Search Results (380)

The convergence of Artificial Intelligence and the Internet of Things has given rise to the Artificial Intelligence of Things (AIoT), which enables connected systems to operate with greater autonomy, adaptability, and contextual awareness. In agriculture, this evolution supports precision farming, improves resource allocation, and strengthens climate resilience by enhancing the capacity of farming systems to anticipate, absorb, and recover from environmental shocks. This review provides a structured synthesis of the transition from IoT-based monitoring to AIoT-driven intelligent agriculture and examines key applications such as smart irrigation, pest and disease detection, soil and crop health assessment, yield prediction, and livestock management. To ensure methodological rigor and transparency, this study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search and multi-stage screening procedure was conducted across major scholarly repositories, resulting in a curated selection of studies published between 2018 and 2025. These sources were analyzed thematically to identify technological enablers, implementation barriers, and contextual factors affecting adoption particularly within low-income countries where infrastructural constraints, limited digital capacity, and economic disparities shape AIoT deployment. Building on these insights, the article proposes an AIoT architecture tailored to resource-constrained agricultural environments. The architecture integrates sensing technologies, connectivity layers, edge intelligence, data processing pipelines, and decision-support mechanisms, and is supported by governance, data stewardship, and capacity-building frameworks. By combining systematic evidence with conceptual analysis, this review offers a comprehensive perspective on the transformative potential of AIoT in advancing sustainable, inclusive, and intelligent food production systems. Full article

Condition monitoring has come to the forefront of intelligent manufacturing and is particularly important in Computer Numerical Control (CNC) machining processes, where reliability, precision, and productivity are crucial. The traditional methods of monitoring, which are mostly premised on single sensors, the localized capture of data, and offline interpretation, are proving too small to handle current machining processes. Being limited in their scale, having limited computational power, and not being responsive in real-time, they do not fit well in a dynamic and data-intensive production environment. Recent progress in the Industrial Internet of Things (IIoT), cloud computing, and edge intelligence has led to a push into distributed monitoring architectures capable of obtaining, processing, and interpreting large amounts of heterogeneous machining data. Such innovations have facilitated more adaptive decision-making approaches, which have helped in supporting predictive maintenance, enhancing machining stability, tool lifespan, and data-driven optimization in manufacturing businesses. A structured literature search was conducted across major scientific databases, and eligible studies were synthesized qualitatively. This systematic review synthesizes over 180 peer-reviewed studies found in major scientific databases, using specific inclusion criteria and a PRISMA-guided screening process. It provides a comprehensive look at sensor technologies, data acquisition systems, cloud–edge–IoT frameworks, and digital twin implementations from an architectural perspective. At the same time, it identifies ongoing challenges related to industrial scalability, standardization, and the maturity of deployment. The combination of cloud platforms and edge intelligence is of particular interest, with emphasis placed on how the two ensure a balance in the computational load and latency, and improve system reliability. The review is a synthesis of the major advances associated with sensor technologies, data collection approaches, machine operations, machine learning, deep learning methods, and digital twins. The paper concludes with what can and cannot be performed to date by providing a comparative analysis of what is known about this topic and the reported industrial case applications. The main issues, such as the inconsistency of data, the lack of standardization, cyber threats, and old system integration, are critically analyzed. Lastly, new research directions are touched upon, including hybrid cloud–edge intelligence, advanced AI models, and adaptive multisensory fusion, which is oriented to autonomous and self-evolving CNC monitoring systems in line with the Industry 4.0 and Industry 5.0 paradigms. The review process was made transparent and repeatable by using a PRISMA-guided approach to qualitative synthesis and literature screening. Full article

Real-time fire localization in urban environments remains a significant challenge due to sparse IoT sensor deployments, measurement uncertainties, and the computational uses of AI-based estimation techniques. To address these limitations, this paper proposes a Chaotic Interval-Based Multi-Objective Honey Badger Algorithm (CI-MOHBA) designed to improve the accuracy and reliability of fire source localization under uncertain and limited sensor data. The approach formulates localization as a multi-objective optimization problem that simultaneously minimizes source estimation error, false alarm rates, and computation time. CI-MOHBA integrates a new chaotic map to improve global search capability and interval arithmetic to effectively manage sensor uncertainty within sparse measurement environments. Experimental evaluation of the proposed chaotic map, supported by entropy convergence analysis and Lyapunov exponent verification, demonstrates the stability and robustness of the proposed technique. Results indicate that CI-MOHBA achieves an average localization error of 0.73 m and a false alarm rate of 8.2%, while maintaining high computational efficiency. Results show that the proposed algorithm is well-suited for real-time fire localization in urban IoT-based monitoring systems. Full article

