Search Results (980)

With the deepening integration of 5G-Advanced (5G-A) technology into smart manufacturing, the large-scale deployment of dynamic terminals—such as mobile robots and automated guided vehicles (AGVs)—within industrial private networks introduces complex, time-varying penetration and path losses. This significantly degrades the accuracy of conventional signal quality and capacity estimation methods, which were primarily designed for static terminal scenarios, thereby posing substantial challenges to coverage and deployment planning of industrial 5G access points, with downstream implications for power capacity dimensioning. To address this problem, this paper proposes a coverage-driven base station deployment optimization method formulated as a combinatorial optimization problem. The study constructs a signal strength assessment and network throughput calculation model tailored for dynamic industrial environments. This model captures the joint impact of terminal mobility and environmental obstacles on signal propagation, thereby enabling more reliable estimation of coverage performance and power consumption. Furthermore, by formulating the base station placement optimization as a combinatorial optimization problem, and by introducing mechanisms for equivalent transformation of the objective function and data preprocessing, the proposed method substantially reduces redundant computations during heuristic iterations. Simulation results verify that, compared with conventional static planning approaches, the proposed scheme enhances both the accuracy and computational efficiency of deployment planning while maintaining coverage quality. This work provides a theoretical foundation and a practical methodology for deploying reliable and energy-efficient industrial 5G-A private networks. Full article

In this paper, we focus on online stochastic optimization problems in which random parameters follow time-varying distributions. In each round t, a decision is obtained from solving the current optimization problem. Then samples are drawn from distributions which are updated after obtaining the decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. To solve the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which is referred to as the MSALM. In each round, we construct model functions for the sample objective and constraint functions based on their properties, which reduce computational complexity. The step size is designed in a dynamic way and decreases as t increases to accelerate convergence. Due to the setting of the online stochastic problem, we use stochastic dynamic regret and constraint violation to measure the performance of our algorithm. Under certain assumptions, we prove that our algorithm’s stochastic dynamic regret and constraint violation have a sublinear bound in terms of the total number of slots T. We design simulation experiments to verify the efficiency of our online algorithm. Its performance is evaluated on a range of information and system engineering problems, including adaptive filtering, online logistic regression, time-varying smart grid energy dispatch, online network resource allocation, and path planning. In addition, in the context of the path planning problem, we integrate our algorithm with supervised learning to demonstrate its enhanced capabilities. The experimental results validate the performance of our new algorithm in practical applications. Full article

This study explores the formation of inclusion complexes between a newly synthesized N-(2-(butylamino)-2-oxoethyl)-2-(3-cyano-4-isobutoxyphenyl)-4-methylthiazole-5-carboxamide with β-cyclodextrin using density functional theory with dispersion correction (DFT-D3) at the B3LYP-GD3/3-21G, 6-31G(d), 6-31G’(d), and 6-311G(d) levels. Two orientations are considered: in Orientation A, the 3-cyano-4-isobutoxyphenyl moiety interacts with the primary hydroxyl rim of β-cyclodextrin, while in Orientation B, the amide side chain faces the wider rim. Complexation energies and thermodynamic parameters are calculated to determine stability. Electronic properties, including HOMO-LUMO energies, and global reactivity descriptors, such as electronegativity (χ), chemical potential (μ), hardness (η), and electrophilicity index (ω), are evaluated. Non-covalent interaction (NCI) analysis is also performed to visualize interaction sites. The results reveal the significant influence of orientation on the host–guest complex stability and electronic properties, providing valuable insights into cyclodextrin-based encapsulation systems. The study provides a computational blueprint for engineering cyclodextrin-based bio-functional systems, where orientation-controlled inclusion governs stability, reactivity, and performance. This can significantly impact the development of smart drug delivery systems, biosensors, and multifunctional biomaterials in modern bioengineering. Full article

