Search Results (360)

This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically. Full article

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

As vital material carriers of human civilization, earthen sites are experiencing continuous surface deterioration under the combined effects of weathering and anthropogenic damage. Traditional surface conservation techniques, due to their poor compatibility and limited reversibility, struggle to address the compound challenges of micro-scale degradation and macro-scale deformation. With the deep integration of digital twin technology, spatial information technologies, intelligent systems, and sustainable concepts, earthen site surface conservation technologies are transitioning from single-point applications to multidimensional integration. However, challenges remain in terms of the insufficient systematization of technology integration and the absence of a comprehensive interdisciplinary theoretical framework. Based on the dual-core databases of Web of Science and Scopus, this study systematically reviews the technological evolution of surface conservation for earthen sites between 2000 and 2025. CiteSpace 6.2 R4 and VOSviewer 1.6 were used for bibliometric visualization analysis, which was innovatively combined with manual close reading of the key literature and GPT-assisted semantic mining (error rate < 5%) to efficiently identify core research themes and infer deeper trends. The results reveal the following: (1) technological evolution follows a three-stage trajectory—from early point-based monitoring technologies, such as remote sensing (RS) and the Global Positioning System (GPS), to spatial modeling technologies, such as light detection and ranging (LiDAR) and geographic information systems (GIS), and, finally, to today’s integrated intelligent monitoring systems based on multi-source fusion; (2) the key surface technology system comprises GIS-based spatial data management, high-precision modeling via LiDAR, 3D reconstruction using oblique photogrammetry, and building information modeling (BIM) for structural protection, while cutting-edge areas focus on digital twin (DT) and the Internet of Things (IoT) for intelligent monitoring, augmented reality (AR) for immersive visualization, and blockchain technologies for digital authentication; (3) future research is expected to integrate big data and cloud computing to enable multidimensional prediction of surface deterioration, while virtual reality (VR) will overcome spatial–temporal limitations and push conservation paradigms toward automation, intelligence, and sustainability. This study, grounded in the technological evolution of surface protection for earthen sites, constructs a triadic framework of “intelligent monitoring–technological integration–collaborative application,” revealing the integration needs between DT and VR for surface technologies. It provides methodological support for addressing current technical bottlenecks and lays the foundation for dynamic surface protection, solution optimization, and interdisciplinary collaboration. Full article

Creating shared spaces is a key part of making extended reality (XR) and Internet of Things (IoT) technology more interactive and collaborative. Currently, one system which stands out in achieving this end commercially involves spatial anchors. Due to the cloud-based nature of these anchors, they can introduce connectivity and privacy issues for projects which need to be isolated from the internet. This research attempts to explore and create a different approach that does not require internet connectivity. This work involves the creation of an AprilTags-based calibration system as a local solution for creating shared XR spaces and investigates its performance. AprilTags are simple, scannable markers that, through computer vision algorithms, can help XR devices figure out position and rotation in a three-dimensional space. This implies that multiple users can be in the same virtual space and in the real-world space at the same time, easily. Our tests in XR showed that this method is accurate and works well for synchronizing multiple users. This approach could make shared XR experiences faster, more private, and easier to use without depending on cloud-based calibration systems. Full article

Internet of Things (IoT) technology in healthcare has enabled innovative services that enhance patient monitoring, diagnostics and medical data management. However, securing sensitive health data while maintaining system efficiency of resource-constrained IoT devices remains a critical challenge. This work presents a comprehensive end-to-end IoT security framework for healthcare environments, addressing encryption at two key levels: lightweight encryption at the edge for resource-constrained devices and robust end-to-end encryption when transmitting data to the cloud via MQTT cloud brokers. The proposed system leverages multi-broker MQTT architecture to optimize resource utilization and enhance message reliability. At the edge, lightweight cryptographic techniques ensure low-latency encryption before transmitting data via a secure MQTT broker hosted within the hospital infrastructure. To safeguard data as it moves beyond the hospital to the cloud, stronger end-to-end encryption are applied to ensure end-to-end security, such as AES-256 and TLS 1.3, to ensure confidentiality and resilience over untrusted networks. A proof-of-concept Python 3.10 -based MQTT implementation is developed using open-source technologies. Security and performance evaluations demonstrate the feasibility of the multi-layer encryption approach, effectively balancing computational overhead with data protection. Security and performance evaluations demonstrate that our novel HECS4MQTT (Health Edge Cloud Security for MQTT) framework achieves a unique balance between efficiency and security. Unlike existing solutions that either impose high computational overhead at the edge or rely solely on transport-layer protection, HECS4MQTT introduces a layered encryption strategy that decouples edge and cloud security requirements. This design minimizes processing delays on constrained devices while maintaining strong cryptographic protection when data crosses trust boundaries. The framework also introduces a lightweight bridge component for re-encryption and integrity enforcement, thereby reducing broker compromise risk and supporting compliance with healthcare security regulations. Our HECS4MQTT framework offers a scalable, adaptable, and trust-separated security model, ensuring enhanced confidentiality, integrity, and availability of healthcare data while remaining suitable for deployment in real-world, latency-sensitive, and resource-limited medical environments. Full article

