Search Results (490)

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

Integrated photonics is a transformative technology for enhancing communication and computation in Cloud and Fog computing networks. Photonic integrated circuits (PICs) enable significant improvements in data-processing speed, energy-efficiency, scalability, and latency. In Cloud infrastructures, PICs support high-speed optical interconnects, energy-efficient switching, and compact wavelength division multiplexing (WDM), addressing growing data demands. Fog computing, with its edge-focused processing and analytics, benefits from the compactness and low latency of integrated photonics for real-time signal processing, sensing, and secure data transmission near IoT devices. PICs also facilitate the low-loss, high-speed modulation, transmission, and detection of RF signals in scalable Radio-over-Fiber (RoF) links, enabling seamless IoT integration with Cloud and Fog networks. This results in centralized processing, reduced latency, and efficient bandwidth use across distributed infrastructures. Overall, integrating photonic technologies into RoF, Fog and Cloud computing networks paves the way for ultra-efficient, flexible, and scalable next-generation network architectures capable of supporting diverse real-time and high-bandwidth applications. This paper provides a comprehensive review of the current state and emerging trends in integrated photonics for IoT sensors, RoF, Fog and Cloud computing systems. It also outlines open research opportunities in photonic devices and system-level integration, aimed at advancing performance, energy-efficiency, and scalability in next-generation distributed computing networks. Full article

Fog computing technology grants computing and storage resources to nearby IoT devices, enabling a fast response and ensuring data locality. Thus, fog-enabled IoT environments provide real-time and convenient services to users in healthcare, agriculture, and road traffic monitoring. However, messages are exchanged on public channels, which can be targeted to various security attacks. Hence, secure authentication protocols are critical for reliable fog-enabled IoT services. In 2024, Harbi et al. proposed a remote user authentication protocol for fog-enabled IoT environments. They claimed that their protocol can resist various security attacks and ensure session key secrecy. Unfortunately, we have identified several vulnerabilities in their protocol, including to insider, denial of service (DoS), and stolen verifier attacks. We also prove that their protocol does not ensure user untraceability and that it has an authentication problem. To address the security problems of their protocol, we propose a security-enhanced blockchain-based secure authentication protocol for fog-enabled IoT environments. We demonstrate the security robustness of the proposed protocol via informal and formal analyses, including Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the proposed protocol with related protocols to demonstrate the excellence of the proposed protocol in terms of efficiency and security. Finally, we conduct simulations using NS-3 to verify its real-world applicability. Full article

Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation. Full article

This paper explores the broad area of Smart City services and how the evolving Cloud-Edge-IoT continuum can support application deployment in Smart Cities. We initially introduce a range of Smart City services and highlight their computational needs. We then discuss the role of the Cloud-Edge-IoT continuum as a technological platform to meet those needs. To validate this approach, we present the COGNIFOG platform, a Cloud-Edge-IoT platform developed to support city-centric use cases, and an initial technology trial that shows the early benefits of using the platform. We conclude with plans for improvements to COGNIFOG based on the trials and with a broader set of observations on the future of the Cloud-Edge-IoT continuum in Smart City services and applications. 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 rapid growth of information and communication technologies, in particular big data, artificial intelligence (AI), and the Internet of Things (IoT), has made it possible to make smart cities a tangible reality. In this context, real-time video surveillance plays a crucial role in improving public safety. This article presents a systematic review of studies focused on the detection of acts of aggression and crime in these cities. By studying 100 indexed scientific articles, dating from 2018 to 2024, we examine the most recent methods and techniques, with an emphasis on the use of machine learning and deep learning for the processing of real-time video streams. The works examined cover several technological axes such as convolutional neural networks (CNNs), fog computing, and integrated IoT systems while also addressing issues such as the challenges related to the detection of anomalies, frequently affected by their contextual and uncertain nature. Finally, this article offers suggestions to guide future research, with the aim of improving the accuracy and efficiency of intelligent monitoring systems. Full article

