Search Results (3,325)

The concept of building health has gained increasing attention due to its strong association with occupant well-being, safety, and the long-term performance of buildings. However, despite the existence of models that assess individual aspects of building conditions, standardized frameworks for comprehensively evaluating overall building health remain limited. A systematic assessment of building health can help identify potential risks and prevent unexpected consequences caused by system failures. This study provides a systematic review of existing approaches to building health assessment by synthesizing relevant indicators and evaluation methods reported in the literature. Key variables are standardized and organized into six major performance dimensions: safety performance, environmental quality, occupant-centred performance, system and infrastructure performance, energy performance, and economic performance. In addition, the review examines the roles of widely used assessment approaches, including Post-Occupancy Evaluation (POE), Internet of Things (IoT)- based monitoring, and machine learning techniques, in supporting building health evaluation. Finally, future directions are proposed to support the development of more comprehensive, data-driven building health assessment frameworks. Full article

Implementing cryptographic primitives on resource-constrained IoT devices involves tight latency, code-size, and energy budgets. This work proposes a general LLVM backend instruction-selection strategy that recognizes single-bit update idioms—typically expressed as LOAD–-(AND/OR)–-STORE sequences in SHA-256 and similar bit-oriented code—and lowers them to the most efficient target-specific bit-manipulation primitive when legality and cost conditions are met. As a concrete instantiation, we implement the strategy for the Renesas RL78/G23 ISA by rewriting eligible patterns into SET1/CLR1 instructions when the constant mask targets exactly one bit. We evaluate the resulting backend on an RL78/G23 platform using cycle counts and code size (bytes) across SHA-256-driven workloads motivated by firmware integrity checking, Merkle-tree hashing, HMAC-based authentication, password-based key derivation (PBKDF2), and chunk-level update validation. The observed cycle reductions are also converted to absolute time across the device’s supported on-chip oscillator frequencies to quantify latency impact under different clocking modes. The experimental validation in this work is limited to the RL78/G23 backend implementation. The underlying instruction-selection idea may be adaptable to other RL78-family devices or to other embedded architectures that provide equivalent single-bit set/clear or bitfield operations; however, such adaptations require target-specific legality checks, cost modeling, and separate experimental validation. Full article

The deployment of Deep Learning (DL) for anomaly detection in Industrial IoT (IIoT) is critically hampered by the non-stationary nature of industrial data streams and the lack of forensic-grade explainability. This systematic review synthesizes 48 peer-reviewed studies (2021–2025) to quantify the performance collapse of static models under concept drift and to establish operational criteria distinguishing post hoc feature attribution (Type A XAI) from forensic root-cause diagnosis (Type B XAI). Our analysis reveals three critical findings: (1) static DL models suffer a 15–22% F1-score degradation across wastewater, manufacturing, and energy sectors when deployed in non-stationary environments, rendering them operationally non-viable without continuous adaptation; (2) the current literature remains saturated with Type A explainability (80% of corpus through 2023), creating a Forensic Gap where operators receive statistical correlations but lack actionable maintenance directives; and (3) emerging 2024–2025 research marks a paradigm shift toward Type B methodologies, yet no unified framework bridges real-time detection with deep causal reasoning. To address these gaps, we contribute the following: (1) a validated operational taxonomy (Cohen’s $\kappa =0.84$) with reproducible five-criterion rubric enabling forensic XAI classification; (2) the first quantitative synthesis of drift penalties in industrial deployments; and (3) a three-tier Edge-Cloud Forensic XAI architecture that achieves 70% communication payload reduction via compressed latent vectors while integrating tnGAN-based data imputation (handling 20–30% missing data) and physics-guided causal reasoning engines. Our framework decouples millisecond-level edge detection from 1–3 s cloud-based forensic diagnosis, ensuring both operational responsiveness and actionable industrial insight. We conclude that the future of safety-critical IIoT demands “Forensic-by-Design” architectures leveraging machine unlearning for drift adaptation and LLM-based natural language interfaces for operator-facing explanations, positioning Industry 5.0 to bridge the gap between algorithmic detection and human-centered decision support. Full article