This review of literature discusses the use of emerging technologies in the prevention of assault, specifically Artificial Intelligence (AI), the Internet of Things (IoT), and wearable technologies. In preventing assaults, GIS-based mobile apps, wearable safety devices, and personal security solutions have been designed to improve personal security, especially for women and the vulnerable. The paper also analyzes interfacing networks, such as edge computing, cloud databases, and security frameworks required for emergency response solutions. In addition, we introduced a framework that brings these technologies together to deliver an effective response system. This review seeks to identify gaps currently present, ascertain major challenges, and suggest potential directions for enhanced personal security with the use of technology. Full article

Nowadays, the evolution and growth of machine learning (ML) algorithms and the Internet of Things (IoT) are enabling new applications. Smart weapons and people detection systems are examples. Firstly, this work takes advantage of an efficient, scalable, and distributed system, named SmartFog, which identifies people with weapons by leveraging edge, fog, and cloud computing paradigms. Nevertheless, security vulnerabilities during data transmission are not addressed. Thus, this work bridges this gap by proposing a secure data transmission system integrating a lightweight security scheme named GS3. Therefore, the main novelty is the evaluation of the GS3 proposal in a real environment. In the first fog sublayer, GS3 leads to a 14% increase in execution time with respect to no secure data transmission, but AES results in a 34.5% longer execution time. GS3 achieves a 70% reduction in decipher time and a 55% reduction in cipher time compared to the AES algorithm. Furthermore, an energy consumption analysis shows that GS3 consumes 31% less power than AES. The security analysis confirms that GS3 detects tampering, replaying, forwarding, and forgery attacks. Moreover, GS3 has a key space of $2^{544}$ permutations, slightly larger than those of Chacha20 and Salsa20, with a faster solution than these methods. In addition, GS3 exhibits strength against differential cryptoanalysis. This mechanism is a compelling choice for energy-constrained environments and for securing event data transmissions with a short validity period. Moreover, GS3 maintains full architectural transparency with the underlying armed detection system. Full article

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) in Building Energy Management Systems (BEMSs) offers transformative potential for improving energy efficiency, enhancing occupant comfort, and supporting grid stability. However, the adoption of these technologies in the European Union (EU) is significantly influenced by a complex regulatory landscape, including the EU AI Act, the General Data Protection Regulation (GDPR), the EU Cybersecurity Act, and the Energy Performance of Buildings Directive (EPBD). This review systematically examines the legal, technological, and economic implications of these regulations on AI- and IoT-driven BEMS. Following the PRISMA-ScR guidelines, 64 relevant sources were reviewed, comprising 34 peer-reviewed articles and 30 regulatory or policy documents. First, legal and regulatory barriers that may hinder innovation are identified, including data protection constraints, cybersecurity compliance, liability concerns, and interoperability requirements. Second, technological challenges in designing regulatory-compliant AI and IoT solutions are examined, with a focus on data privacy-preserving architectures (e.g., edge computing versus cloud processing), explainability requirements for AI decision-making, and cybersecurity resilience. Finally, the economic opportunities arising from regulatory alignment are highlighted, demonstrating how compliant AI and IoT-based BEMS can enable energy savings, operational efficiencies, and new business models in smart buildings. By synthesizing current research and policy developments, this review offers a comprehensive framework for understanding the intersection of regulatory requirements and technological innovation in AI-driven building management. Strategies are discussed for navigating regulatory constraints while leveraging AI and IoT for energy-efficient, intelligent building operations. The insights presented aim to support researchers, policymakers, and industry stakeholders in advancing regulatory-compliant BEMS that balance innovation, security, and sustainability. Full article

