Edge Cloud Research Articles

A survey on Deep Learning in Edge–Cloud Collaboration: Model partitioning, privacy preservation, and prospects

A survey on Deep Learning in Edge–Cloud Collaboration: Model partitioning, privacy preservation, and prospects

Adaptive Scheduling Method of Heterogeneous Resources on Edge Side of Power System Collaboration Based on Cloud–Edge Security Dynamic Collaboration

Adaptive Scheduling Method of Heterogeneous Resources on Edge Side of Power System Collaboration Based on Cloud–Edge Security Dynamic Collaboration

Vehicle-to-Everything-Car Edge Cloud Management with Development, Security, and Operations Automation Framework

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.

A Novel Orchestrator Architecture for Deploying Virtualized Services in Next-Generation IoT Computing Ecosystems

The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native principles, where distributed computing nodes often have unfixed and changing networking configurations. Unlike traditional approaches, this architecture also focuses on managing services across massively distributed mobile nodes, as demonstrated in the automotive use case presented. Apart from working as MANO framework, the proposed solution efficiently handles service lifecycle management in large fleets of vehicles without relying on public or static IP addresses for connectivity. Its modular, microservices-based approach ensures adaptability to emerging trends like Edge Native, WebAssembly and RISC-V, positioning it as a forward-looking innovation for IoT ecosystems.

Exploring In-Network Computing with Information-Centric Networking: Review and Research Opportunities

The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation.

A risk assessment method for power internet of things information security based on multi‐objective hierarchical optimisation

AbstractPower Internet of Things (PIoT) relies on various smart sensors and edge devices to support the operational status of the power grid. To address the challenge of conducting a comprehensive information security risk assessment for communication among heterogeneous devices in PIoT, a reliable security risk assessment method is proposed. This method, based on the analytic hierarchy process (AHP), considers four key dimensions: the cloud platform, communication transmission, edge side and terminal side. It integrates information indexes, the asset loss and risk probability to construct a risk assessment scheme oriented towards PIoT information security. The judgement matrix of AHP which fails the consistency test, is modified by using an improved multi‐objective particle swarm optimisation (IMPSO) algorithm, while minimising semantic offset distance. Verified through a case study, this method effectively reduces the consistency index to an average of 0.01 and achieves a correlation of 90.86% with traditional AHP assessment results.

Designing a Hybrid Heuristic-aided Approach for Replica Placement and Migration Strategy for SaaS Applications in Edge Cloud

Designing a Hybrid Heuristic-aided Approach for Replica Placement and Migration Strategy for SaaS Applications in Edge Cloud

Enhancing Specific Emitter Identification: A Semi-Supervised Approach With Deep Cloud and Broad Edge Integration

Enhancing Specific Emitter Identification: A Semi-Supervised Approach With Deep Cloud and Broad Edge Integration

Privacy-Preserving Machine Learning in Cloud–Edge–End Collaborative Environments

Privacy-Preserving Machine Learning in Cloud–Edge–End Collaborative Environments

Modification of Reward Function in Reinforcement Q-learning Agent to Improve Quality of Service in Federated Edge Cloud

Modification of Reward Function in Reinforcement Q-learning Agent to Improve Quality of Service in Federated Edge Cloud

Data Deduplication and Integrity Check Scheme Based on Unique Tags in Edge Cloud Computing

Data Deduplication and Integrity Check Scheme Based on Unique Tags in Edge Cloud Computing

Hybrid Edge–Cloud Models for Bearing Failure Detection in a Fleet of Machines

Real-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datasets representative of the failures of interesting data that are challenging to acquire due to the rarity of anomalies. Conversely, unsupervised models rely on data from normal operational conditions, which is more readily available. However, these models classify all deviations from normalcy as anomalies, including those unrelated to failure, leading to costly false positives. To address these challenges, we propose a distributed system that integrates supervised and unsupervised learning. A compact unsupervised model is deployed on edge devices near the machines to compress sensor data, which are then transmitted to a centralized cloud-based system. Over time, these data are automatically labeled and used to train a supervised model, improving the accuracy of failure predictions. Our approach enables efficient, scalable failure detection across a fleet of machines while balancing the trade-offs between supervised and unsupervised learning.

Cloud–Edge Learning for Adaptive Video Streaming in B5G Internet of Things Systems

Cloud–Edge Learning for Adaptive Video Streaming in B5G Internet of Things Systems

Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis

With generative Artificial Intelligence (AI) capturing public attention, the appetite of the technology sector for larger and more complex Deep Learning (DL) models is continuously growing. Traditionally, the focus in DL model development has been on scaling the neural network’s foundational structure to increase computational complexity and enhance the representational expressiveness of the model. However, with recent advancements in edge computing and 5G networks, DL models are now aggressively being deployed and utilized across the cloud–edge–IoT continuum for the realization of in situ intelligent IoT services. This paradigm shift introduces a growing need for AI practitioners, as a focus on inference costs, including latency, computational overhead, and energy efficiency, is long overdue. This work presents a benchmarking framework designed to assess DL model scaling across three key performance axes during model inference: classification accuracy, computational overhead, and latency. The framework’s utility is demonstrated through an empirical study involving various model structures and variants, as well as publicly available datasets for three popular DL use cases covering natural language understanding, object detection, and regression analysis.

