Search Results (2,052)

Reducing energy consumption while maintaining stable water quality remains a major challenge in ornamental aquaculture. This study proposes an integrated predictive and energy-aware aquaculture management framework combining Internet of Things (IoT) sensing, Long Short-Term Memory (LSTM)-based prediction, Digital Twin (DT) simulation, and Cyber-Physical System (CPS) control. Real-time sensor networks monitored dissolved oxygen (DO), ammonia (NH₃), temperature, pH, turbidity, and energy consumption in a koi pond over a 45-day deployment period. Forecasted environmental states generated by the LSTM model were validated through a physics-informed Digital Twin prior to actuator execution to improve operational reliability and control safety. Experimental results demonstrated strong agreement between the Digital Twin and observed pond dynamics, achieving R² values of 0.97 for dissolved oxygen and 0.94 for ammonia. Compared with conventional manual operation, the proposed smart predictive control mode reduced total energy consumption by 26.86%. Statistical analysis confirmed that the reduction was highly significant (p < 0.001), with average daily energy consumption decreasing from 212 ± 6.06 Wh/day under manual operation to 154.71 ± 4.52 Wh/day under smart predictive control. Full article

The growth of Internet of Things (IoT) environments has expanded the attack surface of modern systems. Trojan attacks are a major challenge as they evade conventional detection mechanisms and operate silently within legitimate processes. This paper presents an automated Trojan detection framework for Windows-based IoT gateways. The framework combines custom dataset generation informative feature engineering and deep learning-driven analysis. A dataset of 3000 real world executable samples was created through controlled sandbox execution and forensic monitoring. The process captured behavioral static and network-level characteristics. An initial set contained 146 extracted features. A multi-stage feature selection process identified 33 informative attributes. This step allowed efficient learning and preserved discriminative power. A custom deep neural network model named TrDNN was developed using these features. The model captures complex nonlinear patterns linked to Trojan activity. The framework was evaluated against five classical machine learning models. It was also compared with five deep learning baselines. Results show that TrDNN achieves strong detection performance. The accuracy is 0.975. The precision is 0.972. The recall is 0.969. The F1 score is 0.970. The study also examines inference time and energy consumption. The model shows a balance between detection effectiveness, computational cost and energy efficiency. This makes it suitable for resource-constrained IoT gateway deployment. The detection model was integrated into an automated real-time monitoring pipeline. The system enables continuous process surveillance through Windows command line automation with minimal operational overhead. Statistical validation used paired t tests, Wilcoxon signed rank tests and McNemar chi-square test. The performance gains are statistically significant and do not indicate overfitting. The framework provides a reliable, efficient and deployable solution for Trojan detection in modern IoT systems. Full article

As urban rail transit systems evolve within the Industrial Internet of Things (IIoT), the intelligent recovery of regenerative braking energy becomes critical for energy efficiency. However, the existing train operation optimizations primarily focus on time-domain synchronization, frequently neglecting the spatial impedance constraints of the DC traction network. This oversight creates a discrepancy between theoretical energy matching and actual absorption. To address this, this paper proposes a spatiotemporal synergistic optimization framework integrating the analysis of electrical energy transmission factors and train relative motion. First, a dynamic multi-node circuit model based on Kirchhoff’s laws is established to characterize train fleet operations. By evaluating electrical energy transmission factors, the current distribution ratio and line impedance loss are identified as primary determinants of absorption efficiency. This physically quantifies the coupling among instantaneous energy distribution, transmission loss, and source-load relative distance. Second, a time-domain integration-based gradient analysis framework is formulated to deconstruct the energy gradient into amplitude and directional components. By mapping the relative position and speed of interacting trains, their relative motion states are systematically categorized. Subsequently, an adaptive gradient optimization strategy based on these motion states is introduced, which fine-tunes dwell times to precisely guide train trajectories into a low-impedance “optimal window” for energy absorption. Finally, a case study using operational data from Luoyang Metro Line 1 validates the proposed framework. Results demonstrate that the framework achieves dual spatiotemporal matching of braking and traction trains, outperforming the traditional fixed timetable and improving the regenerative braking energy absorption rate by approximately 13%. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