The increasing volume of global waste presents a critical environmental and societal challenge, demanding innovative solutions to support sustainable practices such as recycling. Advances in Computer Vision (CV) have enabled automated waste recognition systems that guide users in correctly sorting their waste, with state-of-the-art architectures achieving high accuracy. More recently, attention has shifted toward lightweight and efficient models suitable for mobile and edge deployment. These systems process data from integrated camera sensors in Internet of Things (IoT) devices, operating in real time to classify waste at the point of disposal, whether embedded in smart bins, mobile applications, or assistive tools for household use. In this work, we extend our previous research by improving both dataset diversity and model efficiency. We introduce an expanded dataset that includes an organic waste class and more heterogeneous images, and evaluate a range of quantized CNN models to reduce inference time and resource usage. Additionally, we explore ensemble strategies using aggregation functions to boost classification performance, and validate selected models on real embedded hardware and under simulated lighting variations. Our results support the development of robust, real-time recycling assistants for resource-constrained devices. We also propose architectural deployment scenarios for smart containers, and cloud-assisted solutions. By improving waste sorting accuracy, these systems can help reduce landfill use, support citizen engagement through real-time feedback, increase material recovery, support data-informed environmental decision making, and ease operational challenges for recycling facilities caused by misclassified materials. Ultimately, this contributes to circular economy objectives and advances the broader field of environmental intelligence. Full article

Cyber threats that target Internet of Things (IoT) and edge computing environments are growing in scale and complexity, which necessitates the development of security solutions that are both robust and scalable while also protecting privacy. Edge scenarios require new intrusion detection solutions because traditional centralized intrusion detection systems (IDSs) lack in the protection of data privacy, create excessive communication overhead, and show limited contextual adaptation capabilities. This paper introduces the SecFedDNN framework, which combines federated deep learning (FDL) capabilities to protect edge–cloud environments from cyberattacks such as Distributed Denial of Service (DDoS), Denial of Service (DoS), and injection attacks. SecFedDNN performs edge-level pre-aggregation filtering through Layer-Adaptive Sparsified Model Aggregation (LASA) for anomaly detection while supporting balanced multi-class evaluation across federated clients. A Deep Neural Network (DNN) forms the main model that trains concurrently with multiple clients through the Federated Averaging (FedAvg) protocol while keeping raw data local. We utilized Google Cloud Platform (GCP) along with Google Colaboratory (Colab) to create five federated clients for simulating attacks on the TON_IoT dataset, which we balanced across selected attack types. Initial tests showed DNN outperformed Long Short-Term Memory (LSTM) and SimpleNN in centralized environments by providing higher accuracy at lower computational costs. Following federated training, the SecFedDNN framework achieved an average accuracy and precision above 84% and recall and F1-score above 82% across all clients with suitable response times for real-time deployment. The study proves that FDL can strengthen intrusion detection across distributed edge networks without compromising data privacy guarantees. Full article

To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, face challenges in controlling critical physico-chemical parameters such as temperature, humidity, and gaseous emissions. Additionally, these methods require significant labor and over 100 days to achieve compost maturity. To address these issues, we propose an intelligent, automated composting system leveraging the Internet of Things (IoT) and wireless sensor networks (WSNs). This system integrates sensors for real-time monitoring of key parameters: DS18b20 for waste temperature, HD-38 for humidity, DHT11 for ambient conditions, and MQ sensors for detecting CO₂, NH₃, and CH₄. Controlled by an ESP32 microcontroller unit (MCU), the system employs a mixer and heating elements to optimize waste degradation based on sensor feedback. Data transmission is managed using the MQTT protocol, allowing real-time monitoring via a cloud-based platform (ThingSpeak). Furthermore, the degradation process was analyzed during the first 24 h, and a recurrent neural network (RNN) algorithm was employed to predict the time required for reaching optimal compost maturity, ensuring an efficient and sustainable solution. Full article

The Global Navigation Satellite System (GNSS), Internet of Things (IoT) and cloud computing technologies enable high-precision positioning with flexible data communication, making real-time/near-real-time monitoring more economical and efficient. In this study, a multi-sensor GNSS-IoT system was developed for measuring precise water surface elevation (WSE). The system, which includes ultrasonic and accelerometer sensors, was deployed on a floating platform in Googong reservoir, Australia, over a four-month period in 2024. WSE data derived from the system were compared against independent reference measurements from the reservoir operator, achieving an accuracy of 7 mm for 6 h averaged solutions and 28 mm for epoch-by-epoch solutions. The results demonstrate the system’s potential for remote, autonomous WSE monitoring and its suitability for validating satellite Earth observation data, particularly from the Surface Water and Ocean Topography (SWOT) mission. Despite environmental challenges such as moderate gale conditions, the system maintained robust performance, with over 90% of solutions meeting quality assurance standards. This study highlights the advantages of combining the GNSS with IoT technologies and multiple sensors for cost-effective, long-term WSE monitoring in remote and dynamic environments. Future work will focus on optimizing accuracy and expanding applications to diverse aquatic settings. Full article

