consumption across the network, thereby enhancing node sustainability. For data forwarding, the routing component employs a DQN-based strategy to dynamically identify energy-efficient transmission paths, ensuring reduced communication overhead and reliable sink connectivity. Performance evaluation, conducted through extensive simulations, utilizes key metrics including network lifetime, total energy consumption, packet delivery ratio (PDR), latency, and load distribution. Comparative analysis with baseline protocols such as LEACH, PEGASIS, and HEED demonstrates that the proposed protocol achieves superior energy efficiency, higher packet delivery reliability, and lower packet losses, while adapting effectively to varying network dynamics. The experimental outcomes highlight the scalability and robustness of the protocol, underscoring its suitability for diverse WSN applications including environmental monitoring, surveillance, and Internet of Things (IoT)-oriented deployments. Full article

Bisphenol A (BPA) has been listed as a substance of very high concern (SVHC) due to its endocrine-disrupting properties according to REACH in 2017. European competent authorities have prepared a REACH restriction proposal to reduce BPA levels in the environment. The proposed limit for the concentration of free BPA and other bisphenols in articles is 10 mg kg⁻¹. If exceeded, migration testing can demonstrate that no more than 0.04 mg L⁻¹ is released from the product or material over its lifetime. German authorities are drafting a new restriction proposal after the original was temporarily withdrawn. The residual and migration limits mentioned above were key requirements from the previous restriction proposal. Numerous national and international standards exist for assessing how environmental factors affect the physical and chemical properties of products and materials—such as notch impact strength and tensile strength—but these standards do not cover the release of pollutants. A standardised procedure that covers all aspects of artificial weathering and monitors the subsequent release of pollutants is necessary, especially in the context of the regulation of these substances. An accelerated weathering procedure was established for non-protected samples. This material was not intended for outdoor applications. The testing procedure applied a typical weathering scenario that represents Central European climate conditions. The procedure was validated and applied to samples under distinct quality assurance aspects. Released BPA is quantified via an organic isotope dilution LC-MS/MS method. In parallel, identical samples were weathered outdoors on a weathering rack. Haze and yellowness index are measured to compare outdoor and weathering chamber results. Full article

The Maxwell–Boltzmann (MB) distribution is important because it provides the statistical foundation for connecting microscopic particle motion to macroscopic gas properties by statistically describing molecular speeds and energies, making it essential for understanding and predicting the behavior of classical ideal gases. This study advances the statistical modeling of lifetime distributions by developing a comprehensive reliability analysis of the MB distribution under an improved adaptive progressive censoring framework. The proposed scheme strategically enhances experimental flexibility by dynamically adjusting censoring protocols, thereby preserving more information from test samples compared to conventional designs. Maximum likelihood estimation, interval estimation, and Bayesian inference are rigorously derived for the MB parameters, with asymptotic properties established to ensure methodological soundness. To address computational challenges, Markov chain Monte Carlo algorithms are employed for efficient Bayesian implementation. A detailed exploration of reliability measures—including hazard rate, mean residual life, and stress–strength models—demonstrates the MB distribution’s suitability for complex reliability settings. Extensive Monte Carlo simulations validate the efficiency and precision of the proposed inferential procedures, highlighting significant gains over traditional censoring approaches. Finally, the utility of the methodology is showcased through real-world applications to physics and engineering datasets, where the MB distribution coupled with such censoring yields superior predictive performance. This genuine examination is conducted through two datasets (including the failure times of aircraft windshields, capturing degradation under extreme environmental and operational stress, and mechanical component failure times) that represent recurrent challenges in industrial systems. This work contributes a unified statistical framework that broadens the applicability of the Maxwell–Boltzmann model in reliability contexts and provides practitioners with a powerful tool for decision making under censored data environments. Full article

WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications. Full article

Wireless Sensor Networks (WSNs) face persistent challenges of uneven energy depletion, limited scalability, and reduced network lifetime, all of which hinder their effectiveness in Internet of Things (IoT) applications. This paper introduces a hybrid framework that integrates Bluetooth Low-Energy (BLE) mesh networking with Ant Colony Optimization (ACO) to deliver energy-aware, adaptive routing over a standards-compliant mesh fabric. BLE mesh contributes a resilient many-to-many topology with Friend/Low-Power Node roles that minimize idle listening, while ACO dynamically selects next hops based on residual energy, distance, and link quality to balance load and prevent hot spots. Using large-scale simulations with 1000 nodes over a 1000 × 1000 m field, the proposed BLE-ACO system reduced overall energy consumption by approximately 35%, extended network lifetime by 40%, and improved throughput by 25% compared with conventional BLE forwarding, while also surpassing a LEACH-like clustering baseline. Confidence interval analysis confirmed the statistical robustness of these results. The findings demonstrate that BLE-ACO is a scalable, sustainable, and standards-aligned solution for energy-constrained IoT deployments, particularly in smart cities, industrial automation, and environmental monitoring, where long-term performance and adaptability are critical. Full article

Wireless sensors serve as a critical means of information perception and collection, profoundly influencing human life and production. In order to optimize the problem of excessive energy drain caused by the selection of cluster heads and the transmission of paths in the network, this study proposes an energy-efficient clustering–routing algorithm that combines K-means++ initialization with the multi-objective Chaotic Mapping Walrus Optimization Algorithm (CM-WaOA). The CM-WaOA employs chaotic mapping and Pareto front optimization to balance node residual energy, cluster-head-to-base-station distance, inter-cluster-head distance, and intra-cluster node count variance when selecting cluster heads. Subsequently, the Sparrow Search Algorithm (SSA) refines routing paths through adaptive population sizing and elite retention, thereby reducing transmission path loss. The simulation results over 1000 rounds demonstrate that the CM-WaOA surpasses LEACH, EEUC, CGWOA, and EBPT-CRA in terms of energy drain, node survival, and latency; it achieves the highest average residual energy, the fewest dead nodes, the most surviving nodes, and the shortest network delay. These findings confirm that the CM-WaOA can still maintain good energy utilization and low-latency characteristics under different sensor densities, effectively extending the network lifetime. Full article

Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided by a dual-metric fitness function combining residual energy and intra-cluster distance. EEL-GA is evaluated against EEL variants using Particle Swarm Optimization (PSO), Differential Evolution (DE), and the Artificial Bee Colony (ABC) across key performance metrics, including energy efficiency, packet delivery, and cluster lifetime. Simulations using real environmental data confirm EEL-GA’s superiority in sustaining energy, minimizing delay, and improving network stability, making it ideal for smart forestry and mission-critical WSN deployments. The model also incorporates environmental dynamics, such as temperature and humidity, enhancing its robustness in real-world applications. These findings support EEL-GA as a scalable, adaptive solution for future energy-aware 3D WSN frameworks. Full article

This study examined the temporal dynamics of metal(loid) concentrations in agricultural soils, fern Amauropelta rivularioides, and surface waters in a peri-urban region on the Brazil–Paraguay border during 2019–2020. Elevated levels of As, Se, Co, Mn, Cu, and Zn raised concerns about environmental and human health risks, especially when compared to international guidelines. Post-harvest and pre-harvest periods, particularly during corn cultivation, revealed higher concentrations of toxic metals, suggesting cumulative effects of agrochemical use. Principal Component Analysis indicated significant geochemical variation, with particular emphasis on the Collection 1 period (1 June 2019). The fern A. rivularioides demonstrated metal accumulation, especially for As, Pb, Cr, and Ba, reflecting the influence of agrochemical residues and seasonal runoff. Surface waters displayed metal concentrations below detection limits, but phosphorus levels surpassed USEPA thresholds for eutrophication risk. Risk assessments indicated moderate to high contamination in soils, particularly for P, As, Mg, and Se. Hazard Quotient and Hazard Index values suggested chronic health risks, and Incremental Lifetime Cancer Risk values for dermal exposure to As, Pb, and Cr indicated an elevated cancer risk. Full article

Wireless sensor networks (WSNs) are widely used in environmental monitoring, industrial automation, and smart cities. The Hybrid Energy-Efficient Distributed (HEED) protocol is a popular clustering algorithm designed to prolong network lifetime by balancing energy consumption among sensor nodes. However, HEED relies on simple heuristics for cluster-head (CH) selection, which may not fully exploit the complex spatiotemporal patterns in node energy and topology. This paper introduces a novel protocol, Deep Learning–Hybrid Energy-Efficient Distributed (DL-HEED), which, for the first time, integrates a Graph Neural Network (GNN) into the clustering process. By leveraging the relational structure of WSNs and a comprehensive set of node and network features—including residual energy, node degree, spatial position, and signal strength—DL-HEED enables intelligent, context-aware, and adaptive CH selection. DL-HEED leverages the relational structure of WSNs through deep learning, enabling more adaptive and energy-efficient cluster head selection than traditional heuristic-based protocols. Extensive simulations demonstrate that DL-HEED significantly outperforms classic HEED achieving up to 60% improvement in the network lifetime and energy efficiency as the network size increases. This work establishes DL-HEED as a robust, scalable, and practical solution for next-generation WSN deployments, marking a substantial advancement in the application of deep learning to resource-constrained IoT environments. Full article

To address the issues of cluster imbalance and inefficient energy utilization in IoT-based microgrids with static nodes, a new routing protocol, called the link efficiency and available-energy-driven routing protocol (LEAD-RP), has been developed. This protocol introduces a flexible method for determining the optimal number of cluster heads by minimizing overall energy consumption during both cluster formation and steady-state transmission phases, thus improving network efficiency. The protocol refines the cluster head selection process by integrating residual energy and distance factors, resulting in a more balanced energy distribution and strategically placed cluster heads. Simulation results demonstrate that the LEAD-RP significantly extends the network’s operational lifetime. Full article

Icing disasters can significantly reduce the reliability of overhead transmission lines, while limited budgets of power grid enterprises constrain the scale of investment. To improve investment efficiency, it is essential to balance the reliability and economic performance of anti-icing strategies. Most existing studies on the performance evaluation of anti-icing strategies for transmission lines focus primarily on reliability, neglecting their economic implications. To address this gap, this paper proposes a time-evolution-based performance evaluation model for overhead transmission line anti-icing strategies. First, a lifetime distribution function of transmission lines during the icing period is constructed based on the Nelson–Aalen method and metal deformation theory. Subsequently, a quantitative risk model for iced transmission lines is developed, incorporating the failure rate, value of lost load, and amount of lost load, providing a monetary-based indicator for icing risk. Finally, a performance evaluation method for anti-icing strategies is developed based on the risk quantification model. Implementation cost is treated as risk control expenditure, and strategy performance is assessed by integrating it with residual risk cost to identify the optimal strategy through composite cost analysis. The proposed model enables a comprehensive assessment of anti-icing strategy performance, improving the accuracy of strategy selection and achieving a dynamic balance between implementation cost and transmission line reliability. The case study results demonstrate that the proposed method effectively reduces the risk of failure in overhead transmission lines under ice disasters while lowering anti-icing costs. Compared with two existing strategy selection approaches, the strategy based on this method achieved 46.11% and 32.56% lower composite cost, and 60.26% and 48.41% lower residual risk cost, respectively. Full article