The Internet of Things (IoT) has become a fundamental paradigm in modern communication systems, enabling the large-scale interconnection of sensors, actuators, and embedded computing platforms. This paper presents a decision-oriented framework for the selection of energy-sensitive wireless communication platforms in IoT systems. The proposed approach combines systematic measurement, structured feature engineering, and lightweight regression models to predict energy consumption and current demand for different hardware platforms and wireless technologies, including ESP32- and NORA-based devices utilizing Wi-Fi, Bluetooth Low Energy (BLE) and LoRa communication. The results confirm that simple and interpretable regression models can provide robust guidance for platform and technology selection in realistic real-world scenarios, without incurring the complexity associated with detailed physical-layer or protocol-level simulations. Full article

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

Artificial Intelligence (AI) and Digital Twins (DTs) can support the digital and energy transition in the construction sector; however, their application to the built environment presents both opportunities and limitations. This study aims to give a critical perspective on the topic analyzing the related key challenges, including error assessment, model interpretability, data availability, cybersecurity risks, organizational constraints, and lifecycle costs. Where AI is nowadays developed as a context-dependent tool set, it is most effective when embedded within integrated socio-technical systems rather than adopted as a universal solution. Instead, DTs can be intended as an enabling framework, integrating AI, Internet of Things (IoT), Big Data, and Building Management Systems (BMS) to enhance energy performance, indoor environmental quality, safety, maintenance, and decision-making at both building and urban scales. The direction international research on these topics is facing is clear as evidenced by the wide number of research papers published. The future of these technologies moves towards a simulative approach oriented towards the sustainable and fair development goals and will bring a broad transformation of the building environment where they are ever more integrated into each social and technical aspect. The work is supported by a case study developed at Sapienza University of Rome founded by the Italian National Recovery and Resilience Plan within Flagship Project 2 (FP2), “Energy Transition and Digital Transition in Urban Regeneration and Construction,” of the Rome Technopole ecosystem. Full article

With the continuous growth of global population and increasing pressure on food security, the transformation toward precise and intelligent agricultural production has become an inevitable trend. In this context, accurate identification of field weeds is crucial for improving crop yields and reducing agricultural inputs. However, agricultural Internet of Things (IoT) edge devices are generally subject to strict constraints in terms of power consumption, storage, and real-time performance. Existing lightweight convolutional neural networks often struggle to simultaneously achieve high accuracy and low resource consumption for fine-grained weed identification tasks. To address this challenge, this paper proposes a hardware aware lightweight convolutional neural network named Weed-Aware Ghost Enhanced Network (WAGENet) for microcontroller deployment. The network synergistically integrates Ghost low-cost feature generation, Mobile Inverted Bottleneck Convolution (MBConv) for deep semantic extraction, Squeeze and Excitation (SE) and Coordinate Attention (CA) dual attention mechanisms for channel space joint calibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context fusion. It constructs a progressive feature abstraction system from shallow textures to high-level semantics. On the public DeepWeeds dataset, WAGENet achieves 95.71% classification accuracy and 93.80% F1 score with only 0.163 M parameters and 2.43 × 10⁸ multiply accumulate operations (MACC), attaining a parameter efficiency of 587.19%/M and significantly outperforming existing mainstream lightweight models. The model has been successfully deployed on the STM32H7B3I microcontroller development board, achieving a single inference latency of 94.63 ms, an internal Flash footprint of only 686.95 KiB, and a single inference energy consumption of 41.45 mJ. Experimental results demonstrate that WAGENet achieves a trade off among accuracy, latency, and energy consumption under strict resource constraints, providing a reproducible microcontroller deployment paradigm for battery powered field robots, drones, and other agricultural IoT edge devices. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