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly dense regulatory landscape governing data processing, cybersecurity, product security, accountability, traceability, interoperability, and safety-relevant deployment. A PRISMA ScR-informed scoping review is used to examine how European Union regulation influences artificial intelligence adoption across four representative domains: energy and smart grids, smart buildings, mobility and transport systems, and industrial and manufacturing environments. The analysis draws on primary legal sources, the peer-reviewed literature, and policy and standards-related materials, and is structured around three dimensions: regulatory barriers, technological and architectural challenges, and cross-sector implications for governance, innovation, and competitiveness. The results show that regulation functions simultaneously as a constraint and an enabling condition. It increases compliance burden, raises integration complexity, and slows deployment in higher risk settings, while promoting trustworthy artificial intelligence, stronger cybersecurity, lifecycle governance, clearer accountability, and more interoperable digital infrastructures. The central finding is that regulation is not external to artificial intelligence adoption in cyber–physical systems, but actively shapes the design space within which such systems can be developed, integrated, validated, and scaled. Future progress therefore depends on regulation-aware systems engineering, stronger implementation guidance, and cross-sector reference architectures capable of aligning legal compliance with technical architecture and operational value creation. Full article

Electric vehicles are an integral part of transportation electrification and are increasingly embedded within smart-grid-integrated energy systems that support accessibility, efficiency, and reduced environmental impact. As electric vehicle adoption grows, new challenges emerge in intelligent vehicle control, energy management, load management, and EV integration into the smart grid. In response, this paper presents a comprehensive survey of electric vehicle systems covering market evolution, enabling technologies, operational performance, and the energy systems that underpin scalable electric mobility. The survey illustrates the need for real-time monitoring, control, and optimization while exploring advanced computational approaches in quantum computing and machine learning that can address these challenges. Finally, this work identifies open research challenges and future directions related to energy optimization, smart-grid integration, and intelligent load management to provide a unified perspective on electric vehicles as a key component of both intelligent vehicle systems and sustainable smart transportation. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

Because smart microgrids can flexibly integrate distributed energy resources and support grid-connected and islanded operation modes, they enhance power supply reliability and promote the efficient utilization of renewable energy. However, the open communication environment and physically exposed infrastructure introduce critical security challenges, including risks of physical hijacking and data leakage. Many existing authentication protocols either fail to address these threats or rely on heavyweight cryptographic operations such as bilinear pairings and modular exponentiation, resulting in high computational and communicational overhead. To address these issues, a lightweight authentication and key agreement (AKA) protocol for smart microgrids is proposed. The protocol symmetrically integrates Physical Unclonable Functions (PUFs) into the smart meter (SM) and smart microgrid control center (SMC) to protect stored secret information against capture attacks. Meanwhile, the SM and SMC register with the data center (DC) in a symmetric manner. During the AKA phase, the DC only assists in authenticating the identities of the SM and SMC online in a symmetric way, without participating in session key computation, thereby reducing the trust burden and computational load on the smart meters and control center. Formal security proof and informal security analysis demonstrate that the proposed protocol can resist known attacks such as physical hijacking and data leakage. Compared with existing smart microgrid authentication protocols, the proposed protocol has performance advantages and the lowest computational cost, confirming its suitability for resource-constrained microgrid environments. Full article

The Agricultural Energy Internet (AEI) represents an emerging systemic paradigm driven by the convergence of intelligent agriculture and rural energy transformation. It is not a simple extension of agricultural informatization or rural electrification; rather, it redefines agricultural processes—such as irrigation, greenhouse environmental control, supplementary lighting, cold-chain logistics, and agricultural machinery—as perceptible, computable, and schedulable energy-related processes, thereby enabling the deep integration of agriculture, energy, environmental management, and intelligent decision-making. This review systematically examines the evolutionary trajectory of AEI, from early agricultural digitalization and Internet of Things (IoT)-based monitoring to edge intelligence and digital twin technologies, and ultimately to the coordinated optimization of agriculture–energy–environment systems. A comprehensive technical framework is established, encompassing physical energy coupling, multi-source sensing and actuation, interconnection and interoperability, edge–cloud collaborative control, data governance, digital twin modeling, artificial intelligence-enabled optimization, and application-oriented decision-making. The review further highlights that high-quality data governance, edge–cloud collaboration, and digital twin calibration are critical enablers of the transition from visualization-oriented management to closed-loop intelligent operation. In addition, this study clarifies the complementary relationship between agricultural informatization and electrification: the former provides capabilities for perception, prediction, optimization, and coordination, whereas the latter provides a controllable execution chain. Together, they constitute the foundation of a cyber-physical agricultural energy system. Finally, frontier research directions are identified, including high-temperature solid oxide electrolysis for hydrogen production, edge AI–IoT-enabled closed-loop agricultural operation, and privacy, security, and trust mechanisms in federated edge intelligence. The findings suggest that AEI can serve as a strategic technological framework for supporting the next generation of smart agriculture toward low-carbon, resilient, and collaborative operation. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