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development. Full article

In the evolving landscape of online gaming, ensuring a high quality of experience (QoE) for players is paramount. This study introduces a dynamic, low-latency load balancing model designed to enhance QoE in massively multiplayer online (MMO) games through a hybrid fog and edge computing architecture. The model addresses the challenges of latency and load distribution by leveraging fog and edge resources to optimize player engagement and response times. The experiments conducted in this study were simulations, providing a controlled environment to evaluate the proposed model’s performance. Key findings demonstrate a significant 67.5% reduction in average latency, a 60.3% reduction in peak latency, and a 65.8% reduction in latency variability, ensuring a more consistent and immersive gaming experience. Additionally, the proposed model was benchmarked against a base model, based on the article titled “A Cloud Gaming Architecture Leveraging Fog for Dynamic Load Balancing in Cluster-Based MMOs”, highlighting its superior performance in load distribution and latency reduction. This research provides a framework for future developments in cloud-based gaming infrastructure, emphasizing the importance of innovative load balancing techniques in maintaining seamless gameplay and scalable systems. Full article

Fog computing has emerged as a promising paradigm to extend cloud services toward the edge of the network, enabling low-latency processing and real-time responsiveness for Internet of Things (IoT) applications. However, the distributed, heterogeneous, and resource-constrained nature of fog environments introduces significant challenges in balancing workloads efficiently. This study presents a systematic literature review (SLR) of 113 peer-reviewed articles published between 2020 and 2024, aiming to provide a comprehensive overview of load-balancing strategies in fog computing. This review categorizes fog computing architectures, load-balancing algorithms, scheduling and offloading techniques, fault-tolerance mechanisms, security models, and evaluation metrics. The analysis reveals that three-layer (IoT–Fog–Cloud) architectures remain predominant, with dynamic clustering and virtualization commonly employed to enhance adaptability. Heuristic and hybrid load-balancing approaches are most widely adopted due to their scalability and flexibility. Evaluation frequently centers on latency, energy consumption, and resource utilization, while simulation is primarily conducted using tools such as iFogSim and YAFS. Despite considerable progress, key challenges persist, including workload diversity, security enforcement, and real-time decision-making under dynamic conditions. Emerging trends highlight the growing use of artificial intelligence, software-defined networking, and blockchain to support intelligent, secure, and autonomous load balancing. This review synthesizes current research directions, identifies critical gaps, and offers recommendations for designing efficient and resilient fog-based load-balancing systems. Full article

Fog computing presents a significant paradigm for extending the computational capabilities of resource-constrained devices executing increasingly complex applications. However, effectively leveraging this potential critically depends on the implementation of efficient task offloading mechanisms to proximal fog nodes, particularly under conditions of high resource contention. To address this challenge, we introduce MAFCPTORA (multi-agent fully cooperative partial task offloading and resource allocation), a decentralized multi-agent deep reinforcement learning algorithm for cooperative task offloading and resource allocation. We evaluated the performance of MAFCPTORA and compared it against recent approaches. MAFCPTORA demonstrated superior performance compared to recent methods, achieving a significantly higher average reward (0.36 ± 0.01), substantially lower average latency (0.08 ± 0.01), and reduced energy consumption (0.76 ± 0.14). Full article

