Search Results (2,895)

Reliable drinking-water production is difficult on remote islands where brackish-water delivery is intermittent, technical personnel are scarce, and reverse-osmosis plants are manually operated. This study presents an operational characterization of an Internet of Things (IoT) retrofit deployed in Santa Cruz, Galápagos; it is not a controlled before-and-after effectiveness evaluation. An ESP32-based M5Stack Tough controller, distributed ESP-NOW sensing nodes, relay–contactor interfaces, a binary pressure permissive, and a ThingSpeak cloud layer were integrated without replacing the existing pumps and membranes. The exported primary-flow channel contained 4,603,989 numeric observations, including 500 pre-official test readings. Operational analyses used 4,603,489 numeric observations from the official monitoring period; 4,603,340 values remained after nominal-range filtering, and positive flow had a median of 12 L/min (interquartile range: 11–15 L/min). Among 332 logged high-pressure commands, 326 were preceded by a low-pressure command (98.2% unbounded command-state consistency), whereas 275 occurred within a 120 s analytical bound (82.8%). The median low-to-high command delay was 27 s (interquartile range: 11–70 s). Four organizational representatives completed a published 41-item Industry 4.0 maturity instrument before and after deployment; the self-reported overall mean was 0.26 at baseline and 1.95 post-deployment, and these results are interpreted descriptively. Energy-consumption and production data were confidential and unavailable to the authors, while water-quality variables were not measured. The contribution is therefore a long-duration, local-first legacy retrofit with auditable telemetry and explicit limitations, rather than a claim of optimized desalination performance. Full article

Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector. Full article

Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning architecture for joint prescriptive maintenance, production rescheduling, and event-triggered communication. Machine condition was estimated from CNC milling data using temporal convolutional models; because predictive uncertainty failed a predefined validation gate, the controller used deterministic health estimates. Evaluation covered five controllers, six simulated scenarios, and 1800 matched episodes. Relative to Graph-MAPPO, MIRA reduced operational cost by 9.38%, weighted tardiness by 28.10%, unexpected failures by 17.39%, message count by 84.98%, and transmitted data by 83.83%, while increasing on-time completion by 23.55%, without a detectable difference in corrected critical-message recall. Across the three independently trained seeds, failures, safety violations, and message count favored MIRA consistently, whereas cost and tardiness favored MIRA in two seeds. Disabling the execution shield increased safety violations from 0 to 3.56 per episode. Post-training variation in the projected-health safe-start threshold from 0.124 to 0.132 produced no safety violations and only small changes in aggregate operational outcomes. Cross-domain health transfer to PHM 2010 failed without adaptation. The results support simulator-level decision coordination, while broader replication, variable-size deployment, and factory validation remain necessary. Full article

The rapid deployment of Internet of Things (IoT) devices across smart cities, healthcare systems, industrial automation, transportation networks, smart grids, and cyber-physical infrastructures has expanded the modern cyberattack surface. IoT devices are often constrained by limited processing capacity, memory, battery power, and communication bandwidth, making conventional security mechanisms difficult to deploy consistently at scale. Intrusion detection systems (IDSs) provide an important defensive layer; however, many machine-learning-based IDSs are developed under static assumptions and may experience performance degradation as traffic distributions evolve due to firmware changes, device onboarding, protocol updates, user behavior variation, or adaptive attacks. This paper presents a hybrid IDS framework that integrates supervised Random Forest classification, unsupervised Isolation Forest anomaly monitoring, and Kolmogorov–Smirnov (KS)-based concept drift monitoring. In the experimental pipeline, Isolation Forest is trained exclusively on benign traffic to ensure that the anomaly detector models normal behavior rather than an attack-dominated training distribution. The evaluation uses a large-scale chronologically sampled subset of the CICIoT2023 dataset containing 3,890,621 records while preserving the natural class distribution of 2.35% benign traffic and 97.65% attack traffic. The chronological 80/20 train/test split is established first at the file level, followed by systematic sampling within each split to reduce the risk of leakage across the evaluation boundary. On the 746,094-record test set, the proposed hybrid IDS achieved 99.73% accuracy, 99.89% precision, 99.83% recall, 99.86% F1-score, and a false positive rate of 4.77%. The corresponding confusion matrix contains TN = 16,683, FP = 836, FN = 1205, and TP = 727,370, yielding 95.23% specificity and 97.53% balanced accuracy. Standalone Random Forest marginally outperformed the hybrid model in raw accuracy and false positive rate; therefore, the contribution of the proposed framework is centered on deployment-oriented anomaly monitoring, drift awareness, and generalization rather than absolute superiority in static classification metrics. A leave-one-attack-family-out experiment withholding MITM-ArpSpoofing from training showed that the hybrid model detected 85.26% of the unseen attack-family samples, compared with 85.18% for Random Forest alone and 7.05% for Isolation Forest alone. These findings provide initial evidence of generalization to one held-out attack family but should not be interpreted as proof of broad zero-day detection capability. The framework is therefore positioned as a competitive IDS that combines supervised detection with anomaly monitoring and concept drift awareness for deployment-oriented IoT security. Full article