IoT-integrated supply chains play an important role in managing the movement of products and distribution, which relies on the processing of real-time data gathered using sensors and IoT-connected vehicles to make informed decisions that reduce logistical expenses. However, the optimization of transportation and logistics scheduling is still one of the most difficult tasks, which requires balancing demand and vehicle capacity, as well as delivery time in varying circumstances. This research assesses the performance capabilities and utility of four optimization algorithms, differential evolution (DE), a genetic algorithm (GA), simulated annealing (SA), and prism refraction search (PRS), which are applicable in IoT-integrated logistical processes. Notably, on the basis of the unique characteristics possessed by the four algorithms, a combination approach referred to as Bidirectional PRS-SA Optimization (Bi-PRS-SA) was formulated. This method ideally exploits the strengths of global and local searches within the search space. Furthermore, the research aims to discuss the proposed conceptual framework for integrating the proposed strategy into an overall IoT framework that would initiate dynamic supply chain management through the adaptation of the proposed strategy. Results show that the proposed strategy is better than the existing strategies of DE, GAs, SA, and PRS in terms of an overall range of 15–25%. Statistical validation via the Wilcoxon signed-rank test confirms these improvements are significant (p < 0.05). The findings suggest that the Bi-PRS-SA framework offers a robust and scalable solution for real-time logistics management in IoT-enabled environments. Full article

The proliferation of Internet of Things (IoT) devices challenges deep learning (DL) deployment due to their limited computational power, while cloud offloading introduces high latency and network strain. Fog computing provides a viable middle ground. We present a resource-aware framework that intelligently partitions DL tasks between fog nodes and the cloud using a novel Binary Search-Inspired Recursive (BSIR) optimization algorithm for rapid, low-overhead decision-making. This is enhanced by a novel module that fine-tunes deployment by analyzing memory at a per-layer level. For true adaptability, a Retrieval-Augmented Generation (RAG) technique consults a knowledge base to dynamically select the best optimization strategy. Our experiments demonstrate dramatic improvements over established metaheuristics. The complete framework boosts memory utilization in fog environments to a remarkable 99%, a substantial leap from the 85.25% achieved by standard algorithms like Genetic Algorithms (GA), Simulated Annealing (SA), and Particle Swarm Optimization (PSO). The enhancement module alone improves these traditional methods by over 13% without added computational cost. Our system consistently operates with a CPU footprint under 3% and makes decisions in fractions of a second, significantly outperforming recent methods in speed and resource efficiency. In contrast, recent DL methods may use 51% CPU and take over 90 s for the same task. This framework effectively reduces cloud dependency, offering a scalable solution for DL in the IoT landscape. Full article

The increasing deployment of Internet of Things (IoT) sensors in precision agriculture has created critical challenges related to wireless communication range, energy efficiency, and data transmission latency, particularly in large-scale rural operations. This systematic survey, conducted following the PRISMA 2020 guidelines, investigates how drones, acting as mobile data collectors and communication gateways, can enhance the performance of agricultural wireless sensor networks (WSNs). The literature search was carried out in the Scopus and IEEE Xplore databases, considering peer-reviewed studies published in English between 2014 and 2025. After duplicate removal, 985 unique articles were screened based on predefined inclusion and exclusion criteria related to relevance, agricultural application, and communication technologies. Following full-text evaluation, 64 studies were included in this review. The survey analyzes how drones can be efficiently integrated with WSNs to improve data collection, addressing technical and operational challenges such as energy constraints, communication range limitations, propagation losses, and data latency. It further examines the primary applications of drone-based data acquisition supporting efficiency and sustainability in agriculture, identifies the most relevant wireless communication protocols and Technologies and discusses their trade-offs and suitability. Finally, it considers how drone-assisted data collection contributes to improved prediction models and real-time analytics in digital agriculture. The findings reveal persistent challenges in energy management, coverage optimization, and system scalability, but also highlight opportunities for hybrid architectures and the use of intelligent reflecting surfaces (IRSs) to improve connectivity. This work provides a structured overview of current research and future directions in drone-assisted agricultural communication systems. Full article