This work presents a high-speed hybrid communication system integrating Underwater Optical Wireless Communication (UOWC), Multimode Fiber (MMF), and Free-Space Optics (FSO) channels, leveraging Orbital Angular Momentum (OAM) beams for enhanced data transmission. A Photodetector, Remodulate, and Forward Relay (PRFR) is employed to enable wavelength conversion from 532 nm for UOWC to 1550 nm for MMF and FSO links. Four distinct OAM beams, each supporting a 5 Gbps data rate, are utilized to evaluate the system’s performance under two scenarios. The first scenario investigates the effects of absorption and scattering in five water types on underwater transmission range, while maintaining fixed MMF length and FSO link. The second scenario examines varying FSO propagation distances under different fog conditions, with a consistent underwater link length. Results demonstrate that water and atmospheric attenuation significantly impact transmission range and received optical power. The proposed hybrid system ensures reliable data transmission with a maximum overall transmission distance of 1125 m (comprising a 25 m UOWC link in Pure Sea (PS) water, a 100 m MMF span, and a 1000 m FSO range in clear weather) in the first scenario. In the second scenario, under Light Fog (LF) conditions, the system achieves a longer reach of up to 2020 m (20 m UOWC link + 100 m MMF span + 1900 m FSO range), maintaining a BER ≤ 10⁻⁴ and a Q-factor around 4. This hybrid design is well suited for applications such as oceanographic research, offshore monitoring, and the Internet of Underwater Things (IoUT), enabling efficient data transfer between underwater nodes and surface stations. Full article

In the context of evolving Smart Cities, the integration of drone technology and distributed computing paradigms presents significant potential for enhancing urban infrastructure and services. This paper proposes a comprehensive approach to optimizing urban delivery logistics through a cloud-based model that employs Ant Colony Optimization (ACO) for planning and Model Predictive Control (MPC) for trajectory tracking within a broader Computing Continuum framework. The proposed system addresses the Capacitated Vehicle Routing Problem (CVRP) by considering both drone capacity constraints and autonomy, using the ACO-based algorithm to efficiently assign delivery destinations while minimizing travel distances. Collision-free paths are computed by using a Visibility Graph (VG) based approach, and MPC controllers enable drones to adapt to dynamic obstacles in real time. Additionally, this work explores how clusters of drones can be deployed as edge devices within the Computing Continuum, seamlessly integrating with IoT sensors and fog computing infrastructure to support various urban applications, such as traffic management, crowd monitoring, and infrastructure inspections. This dual-architecture approach, combining the optimization capabilities of ACO with the flexible, distributed nature of the Computing Continuum, allows for scalable and efficient urban drone deployment. Simulation results validate the effectiveness of the proposed model in enhancing delivery efficiency and collision avoidance while demonstrating the potential of integrating drone technology into Smart City environments for improved data collection and real-time response. Full article

Fog Computing extends Cloud computing capabilities by providing computational resources closer to end users. Fog Computing has gained considerable popularity in various domains such as drones, autonomous vehicles, and smart cities. In this context, the careful selection of suitable Fog resources and the optimal assignment of services to these resources (the service placement problem (SPP)) is essential. Numerous studies have attempted to tackle this issue. However, to the best of our knowledge, none of the previously proposed works took into consideration the dynamic context awareness and the user preferences for IoT service placement. To deal with this issue, we propose a hybrid recommendation system for service placement that combines two techniques: collaborative filtering and content-based recommendation. By considering user and service context, user preferences, service needs, and resource availability, the proposed recommendation system provides optimal placement suggestions for each IoT service. To assess the efficiency of the proposed system, a validation scenario based on Internet of Drones (IoD) was simulated and tested. The results show that the proposed approach leads to a considerable reduction in waiting time and a substantial improvement in resource utilization and the number of executed services. Full article

Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and flexibility. As the demands of Internet of Things (IoT) devices keep varying, it is important to rapidly modify the resource allocation policies to satisfy them. Constant fluctuation of the demands leads to over or under provisioning of resources. The computing capability of the fog nodes is small, and hence there is a necessity to develop resource provisioning policies that reduce the delay and bandwidth consumption. In this paper, a novel large language model (LLM)-guided Q-learning framework is designed and developed. The uncertainty in the fog environment in terms of delay incurred, bandwidth usage, and heterogeneity of fog nodes is represented using the LLM model. The reward shaping of a Q-learning agent is enriched by considering the heuristic value of the LLM model. The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. The results are further validated through the expected value analysis statistical methodology. Full article