Search Results (32)

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. 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

This research presents an IoT-empowered adaptive mobility framework that integrates high-dimensional gesture recognition with edge-cloud orchestration for safety-critical human–machine interaction. The system architecture establishes a three-tier IoT network: a perception layer with 60 GHz FMCW radar and TOF infrared arrays (12-node mesh topology, 15 cm baseline spacing) for real-time motion tracking; an edge intelligence layer deploying a time-aware neural network via NVIDIA Jetson Nano to achieve up to 99.1% recognition accuracy with latency as low as 48 ms under optimal conditions (typical performance: 97.8% ± 1.4% accuracy, 68.7 ms ± 15.3 ms latency); and a federated cloud layer enabling distributed model synchronization across 32 edge nodes via LoRaWAN-optimized protocols (κ = 0.912 consensus). A reconfigurable chassis with three operational modes (standing, seated, balance) employs IoT-driven kinematic optimization for enhanced adaptability and user safety. Using both radar and infrared sensors together reduces false detections to 0.08% even under high-vibration conditions (80 km/h), while distributed learning across multiple devices maintains consistent accuracy (variance < 5%) in different environments. Experimental results demonstrate 93% reliability improvement over HMM baselines and 3.8% accuracy gain over state-of-the-art LSTM models, while achieving 33% faster inference (48.3 ms vs. 72.1 ms). The system maintains industrial-grade safety certification with energy-efficient computation. Bridging adaptive mechanics with edge intelligence, this research pioneers a sustainable IoT-edge paradigm for smart mobility, harmonizing real-time responsiveness, ecological sustainability, and scalable deployment in complex urban ecosystems. Full article

As a core technology emerging from the convergence of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), Service Function Chaining (SFC) enables the dynamic orchestration of Virtual Network Functions (VNFs) to support diverse service requirements. However, in dynamic network environments, SFC faces significant challenges, such as resource fluctuations, user mobility, and fault recovery. To ensure service continuity and optimize resource utilization, an efficient migration mechanism is essential. This paper presents a comprehensive review of SFC migration research, analyzing it across key dimensions including migration motivations, strategy design, optimization goals, and core challenges. Existing approaches have demonstrated promising results in both passive and active migration strategies, leveraging techniques such as reinforcement learning for dynamic scheduling and digital twins for resource prediction. Nonetheless, critical issues remain—particularly regarding service interruption control, state consistency, algorithmic complexity, and security and privacy concerns. Traditional optimization algorithms often fall short in large-scale, heterogeneous networks due to limited computational efficiency and scalability. While machine learning enhances adaptability, it encounters limitations in data dependency and real-time performance. Future research should focus on deeply integrating intelligent algorithms with cross-domain collaboration technologies, developing lightweight security mechanisms, and advancing energy-efficient solutions. Moreover, coordinated innovation in both theory and practice is crucial to addressing emerging scenarios like 6G and edge computing, ultimately paving the way for a highly reliable and intelligent network service ecosystem. Full article

The explosion of connected devices and data transmission in the Internet of Things (IoT) era brings substantial burden on the capability of cloud computing. Moreover, these IoT devices are mostly positioned at the edge of a network and limited in resources. To address these challenges, edge cloud-distributed computing networks emerge. Because of the distributed nature of edge cloud networks, many research works considering software defined networks (SDNs) and network–function–virtualization (NFV) could be key enablers for managing, orchestrating, and load balancing resources. This article provides a comprehensive survey of these emerging technologies, focusing on SDN controllers, orchestration, and the function of artificial intelligence (AI) in enhancing the capabilities of controllers within the edge cloud computing networks. More specifically, we present an extensive survey on the research proposals on the integration of SDN controllers and orchestration with the edge cloud networks. We further introduce a holistic overview of SDN-enabled edge cloud networks and an inclusive summary of edge cloud use cases and their key challenges. Finally, we address some challenges and potential research directions for further exploration in this vital research area. Full article

Modern autonomous driving and intelligent transportation systems face critical challenges in managing real-time data processing, network latency, and security threats across distributed vehicular environments. Conventional cloud-centric architectures typically struggle to meet the low-latency and high-reliability requirements of vehicle-to-everything (V2X) applications, particularly in dynamic and resource-constrained edge environments. To address these challenges, this study introduces the V2X-Car Edge Cloud system, which is a cloud-native architecture driven by DevSecOps principles to ensure secure deployment, dynamic resource orchestration, and real-time monitoring across distributed edge nodes. The proposed system integrates multicluster orchestration with Kubernetes, hybrid communication protocols (C-V2X, 5G, and WAVE), and data-fusion pipelines to enhance transparency in artificial intelligence (AI)-driven decision making. A software-in-the-loop simulation environment was implemented to validate AI models, and the SmartX MultiSec framework was integrated into the proposed system to dynamically monitor network traffic flow and security. Experimental evaluations in a virtual driving environment demonstrate the ability of the proposed system to perform automated security updates, continuous performance monitoring, and dynamic resource allocation without manual intervention. Full article