Hazard analysis and critical control point (HACCP) systems provide a preventive framework for food safety by implementing quality assurance plans, continuous monitoring, corrective actions, and risk mitigation strategies at critical control points throughout food processing, including dairy products such as ice cream. Artificial intelligence (AI) is increasingly transforming food safety management by enabling real-time monitoring, predictive analytics, and automated decision-making within food processing systems. This review critically examines the integration of AI technologies into HACCP systems for ice cream manufacturing, with an emphasis on improving hazard detection, process control, traceability, and the efficiency of corrective actions. The review evaluates the application of Internet of Things sensors, computer vision, and machine learning-based predictive monitoring systems across critical processing stages, including raw material reception, pasteurization, continuous freezing, and hardening/storage. Compared to conventional HACCP systems, AI-assisted technologies offer greater capabilities for anomaly detection, predictive maintenance, automated verification, and data-driven risk management. Nevertheless, their industrial implementation remains constrained by data quality limitations, infrastructure cost, cybersecurity risks, regulatory uncertainty, and limited model explainability. Accordingly, this review highlights key research gaps related to industrial scalability, validation under dynamic processing conditions, and the scarcity of ice cream-specific AI datasets. Finally, the review identifies future research directions and emerging opportunities for applying AI technologies in food processing and quality control systems, providing a framework for the evolution of intelligent HACCP systems in frozen dairy manufacturing. Full article

The rapid adoption of Internet of Things (IoT)-enabled applications has significantly expanded the cyberattack surface across a wide range of critical systems such as industrial IoT (IIoT), smart grids, transportation, healthcare, industrial control systems, and smart cities. False data injection attack (FDIA) has emerged as a serious security threat to these applications due to its stealthiness and adversarial nature, silently corrupting the data integrity of critical operational processes without triggering conventional detection mechanisms. Existing FDIA solutions rely on single-model architectures that are built based on classical or limited predefined attack scenarios. Such solutions often fail to achieve robust detection under adversarial and evolving attack conditions; accordingly, they lack generalisability and are insufficient to capture the broader scope of FDIAs. In this study, a hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier. The proposed framework addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability. Different types of stealthy and adversarial FDIA have been modelled and injected into the dataset samples for use in training the proposed model. The results show that the overall detection performance of the proposed architecture improved by 2.39 percentage points in terms of F1-score while maintaining a low false-positive rate of 0.49%. These findings reflect the effectiveness of feature representation learning via autoencoders and hybrid classification strategies against stealthy and adversarial FDIA patterns. Future work should include temporal modelling for further advancing robust detection against evolving adversarial threats. Full article

Vertical farming (VF) offers a smart way to grow crops in stacked layers inside controlled indoor environments. By doing so, it uses far less land and water than traditional open-field agriculture, making it a promising solution for cities with limited space and resources. In recent years, artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies have begun to transform vertical farming. These tools are moving the industry away from rigid, rule-based systems toward more flexible, data-driven operations that can adapt in real time. This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management. This review examines the performance of different algorithms, including Convolutional Neural Networks (CNNs), Random Forest, XGBoost, and LSTMs across hydroponic, aeroponic, and aquaponic systems. The review also covers IoT setups with multi-sensor networks, edge-cloud computing, and automated control systems. Commercial farms have shown real gains in resource efficiency and shorter supply chains. However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms. Overall, AI-powered vertical farming has strong potential to support resilient urban food systems. Future work should focus on lightweight edge AI models, improved data standards, explainable AI, and robust life cycle assessments to ensure the benefits outweigh the environmental and economic costs. Full article

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article

The digital transformation associated with Industry 4.0 has increased the demand for engineers with competencies in condition monitoring, the Internet of Things (IoT), and artificial intelligence (AI). However, evidence on educational methodologies integrating these technologies within a competency-based assessment framework remains limited. This study developed and preliminarily evaluated a competency-based educational methodology and characterized the technical, digital, and analytical competency profiles of Mechanical Engineering students following participation in smart maintenance learning activities. A descriptive–exploratory case study was conducted with 31 students enrolled in an Industrial Maintenance course. The methodology integrated vibration analysis, infrared thermography, acoustic monitoring, IoT-based remote monitoring, and ChatGPT-assisted fault diagnosis. Competencies were assessed using an analytic rubric applied by six faculty evaluators, while students’ perceptions were examined through a 22-item questionnaire. The Global Competency Index for Smart Maintenance (GCIIM) was 72.86%, indicating a moderate overall competency level. Critical evaluation of artificial intelligence achieved the highest score, whereas fault diagnosis, technical communication, and technological adaptability showed the lowest performance. Students reported a favorable overall perception score (75.04%) across the questionnaire dimensions. These findings provide preliminary evidence of the feasibility of an integrated competency-based framework for smart maintenance education and its potential to support the responsible incorporation of digital technologies and artificial intelligence into engineering curricula. Full article