Smart home environments are evolving toward context-aware intelligent systems with the rapid integration of the Internet of Things (IoT), edge computing, and artificial intelligence. In such settings, large volumes of heterogeneous sensor data must be continuously processed to support perception, behavior understanding, and autonomous decision-making. Deep learning has emerged as a key approach for transforming raw sensor signals into structured representations that enable these functions. This review examines recent advances in deep learning for smart home applications from a sensor-driven perspective. Existing studies are organized into five major domains: human activity recognition, health monitoring and assisted living, smart energy management, security monitoring and anomaly detection, and voice interaction and intelligent control. Representative methodological paradigms—including convolutional and recurrent neural networks, Transformers, graph-based learning, multimodal fusion, and deep reinforcement learning—are discussed with emphasis on their roles in signal representation, multimodal integration, and decision-oriented modeling. Despite notable progress, several challenges continue to limit real-world deployment. These include the scarcity of high-quality labeled data, privacy and security concerns associated with continuous sensing, limited generalization across environments and users, constraints of edge devices, and the limited interpretability of model output. Addressing these issues requires advances not only in model design but also in data-efficient learning, privacy-preserving architectures, and system-level integration. Future research is expected to focus on multimodal perception, distributed and edge intelligence, knowledge-enhanced modeling, and human-centered explainable systems. By synthesizing current developments and highlighting open challenges, this review aims to support the development of robust and deployable deep learning solutions for next-generation smart home systems. Full article

The widespread deployment of Internet of Things (IoT) technologies in smart-home energy systems has increased the demand for secure, context-aware, and energy-efficient access control (AC) mechanisms. Although blockchain-based AC provides immutability, auditability, and fine-grained policy enforcement, its dependence on on-chain decision-making introduces significant computational latency and energy overhead, limiting its suitability for resource-constrained IoT environments. This paper proposes Optimized Dynamic-Attribute-Based Access Control-IoT (ODABAC-IoT), a hybrid off-chain and decentralized ABAC framework that combines off-chain LightGBM inference with selective on-chain verification to reduce blockchain workload while preserving trust and transparency. This work focuses on improving the computational efficiency, latency, and energy consumption of blockchain-enabled AC within smart-home energy systems, rather than directly optimizing physical energy consumption. In the proposed framework, high-confidence access requests are evaluated off-chain, whereas uncertain requests are forwarded to smart contracts for final validation. This hybrid decision-making strategy reduces unnecessary blockchain transactions, lowers latency, and improves computational efficiency without compromising security. Experimental results demonstrate up to 65% reduction in blockchain transaction volume, 64% improvement in latency, and 65% reduction compared to on-chain ABAC and 50% compared to hybrid blockchain approaches. These gains correspond to a reduction in daily blockchain energy consumption from 10 kWh to 3.5 kWh in a representative household scenario. The results indicate that ODABAC-IoT improves scalability, energy efficiency of the digital control layer, and responsiveness in IoT-enabled smart home energy systems, offering an effective pathway toward energy-aware and secure AC in the digital infrastructure of smart home energy systems. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article