This paper presents the design and development of a flexible hyper-distributed IoT–Edge–Cloud computing platform for real-time Digital Twins in real logistics and industrial environments, intended as a novel living lab and testbed for future 6G applications. It expands the limited capabilities of IoT devices with extended Cloud and Edge computing functionalities, creating an IoT–Edge–Cloud continuum platform composed of multiple stakeholder solutions, in which vertical application developers can take full advantage of the computing resources of the infrastructure. The platform is built together with a private 5G network to connect machines and sensors on a large scale. Artificial intelligence and machine learning are used to allocate computing resources for real-time services by an end-to-end intelligent orchestrator, and real-time distributed analytic tools leverage Edge computing platforms to support different types of Digital Twin applications for logistics and industry, such as immersive remote driving, with specific characteristics and features. Performance evaluations demonstrated the platform’s capability to support the high-throughput communications required for Digital Twins, achieving user-experienced rates close to the maximum theoretical values, up to 552 Mb/s for the downlink and 87.3 Mb/s for the uplink in the n78 frequency band. Moreover, the platform’s support for Digital Twins was validated via QoE assessments conducted on an immersive remote driving prototype, which demonstrated high levels of user satisfaction in key dimensions such as presence, engagement, control, sensory integration, and cognitive load. Full article

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field. Full article

Edge computing promising a vision of processing data close to its generation point, reducing latency and bandwidth usage compared with traditional cloud computing architectures, has attracted significant attention lately. The integration of edge computing in modern systems takes advantage of Internet of Things (IoT) devices and can potentially improve the systems’ performance, scalability, privacy, and security with applications in different domains. In the healthcare domain, modern IoT devices can nowadays be used to gather vital parameters and information that can be fed to edge Artificial Intelligence (AI) techniques able to offer precious insights and support to healthcare professionals. However, issues regarding data privacy and security, AI optimization, and computational offloading at the edge pose challenges to the adoption of edge AI. This paper aims to explore the current state of the art of edge AI in healthcare by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and analyzing more than 70 Web of Science articles. We have defined the relevant research questions, clear inclusion and exclusion criteria, and classified the research works in three main directions: privacy and security, AI-based optimization methods, and edge offloading techniques. The findings highlight the many advantages of integrating edge computing in a wide range of healthcare use cases requiring data privacy and security, near real-time decision-making, and efficient communication links, with the potential to transform future healthcare services and eHealth applications. However, further research is needed to enforce new security-preserving methods and for better orchestrating and coordinating the load in distributed and decentralized scenarios. Full article

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. Full article

The distributed energy power system needs to provide sufficient and flexible computing power on demand to meet the increasing digitization and intelligence requirements of the smart grid. However, the current distribution of the computing power and loads in the energy system is unbalanced, with data center loads continuously increasing, while there is a large amount of idle computing power at the edge. Meanwhile, there are a large number of real-time computing tasks in the distributed energy power system, which have strict requirements on execution deadlines and require reasonable scheduling of multi-level heterogeneous computing power to meet real-time computing demands. Based on the aforementioned background and issues, this paper studies the real-time service scheduling problem in a multi-level heterogeneous computing network of distributed energy power systems. Specifically, we consider the divisibility of tasks in the model. This paper presents a hierarchical real-time task-scheduling framework specifically designed for distributed energy power systems. The framework utilizes an orchestrating agent (OA) as the execution environment for the scheduling module. Building on this, we propose a hierarchical selection algorithm for choosing the appropriate network layer for real-time tasks. Further, we develop two scheduling algorithms based on greedy strategy and genetic algorithm, respectively, to effectively schedule tasks. Experiments show that the proposed algorithms have a superior success rate in scheduling compared to other current algorithms. Full article

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands of latency-sensitive and data-intensive applications. This research paper presents a comprehensive study on the intelligent orchestration of 5G edge computing infrastructures. The proposed Smart 5G Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates a ONEedge5G experimental component, which offers intelligent workload forecasting and infrastructure orchestration and automation capabilities, for optimal allocation of virtual resources across diverse edge locations. The research evaluated different forecasting models, based both on traditional statistical techniques and machine learning techniques, comparing their accuracy in CPU usage prediction for a dataset of virtual machines (VMs). Additionally, an integer linear programming formulation was proposed to solve the optimization problem of mapping VMs to physical servers in distributed edge infrastructure. Different optimization criteria such as minimizing server usage, load balancing, and reducing latency violations were considered, along with mapping constraints. Comprehensive tests and experiments were conducted to evaluate the efficacy of the proposed architecture. Full article

The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results. Full article

Federated learning (FL) is emerging as a powerful paradigm for distributed data mining in the context of Internet of Things (IoT) big data. It addresses privacy concerns associated with data outsourcing by enabling local data training and knowledge (i.e., model) sharing. However, simplistic local knowledge sharing can inadvertently expose user privacy to advanced attacks, such as model inversion or gradient leakage. Furthermore, achieving fine-grained and personalized privacy protection for IoT users remains a challenge. In this paper, we propose a novel solution called hierarchical blockchain-empowered cloud-edge orchestrated federated learning (HBCE-FL) to address these challenges. HBCE-FL is designed to provide secure, intelligent, and distributed data analysis for IoT users. To tackle FL’s privacy issues, we develop a multi-level access control encryption and blockchain-based approach for sharing IoT knowledge within the HBCE-FL framework. Our approach classifies IoT users into different levels based on their individual privacy requirements, enabling fine-grained privacy protection. The blockchain is employed for identity authentication, key management, and message sanitization. For scenarios involving IoT users with non-IID data, we integrate federated multi-task learning into HBCE-FL to ensure fairness, robustness, and privacy. Finally, we conduct experiments using classic MNIST and CIFAR10 datasets to validate our approach. The experimental results illustrate that HBCE-FL effectively achieves personalized privacy-preserving FL without losing IoT data availability. Regardless of whether IoT data are homogeneous or heterogeneous, our approach enhances model accuracy and convergence rates by enabling secure IoT knowledge access and sharing for IoT users. Full article