The rapid digitalization of district heating systems (DHSs) has driven the large-scale deployment of thermal Internet of Things (TIoT) sensors, which generate massive real-time operational data. Traditional centralized computing architectures struggle to process massive concurrent data. Furthermore, they fail to balance the stringent low-latency demands of real-time control tasks with the low-energy constraints of battery-powered terminal devices. To solve the complex problem of minimizing the weighted sum of system latency and energy consumption, we propose an Improved Mayfly Algorithm (IMA). The algorithm integrates five targeted structural enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset mechanism, and simulated annealing (SA)-driven local search, to efficiently navigate the high-dimensional rugged decision space and mitigate premature convergence. Extensive simulation results show that the proposed collaborative architecture achieves the lowest total system cost compared with traditional isolated computing paradigms (local-only, edge-only, and cloud-only). Notably, the proposed IMA reduces the total baseline weighted cost by 17.2% compared with the standard MA. Furthermore, under maximum practical industrial workloads (750 concurrent tasks, representing a highly complex 2250-dimensional MINLP space), the IMA maintains strong scalability and dominance, outperforming the second-best algorithm (BWO) by 15.8%. This research provides a low-latency, energy-efficient scheduling solution for TIoT-enabled DHS, and offers technical support for the intelligent and low-carbon transformation of urban energy infrastructure. 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

Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-access circuits face increasing power loss, parasitic effects, interconnect complexity, and sensitivity to process variations at the nanoscale. To address these limitations, this paper proposes a quantum-dot cellular automata (QCA)-based decoder-driven static random-access memory (SRAM)-access architecture for compact and energy-efficient IoT perception-layer memory. The proposed framework integrates three main components: a majority-logic RAM cell with feedback-based storage and non-destructive readout, a compact 2 × 4 decoder with enable and auxiliary asynchronous set/reset control, and a 1 × 4 SRAM array in which the decoder is embedded to reduce routing and clocking overhead. The circuit layouts were implemented and functionally verified using QCADesigner 2.0.3, while the energy behavior was evaluated using QCADesigner-E. Simulation results confirm correct write/read (W/R) and address-selection behavior. The proposed 2 × 4 decoder achieves 86 QCA cells, 0.08 µm² occupied area, and one clocking unit, reducing cell count, area, and clocking by 48.19%, 50.00%, and 20.00%, respectively, compared with the best selected decoder baseline. The integrated 1 × 4 SRAM array achieves 684 cells and 14 clocking units, improving timing by 30.00% compared with the closest SRAM-array baseline. These results demonstrate that the proposed QCA-based memory-access structure provides a compact and low-overhead solution for energy-constrained IoT communication systems. 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 adoption of Internet of Things (IoT) technologies in smart grids enables fine-grained monitoring and control of energy systems. However, maintaining grid stability remains challenging when electricity production decreases unexpectedly due to fault-prone operating conditions at power generation units. This paper proposes an Artificial Intelligence of Things (AIoT)-based adaptive energy management framework that supports online adaptive demand-side control by detecting production drop anomalies and translating them into priority-aware load control actions. In practical energy systems, purely reactive strategies that trigger actions only after a demand violation may introduce temporary production–consumption imbalance and operational stress; therefore, the proposed framework targets preventive and data-driven intervention. Instead of relying on electricity production forecasting or static load shedding, the framework learns normal production behavior offline and identifies deviations using machine learning techniques. A fault modeling approach is used to generate scenario-based training data, and Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) algorithms are employed for anomaly detection and control magnitude estimation. Following anomaly identification, IoT-enabled devices are selectively regulated based on device priority levels via an MQTT-based communication infrastructure. The framework is evaluated through simulations conducted on the CupCarbon platform under normal and production degradation scenarios. Results demonstrate that early anomaly detection alone is insufficient without accurate estimation of the required demand reduction and that the proposed approach enables effective demand-side control while preserving critical loads, thereby supporting resilient smart grid operation. 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