With the increasing urgency for digital transformation in large-scale caged layer farms, traditional methods for monitoring the environment and chicken health, which often rely on human experience, face challenges related to low efficiency and poor real-time performance. In this study, we focused on caged layer chickens and proposed an improved abnormal beak detection model based on the You Only Look Once v8 (YOLOv8) framework. Data collection was conducted using an inspection robot, enhancing automation and consistency. To address the interference caused by chicken cages, an Efficient Multi-Scale Attention (EMA) mechanism was integrated into the Spatial Pyramid Pooling-Fast (SPPF) module within the backbone network, significantly improving the model’s ability to capture fine-grained beak features. Additionally, the standard convolutional blocks in the neck of the original model were replaced with Grouped Shuffle Convolution (GSConv) modules, effectively reducing information loss during feature extraction. The model was deployed on edge computing devices for the real-time detection of abnormal beak features in layer chickens. Beyond local detection, a digital twin remote monitoring system was developed, combining three-dimensional (3D) modeling, the Internet of Things (IoT), and cloud-edge collaboration to create a dynamic, real-time mapping of physical layer farms to their virtual counterparts. This innovative approach not only improves the extraction of subtle features but also addresses occlusion challenges commonly encountered in small target detection. Experimental results demonstrate that the improved model achieved a detection accuracy of 92.7%. In terms of the comprehensive evaluation metric (mAP), it surpassed the baseline model and YOLOv5 by 2.4% and 3.2%, respectively. The digital twin system also proved stable in real-world scenarios, effectively mapping physical conditions to virtual environments. Overall, this study integrates deep learning and digital twin technology into a smart farming system, presenting a novel solution for the digital transformation of poultry farming. Full article

The widespread adoption of Internet of Things (IoT) devices has significantly increased the exposure of cloud-based architectures to cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks. Traditional detection methods often fail to efficiently identify and mitigate these threats in dynamic IoT/Cloud environments. This study proposes a machine-learning-based framework to enhance DDoS attack detection and mitigation, employing Random Forest, XGBoost, and Long Short-Term Memory (LSTM) models. Two well-established datasets, CIC-DDoS2019 and N-BaIoT, were used to train and evaluate the models, with feature selection techniques applied to optimize performance. A comparative analysis was conducted using key performance metrics, including accuracy, precision, recall, and F1-score. The results indicate that Random Forest outperforms other models, achieving a precision of 99.96% and an F1-score of 95.84%. Additionally, a web-based dashboard was developed to visualize detection outcomes, facilitating real-time monitoring. This research highlights the importance of efficient data preprocessing and feature selection for improving detection capabilities in IoT/Cloud infrastructures. Furthermore, the potential integration of metaheuristic optimization for hyperparameter tuning and feature selection is identified as a promising direction for future work. The findings contribute to the development of more resilient and adaptive cybersecurity solutions for IoT/Cloud-based environments. Full article

In recent years, a large number of Internet of Things (IoT)-based products, solutions, and services have emerged from the industry to enter the marketplace, improving the quality of service. With the wide adoption of IoT-based systems/applications in real scenarios, the privacy preservation (PP) topic has garnered significant attention from both academia and industry; as a result, many PP solutions have been developed, tailored to IoT-based systems/applications. This paper provides an in-depth analysis of state-of-the-art (SOTA) PP solutions recently developed for IoT-based systems and applications. We delve into SOTA PP methods that preserve IoT data privacy and categorize them into two scenarios: on-device and cloud computing. We categorize the existing PP solutions into privacy-by-design (PbD), such as federated learning (FL) and split learning (SL), and privacy engineering solutions (PESs), such as differential privacy (DP) and anonymization, and we map them to IoT-driven applications/systems. We further summarize the latest SOTA methods that employ multiple PP techniques like $ϵ$-DP + anonymization or $ϵ$-DP + blockchain + FL (rather than employing just one) to preserve IoT data privacy in both PES and PbD categories. Lastly, we highlight quantum-based methods devised to enhance the security and/or privacy of IoT data in real-world scenarios. We discuss the status of current research in PP techniques for IoT data within the scope established for this paper, along with opportunities for further research and development. To the best of our knowledge, this is the first work that provides comprehensive knowledge about PP topics centered on the IoT, and which can provide a solid foundation for future research. Full article