Modern Industrial Internet of Things (IIoT) integrates machine learning models for monitoring and control. However, they remain vulnerable to adversarial machine learning (AML) attacks, where an adversary adds small changes to the input data. These small changes degrade model quality, reduce accuracy, and can ultimately compromise the safety and security of the entire system. AML research in IIoT often focuses on individual attack types, defense methods, and datasets. Existing reviews lack a unified quantitative and system-level perspective. Therefore, a systematic literature review (SLR) is needed to provide a holistic analysis of existing attacks, defenses, and databases. This SLR analyzes 50 research articles to provide a holistic view of AML threats in IIoT systems and identifies seven distinct attack types: gradient-based perturbations, GAN-generated samples, poisoning attacks, reinforcement learning-based (RL) strategies, saliency-based feature manipulation, false data injection, and hybrid approaches. To illustrate the range of observed impacts, selected studies report the following degradation examples: saliency-based attacks cause accuracy reductions of 6–11 percentage points; iterative gradient attacks reduce accuracy from 95–99% to 30–40% in SIEM systems; and RL-based attacks reduce detection rates from 100% to 0% in rule-based IDS settings. In addition to the analysis of attack types, this SLR also evaluates current defense methods to protect IIoT systems. It has been observed that existing defense mechanisms lack generalization and require high computational resources. Moreover, the testing is performed under simplified threat models. The analysis of datasets further shows a clear gap between realistic industrial benchmarks (such as SWaT, WADI, and NSL-KDD) and synthetic datasets used for controlled experiments. By connecting attack behavior, defense performance, dataset characteristics, and system-level effects, this SLR identifies the key research gaps that must be addressed in future work. Full article

Driven by Vision 2030, the construction industry in the Kingdom of Saudi Arabia (KSA) has expanded rapidly. This expansion has raised concerns about safety performance and created a need to identify the factors that most strongly influence safety outcomes. This study is an exploratory, perception-based investigation into the perceived importance of factors affecting construction safety performance and into the perceived value of emerging Industry 4.0 technologies in improving it. Data were collected from 68 construction engineering professionals using a structured questionnaire covering 50 safety-related factors across five domains: management, worker, equipment and environmental, organisational and policy, and technology integration. The Importance Index (I) method was used to analyse the responses, and the domain-level results are reported as descriptive prioritisations rather than as statistically validated constructs. The three highest-rated items were lack of management commitment to safety programmes (I = 91.2%), inadequate and infrequent safety training (I = 89.7%), and absence of safety awareness among top management (I = 88.5%), followed closely by weak enforcement of safety regulations (I = 87.4%). At the domain level, management-related factors recorded the highest mean (I = 81.7%), ahead of worker-related factors (I = 79.5%) and technology integration (I = 75.0%). Although respondents identified artificial intelligence (AI), the Internet of Things (IoT), virtual and augmented reality (VR/AR), building information modelling (BIM), and drones as valuable safety enablers, they rated organisational and human factors as more important than the technologies themselves. Extreme heat and adverse weather conditions (I = 81.7%) emerged as a leading environmental risk, underlining the need for climate-specific safety interventions in the KSA context. Heat stress could be considered a notable concern in this context, as construction work in KSA is often performed outdoors during extended periods of high summer temperatures, which can elevate the risk of heat exhaustion, heat stroke, and fatigue-related incidents. This contrasts with the more temperate conditions of many international comparator studies and helps to explain why respondents treat heat as a leading environmental concern. To the authors’ knowledge, this is among the few exploratory studies to bring management, workforce, policy, environmental, and Industry 4.0 technology determinants together within a single KSA-specific framework. The findings contribute to both theory and practice in construction safety by identifying priority areas for intervention and by providing a basis for future large-scale confirmatory studies. Full article

Industrial supervision can be expanded through the Internet of Things (IoT) without moving the control logic outside the PLC. This study evaluates a level-monitoring and control architecture using a point-to-point LoRa link between two Siemens S7-1200 PLCs; LoRaWAN is used solely as a conceptual architectural reference, and no gateway, network server, or OTAA/ABP procedures were implemented. An Arduino Uno with an Ethernet Shield W5100 exchanges variables with the PLC through Modbus TCP and transfers them via UART to Heltec LoRa ESP32 modules. Factory I/O simulates the process, and Adafruit IO provides remote supervision. The field campaign covered twelve locations between 10 and 120 m and 1200 frames. Reception, packet loss, RSSI, SNR, and the latency value calculated by the firmware were recorded. Overall reception was 94.17%, packet loss was 5.83%, and the mean latency value was 727.17 ms. The main contribution is the separation of local control from wireless communication and the quantitative evaluation of the link. Because the PLC maintained control when frames were lost, the solution is suitable for supervising slow processes, but not for critical loops. The results are specific to the evaluated radio and firmware configuration. Full article