This work is devoted to the application of complex-valued neural networks based on the multilayer neural network with multi-valued neurons (MLMVN) for short-term electrical load forecasting in smart grid energy systems. Accurate forecasting is a critical component of energy management systems, as it directly impacts the efficiency of control and optimization strategies in increasingly distributed and stochastic environments. The proposed approach leverages the intrinsic properties of complex numbers to model periodicity and nonlinear relationships typical of load time series. A compact feedforward architecture with two hidden layers is adopted and combined with multiple preprocessing strategies, including unit circle encoding, Fourier transform representations, and hybrid feature mappings incorporating temporal information such as the day of the week. The performance of the proposed models is evaluated on real-world prosumer data and compared against two benchmarks: a seasonal persistence model and a Long Short-Term Memory network. Results show that MLMVN-based approaches achieve comparable or improved performance in terms of RMSE and error reduction capability, despite their lower architectural complexity. Fourier-based preprocessing methods demonstrate strong effectiveness in capturing underlying temporal patterns. These findings suggest that complex-valued representations provide a promising alternative to traditional deep learning approaches, offering a favorable balance between accuracy, interpretability, and computational efficiency in Smart Grid forecasting applications. Full article

Accurate forecasting of energy consumption in sensor-rich environments remains challenging due to strong inter-sensor dependencies, temporal variability, and heterogeneous sensor behavior. This paper proposes a lightweight and interpretable multi-sensor fusion framework for short-term energy consumption forecasting. The heterogeneous sensor dataset is first preprocessed to handle missing values, outliers, and temporal misalignment, followed by synchronization of the multivariate signals on a common timeline to enable consistent learning. The proposed framework systematically investigates multiple strategies for exploiting information from synchronized multi-sensor data without performing explicit feature elimination or time-lag engineering. In particular, three fusion paradigms are considered: (i) Early Fusion, where all sensor measurements are jointly used as input features for a multivariate regression model; (ii) Late Fusion, where individual sensor predictors are trained independently and their outputs are combined using reliability-based weighting; and (iii) an attention-inspired fusion strategy, in which adaptive weights are assigned to sensor-level predictions based on their predictive reliability estimated from training errors and normalized via a softmax function. In addition, classical machine learning models including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (GB) are evaluated under the same experimental conditions to provide a consistent benchmark. Experimental results on a real-world building energy monitoring dataset consisting of nine heterogeneous sensors demonstrate that multi-sensor fusion approaches consistently improve forecasting performance compared to single-model baselines. Among the evaluated strategies, Late Fusion provides stable performance across strongly correlated loads, while the attention-inspired fusion strategy exhibits improved robustness when handling sensors with varying predictive reliability. To ensure robustness and reproducibility, results are reported using multiple chronological validation splits, with performance evaluated in terms of RMSE, MAE, and $R^2$ along with statistical measures including standard deviation and confidence intervals. The proposed framework provides a practical balance between predictive accuracy, interpretability, and computational efficiency, making it suitable for smart building energy management and real-world deployment scenarios. Full article

Deploying post-quantum cryptography on highly constrained devices remains challenging due to the large key sizes and substantial storage and memory-traffic demands of leading lattice-based schemes. Although constructions such as Kyber, Dilithium, and NTRU offer strong resistance against quantum adversaries, their multi-kilobyte public keys and intensive memory access patterns limit practical adoption in microcontrollers, smart cards, and low-power edge environments. This work proposes a hybrid key-encapsulation mechanism that integrates a compact, seed-generated Module-LWE structure with a quantum-secure hash-based authentication layer. The design employs a small public seed to instantiate lattice matrices on demand via a lightweight pseudorandom generator and incorporates a Merkle-tree commitment to represent compressed auxiliary error information. Additional design considerations—including sparsity-aware secret keys, SIMD-friendly polynomial operations, and cache-efficient decryption paths—are intended to reduce runtime memory usage and computational overhead. The security of the proposed construction is analysed under both Module-LWE and hash-based one-way assumptions, with further consideration of constant-time execution and cache-line alignment to mitigate side-channel risks. This hybrid approach outlines a design pathway toward post-quantum key-encapsulation mechanisms suitable for deployment on memory-limited and energy-constrained platforms. Full article