Low-cost capacitive (LCC) soil moisture sensors have considerable potential for precision agriculture due to their low cost, low energy consumption, and suitability for IoT-based systems. However, their field performance and the transferability of laboratory calibration to field conditions remain insufficiently documented. The objective of this study was to evaluate the field performance of LCC sensors calibrated in the laboratory and to assess practical calibration strategies for field application. Laboratory calibration was performed for eight soil types, and field performance was evaluated in four experiments under different soil and climatic conditions. The sensors showed high accuracy under laboratory conditions, with RMSE values of 0.002–0.022 m³/m³ for soil-specific calibration, but substantially larger errors when laboratory-derived calibrations were applied directly in the field (RMSE 0.055–0.191 m³/m³). Soil-specific field calibration gave the highest accuracy (RMSE 0.005–0.036 m³/m³), whereas a simplified one-point calibration also improved performance considerably (RMSE 0.006–0.078 m³/m³) while requiring much less effort. Sensor performance declined at high water contents and under saline conditions. The results show that low-cost capacitive sensors can be used for reliable field soil moisture monitoring. Full article

The rapid growth of prosumer photovoltaic installations has introduced significant supply–demand imbalances in modern power grids, motivating the development of energy management systems that can coordinate distributed resources without sacrificing local control responsiveness. This paper presents qToggleEMS, a distributed architecture that combines cloud-resident receding-horizon planning with edge-resident bounded-override control for prosumer sites equipped with photovoltaic generation, battery storage, and grid interconnection. The contribution is positioned at the systems-engineering level: a documented partitioning of responsibilities between a cloud planner (forecasting, price-aware scheduling) and an edge controller (sub-second actuation, autonomous fallback) that preserves planning quality while remaining operational under cloud–edge disconnection. The cloud component, powerHub, is implemented as a set of microservices communicating via MQTT and TimescaleDB; the edge component runs qToggleOS on an ARM single-board computer and accesses inverters directly via Modbus RTU, bypassing manufacturer-provided cloud APIs. The system was deployed at a commercial prosumer site for approximately two months using the prosumer-oriented optimization strategy. Compared with a within-period counterfactual baseline (the cost the site would have incurred under its previous flat-tariff contract), monthly energy costs decreased by 14–15%. An analytical projection of the producer-oriented strategy using historical day-ahead prices from OPCOM PZU suggests a revenue uplift of approximately 23%, pending field validation. Full article

Wireless sensor networks (WSNs) are fundamental to the Internet of Things (IoT) and are widely used in environmental, industrial, and healthcare applications. However, their operational lifetime is constrained by the limited energy resources of sensor nodes. The Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol reduces energy consumption through clustering but suffers from random cluster head (CH) selection, leading to uneven energy usage and reduced stability. This study introduces a hybrid optimization approach, LEACH-CSA, which integrates the Crow Search Algorithm (CSA) with LEACH to enhance CH selection and positioning. The proposed method employs CSA’s intelligent search behavior to minimize intra-cluster distances and balance energy consumption across nodes. MATLAB simulations with 100 sensor nodes in a 100 × 100 m² area demonstrate that LEACH-CSA significantly reduces energy consumption and extends network lifetime compared with LEACH and its variants. Furthermore, CSA parameters were optimized using a progressive randomized tuning strategy with 1000, 2000, and 4000 candidate configurations. A comparative evaluation against LEACH-based GA, PSO, GWO, and WOA demonstrated that LEACH-CSA consistently improved the FND metric under different node density and area-scaling scenarios. Full article

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

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. 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

Under low wind speed conditions, conventional bluff body energy harvesters suffer from a single vibration mechanism and a narrow effective wind speed range, making it difficult to meet the continuous power supply demands of miniature electronic devices. In this paper, by systematically optimizing the number of triangular prisms N and the circumferential installation angle α, a parametrically adjustable star-shaped energy harvester (SEH) is proposed. The proposed structure consists of a cylindrical base with a tunable number of triangular prisms uniformly distributed along its circumference, aiming to reveal the regulation mechanism of the VIV-galloping coupling response and energy harvesting performance. Conceptual design and theoretical modeling of the SEH are first carried out. Then, three-dimensional fluid–structure interaction simulations are performed by varying N and α, and a prototype is fabricated for wind tunnel experimental validation. The results show that under the optimal parameter combination of N = 7 and α = 51.4°, the SEH achieves a maximum output voltage of 12.2 V at a wind speed of 3.41 m/s, with a maximum output power of 1.488 mW, and the effective wind speed range is broadened to 2.5~12.44 m/s. Compared with the conventional cylindrical energy harvester (CEH), the SEH (N = 7) increases the maximum output voltage by 44.38%, the maximum output power by 108.4%, and expands the effective wind speed range by 198.50%. Through systematic optimization of key geometric parameters, this study achieves synergistic regulation of flow-induced vibration modes and performance enhancement, providing a parametric design basis for efficient low-speed wind energy harvesting, which can promote the development of self-powered technologies for micro-sensors and IoT devices. 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