The growing pressure on industrial organizations to align digital transformation with sustainability objectives has intensified the need to systematically understand the role of emerging digital technologies in sustainable industrial development. The accelerated digitalization of industrial ecosystems has positioned the Internet of Things (IoT) as a critical enabler of corporate sustainability within Industry 4.0. However, evidence on how IoT contributes to environmental, social, and economic performance remains fragmented. This study conducts a systematic literature review following PRISMA 2020 guidelines to consolidate the scientific advances linking IoT with sustainable corporate management. The search covered 2009–2025 and included publications indexed in Scopus, EBSCO Essential, and MDPI, identifying 65 empirical and conceptual studies that met the inclusion criteria. Bibliometric analyses—such as keyword co-occurrence mapping and temporal heatmaps—were performed using VOSviewer v. 2023 to detect dominant research clusters and emerging thematic trajectories. Results reveal four domains in which IoT significantly influences sustainability: (1) resource-efficient operations enabled by real-time sensing and predictive analytics; (2) energy optimization and green digital transformation initiatives; (3) circular-economy practices supported by data-driven decision-making; and (4) the integration of IoT with Green Human Resource Management to strengthen environmentally responsible organizational cultures. Despite these advances, gaps persist related to Latin American contexts, theoretical integration, and longitudinal assessment. This study proposes a conceptual model illustrating how IoT-enabled technologies enhance corporate sustainability and offers strategic insights for aligning Industry 4.0 transformations with the Sustainable Development Goals (SDGs), particularly SDGs 7, 9, and 12. Full article

Background: The vehicle routing problem (VRP) is of great importance in the Industry 4.0 era because enabling technologies such as the internet of things (IoT), artificial intelligence (AI), big data, and geographic information systems (GISs) allows for real-time solutions to versions of the problem, adapting to changing conditions such as traffic or fluctuating demand. Methods: In this paper, we model and optimize a classic multi-link distribution network topology, including randomness in travel times, vehicle availability times, and product demands, using a hybrid approach of nested linear stochastic programming and Monte Carlo simulation under a time-window scheme. The proposed solution is compared with cutting-edge metaheuristics such as Ant Colony Optimization (ACO), Tabu Search (TS), and Simulated Annealing (SA). Results: The results suggest that the proposed method is computationally efficient and scalable to large models, although convergence and accuracy are strongly influenced by the probability distributions used. Conclusions: The developed proposal constitutes a viable alternative for solving real-world, large-scale modeling cases for transportation management in the supply chain. Full article

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article

The health and operational continuity of emergency responders are fundamental pillars of sustainable and resilient disaster management systems. These personnel operate in high-risk environments, exposed to intense physical, environmental, and psychological stress. This makes it crucial to monitor their health to safeguard their well-being and performance. Traditional methods, which rely on intermittent, voice-based check-ins, are reactive and create a dangerous information gap regarding a responder’s real-time health and safety. To address this sustainability challenge, the convergence of the Internet of Things (IoT) and wearable biosensors presents a transformative opportunity to shift from reactive to proactive safety monitoring, enabling the continuous capture of high-resolution physiological and environmental data. However, realizing a field-deployable system is a complex “system-of-systems” challenge. This review contributes to the field of sustainable emergency management by analyzing the complete technological chain required to build such a solution, structured along the data workflow from acquisition to action. It examines: (1) foundational health sensing technologies for bioelectrical, biophysical, and biochemical signals; (2) powering strategies, including low-power design and self-powering systems via energy harvesting; (3) ad hoc communication networks (terrestrial, aerial, and space-based) essential for infrastructure-denied disaster zones; (4) data processing architectures, comparing edge, fog, and cloud computing for real-time analytics; and (5) visualization tools, such as augmented reality (AR) and heads-up displays (HUDs), for decision support. The review synthesizes these components by discussing their integrated application in scenarios like firefighting and urban search and rescue. It concludes that a robust system depends not on a single component but on the seamless integration of this entire technological chain, and highlights future research directions crucial for quantifying and maximizing its impact on sustainable development goals (SDGs 3, 9, and 11) related to health, sustainable cities, and resilient infrastructure. Full article