Effective Detection of Cloud Masks in Remote Sensing Images.

Effective detection of the contours of cloud masks and estimation of their distribution can be of practical help in studying weather changes and natural disasters. Existing deep learning methods are unable to extract the edges of clouds and backgrounds in a refined manner when detecting cloud masks (shadows) due to their unpredictable patterns, and they are also unable to accurately identify small targets such as thin and broken clouds. For these problems, we propose MDU-Net, a multiscale dual up-sampling segmentation network based on an encoder-decoder-decoder. The model uses an improved residual module to capture the multi-scale features of clouds more effectively. MDU-Net first extracts the feature maps using four residual modules at different scales, and then sends them to the context information full flow module for the first up-sampling. This operation refines the edges of clouds and shadows, enhancing the detection performance. Subsequently, the second up-sampling module concatenates feature map channels to fuse contextual spatial information, which effectively reduces the false detection rate of unpredictable targets hidden in cloud shadows. On a self-made cloud and cloud shadow dataset based on the Landsat8 satellite, MDU-Net achieves scores of 95.61% in PA and 84.97% in MIOU, outperforming other models in both metrics and result images. Additionally, we conduct experiments to test the model's generalization capability on the landcover.ai dataset to show that it also achieves excellent performance in the visualization results.

GPThingSim: A IoT Simulator Based GPT Models Over an Edge-Cloud Environments

The Internet of Things encapsulates a vision of a world in which billions of objects, called connected devices, are endowed with intelligence, communication capabilities, and integrated sensing and actuation capabilities. They often outsource their storage and computing needs to more powerful resources in the cloud. In Edge computing, it is the device itself that collects and processes data and must make real-time decisions. A model that combines the edge and the cloud allows for instant processing of large amounts of data, making devices increasingly intelligent. One fundamental aspect of Artificial Intelligence, particularly Machine Learning (ML), is that it requires a substantial amount of data to learn. The GPT model can be adapted to operate within a cloud environment, which enables instant processing of large quantities of data. In this article, our approach relies on a hybrid of Cloud computing and Edge computing, merging the best features of both approaches by using Edge for real-time processing and the cloud for storage and large-scale data analysis. We will explore the possibilities of integrating the GPT model into IoT devices. This extension allows devices to offer processing capabilities at the local level, which can be extremely beneficial for many applications. We propose a platform for simulating connected objects augmented by the LSTM neural network model to predict the energy consumption of IoT devices in a smart city scenario.

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

Minimization of Task Completion Time in Wireless Powered Mobile Edge–Cloud Computing Networks

A Scalable Cloud–Edge Collaborative Approach for Intelligent Low-Code Fault Diagnosis: Successful Applications of Agile Migration Deployment in Heterogeneous Fault Diagnosis Scenarios

A Scalable Cloud–Edge Collaborative Approach for Intelligent Low-Code Fault Diagnosis: Successful Applications of Agile Migration Deployment in Heterogeneous Fault Diagnosis Scenarios

A Study on Detailed Image Processing of Fractal Characteristics of Irregular Cloud Edges

With the recent development of computer graphics and image processing technology, this paper describes the progress of cloud image generation and processing techniques. Clouds in nature usually have complex and changeable shapes, so accurate and detailed processing of their images has always been an important research topic in computer graphics. First, this paper summarizes the application value of fractal theory in cloud image processing. Next, it presents the fractal generation and edge thinning algorithm as crucial technical components in forming the framework of cloud image detail processing. The fractal generation algorithm uses the fractal geometry principle to simulate the basic shape of the cloud. In contrast, the edge thinning algorithm enhances the realism of the cloud edge by adding small-scale features on the boundary.

Distributed Recommendation Systems: Survey and Research Directions

With the explosive growth of online information, recommendation systems have become essential tools for alleviating information overload. In recent years, researchers have increasingly focused on centralized recommendation systems, capitalizing on the powerful computing capabilities of cloud servers and the rich historical data they store. However, the rapid development of edge computing and mobile devices in recent years has provided new alternatives for building recommendation systems. These alternatives offer advantages such as privacy protection and low-latency recommendations. To leverage the advantages of different computing nodes, including cloud servers, edge servers, and terminal devices, researchers have proposed recommendation systems that involve the collaboration of these nodes, known as distributed recommendation systems. This survey provides a systematic review of distributed recommendation systems. Specifically, we design a taxonomy for these systems from four perspectives and comprehensively summarize each study by category. In particular, we conduct a detailed analysis of the collaboration mechanisms of distributed recommendation systems. Finally, we discuss potential future research directions in this field.