The Message Queuing Telemetry Transport (MQTT) protocol has become one of the most widely used messaging protocols for IoT and Industrial Internet of Things (IIoT) applications because of its lightweight publish–subscribe architecture. However, the standard MQTT PUBLISH message supports only a single topic–payload pair, requiring multiple protocol transactions and acknowledgment exchanges when several related measurements need to be transmitted together. This limitation increases communication overhead and latency, particularly in data-intensive IoT and IIoT environments. To address this limitation, this paper proposes M_PUBLISH, a protocol-level extension that aggregates multiple topic–payload pairs into a single MQTT control packet. At the broker, the aggregated packet is transparently decomposed into standard MQTT PUBLISH messages before being forwarded to subscribers, preserving native topic-based routing and compatibility with existing MQTT subscribers. The proposed protocol is analytically and experimentally evaluated in terms of latency, protocol overhead, frame size, and goodput across all MQTT QoS levels. The protocol is implemented in the aMQTT broker and validated under representative IoT/IIoT scenarios, including packet loss, network latency, bandwidth limitation, and selective subscribers. The results show up to 95% lower end-to-end latency, up to 20× higher goodput, and significantly lower protocol overhead while remaining compatible with existing MQTT subscribers and require modifications to publishers and brokers. Full article

This article presents a systematic review of Internet of Things technology applications across the entire lifecycle of prefabricated buildings. By combining bibliometric analysis with qualitative research methods, seven key research topics in this field are identified and analyzed: integrated information management platforms, position tracking, quality checking and management, project management and cost control, carbon emissions monitoring, indoor environment monitoring, and data security and information encryption. The current research status and challenges pertaining to each of these topics are critically assessed with emphasis on the main challenges in terms of automation level and accuracy, system integration and data interoperability, and deployment economy and robustness. The findings reveal that current IoT applications in prefabricated buildings are mainly focused on data collection, data visualization, and status monitoring, and future research should further strengthen the integration of IoT with AI, big data, and other technologies to promote predictive analysis, intelligent optimization, and autonomous decision-making. Three key topics are subsequently discussed from a management perspective: collaborative carbon information flow management, human-centered health and safety management, and finally, smart operation, maintenance, and disassembly driven by a circular economy, and directions are proposed for their future integration and innovation with emerging technologies. This study provides directional recommendations and references for researchers and practitioners in related fields. The review further suggests that the future development of IoT-enabled prefabricated buildings requires not only technological breakthroughs but also the collaborative evolution of digital technologies, construction practices, and industrial systems, supported by effective management mechanisms, industry collaboration, and practical implementation strategies. Full article

Deep learning has been widely deployed in critical scenarios such as the Internet of Things (IoT), industrial sensing, network intrusion detection, and cyber-physical system monitoring, where model inference directly affects system security, operational reliability, and service continuity. However, existing adversarial example detection studies remain insufficient for practical IoT deployment, as their validation often overlooks endpoint resource constraints, heterogeneous data modalities, physical environmental interference, communication protocol specifications, adaptive attacks, and adversary capability models. Moreover, detection outcomes are rarely connected with deployment locations, computational overhead, formal security assurance, and subsequent response strategies, which limits their engineering applicability. To address these limitations, this review systematically synthesizes recent representative studies in adversarial example detection and constructs a unified analytical framework integrating detection evidence, IoT deployment feasibility, and adaptive-attack evaluation. Based on the source of detection evidence, existing methods are categorized into input-consistency-based, feature-statistics-based, predictive-uncertainty-based, model-reconstruction-based, runtime-context-aware, and multi-strategy fusion detection, while formal certification is discussed as an independent security-assurance dimension. The review further analyzes the principles, applicable conditions, limitations, compatibility conflicts with IoT deployment constraints, and typical failure modes of these methods. The analysis identifies four key challenges: the lack of IoT-native adaptive evaluation, limited anomaly-boundary identification and cross-modal generalization, insufficient deployment-time security assurance, and weak coordination between detection decisions and security responses. Future research should therefore emphasize feasible attack paradigms, hierarchical lightweight detection, reliable multimodal fusion, certifiable operational boundaries, and auditable end-to-end response mechanisms, thereby supporting the evaluation and deployment of adversarial example detection in IoT scenarios. Full article