Intrusion detection in Internet of Things (IoT) environments is challenged by high-dimensional traffic, heterogeneous attack behaviors, and severe class imbalance. To address these issues, this paper proposes EHFOA-ID, an intrusion detection framework driven by an Enhanced HawkFish Optimization Algorithm integrated with a hybrid deep ensemble. The proposed optimizer jointly performs feature selection and hyperparameter tuning using adaptive exploration–exploitation balancing, Lévy flight-based global searching, and diversity-preserving reinitialization, enabling efficient navigation of complex IoT feature spaces. The optimized features are processed through a multi-view ensemble that captures spatial correlations, temporal dependencies, and global contextual relationships, whose outputs are fused via a meta-learner to improve decision reliability. This unified optimization–learning pipeline reduces feature redundancy, enhances generalization, and improves robustness against diverse intrusion patterns. Experimental evaluation on benchmark IoT datasets shows that EHFOA-ID achieves detection accuracies exceeding 99% on UNSW-NB15 and 98% on SECOM, with macro-F1 scores above 0.97 and false-alarm rates reduced to below 2%, consistently outperforming state-of-the-art intrusion detection approaches. Full article

The rapid growth of Internet of Things (IoT) devices and the emergence of 5G/6G networks have created major challenges in secure and reliable data transmission. Traditional cryptographic algorithms, while robust, often suffer from high computational complexity and latency, making them less suitable for large-scale, real-time applications. This paper proposes a chaos-based encryption framework that uses the Sprott chaotic oscillator to generate secure and unpredictable signals for encryption. To achieve accurate synchronization between the transmitter and the receiver, two bio-inspired metaheuristic algorithms—the Pachycondyla Apicalis Algorithm (API) and the Penguin Search Optimization Algorithm (PeSOA)—are employed to identify the optimal control parameters of the Sprott system. This optimization improves synchronization accuracy and reduces computational overhead. Simulation results show that PeSOA-based synchronization outperforms API in convergence speed and Root Mean Square Error (RMSE). The proposed framework provides robust, scalable, and low-latency encryption for IoT and 5G/6G networks, where massive connectivity and real-time data protection are essential. Full article

Background/Objectives: There is increased advocacy for the potential for digital applications (apps) and the Internet of Things (IoT) to improve women’s health. We conducted a systematic review to assess and synthesise the role of Apps and the IoT in improving the health of non-pregnant women. Methods: Six databases were searched from inception to 13 February 2023. We included randomised controlled trials that assessed the effects of various Apps and the IoT with regard to improving the health of non-pregnant women in high-income countries. Our primary outcomes were health status and well-being or quality of life, and we assessed behaviour change as the secondary outcome. Screening, data extraction, and quality assessment were performed in duplicate. Study quality was assessed using the Cochrane Risk of Bias 2.0 tool. Narrative methods were used to synthesise study outcomes. Results: The search retrieved 18,433 publications and seven publications from six studies met the inclusion criteria. Participants included overweight or obese women, postmenopausal women, or women with stage I–III breast cancer. Intervention types varied across included studies but broadly included wearable or sensor-based personal health tracking digital technologies. The most commonly assessed intervention effect was on behaviour change outcomes related to promoting physical activity. Interventions administered yielded positive effects on health outcomes and well-being or quality of life in one study each, while three of the four studies that assessed behaviour change reported significant positive effects. Most included studies had methodological concerns, while study designs and methodologies lacked comparability. Conclusions: Based on our findings, the use of apps and the IoT may be promising for facilitating behaviour change to promote physical activity. However, more evidence is needed to assess the effectiveness of the IoT for improving health status, well-being and quality of life among non-pregnant women. Full article

Because Internet of Things (IoT) networks are widely deployed, they have become attractive targets for botnet and distributed denial of service (DDoS) attacks, which require effective intrusion detection. Convolutional neural networks (CNNs) can achieve strong detection performance, but their many hyperparameters are usually tuned manually, which is costly and time-consuming. This paper proposes a new hybrid metaheuristic optimizer, FW-CNN, that combines Grey Wolf Optimization and Red Fox Optimization to automatically tune the key hyperparameters of a one-dimensional CNN for IoT intrusion detection. The Red Fox component enhances exploration and helps the search escape local optima, while the Grey Wolf component strengthens exploitation and guides convergence toward high-quality solutions. The proposed model is evaluated using the N-BaIoT dataset and compared with a feedforward neural network as well as a metaheuristic-optimized model based on the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-CNN. It achieves a final accuracy of 95.56%, improving on the feedforward network by 12.56 percentage points and outperforming the Adaptive Particle Swarm Optimization–Whale Optimization Algorithm-based CNN model by 1.02 percentage points. It also yields higher average precision, Kappa coefficient, and Jaccard similarity, and significantly reduces Hamming loss. These results indicate that the proposed hybrid optimizer is stable and effective for multi-class IoT intrusion detection in real environments. Full article