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An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Uma Rani, Sunil Kumar, Neeraj Dahiya, Kamna Solanki, Shanu Rakesh Kuttan, Sajid Shah, Momina Shaheen, Faizan Ahmad
Bitcoin exchange security is crucial because of MEC's widespread use. Cryptojacking has compromised MEC app security and bitcoin exchange ecosystem functionality. This paper propose a cutting-edge neural network and AdaHessian optimization technique for cryptojacking prediction and defense. We provide a cutting-edge deep neural network (DNN) cryptojacking attack prediction approach employing pruning
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Privacy-preserving federated learning based on partial low-quality data J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Huiyong Wang, Qi Wang, Yong Ding, Shijie Tang, Yujue Wang
Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still obtain participants’ privacy through inference attacks
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A secure data interaction method based on edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Weiwei Miao, Yuanyi Xia, Rui Zhang, Xinjian Zhao, Qianmu Li, Tao Wang, Shunmei Meng
Deep learning achieves an outstanding success in the edge scene due to the appearance of lightweight neural network. However, a number of works show that these networks are vulnerable for adversarial examples, bringing security risks. The classical adversarial detection methods are used in white-box setting and show weak performances in black-box setting, like the edge scene. Inspired by the experimental
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TCP Stratos for stratosphere based computing platforms J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-15 A. A. Periola
Stratosphere computing platforms (SCPs) benefit from free cooling but face challenges necessitating transmission control protocol (TCP) re-design. The redesign should be considered due to stratospheric gravity waves (SGWs), and sudden stratospheric warming (SSWs). SGWs, and SSWs disturb the wireless channel during SCPs packet communications. SCP packet transmission can be done using existing TCP variants
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Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-15 Zhiqing Bai, Caizhong Li, Javad Pourzamani, Xuan Yang, Dejuan Li
Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features
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SRA-E-ABCO: terminal task offloading for cloud-edge-end environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-14 Shun Jiao, Haiyan Wang, Jian Luo
The rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments
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FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-13 Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li
With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated
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Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-08 Yu Hu, Feipeng Qiu, Fei Zheng, Jilong Zhao
In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the
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Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-07 Wen Xing, Adam Slowik, J. Dinesh Peter
With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music recommendation will be a potential development direction for online music education. In this paper, we propose
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RNA-RBP interactions recognition using multi-label learning and feature attention allocation J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-07 Huirui Han, Bandeh Ali Talpur, Wei Liu, Limei Wang, Bilal Ahmed, Nadia Sarhan, Emad Mahrous Awwad
In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures
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Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-05 Guohao Tang, Huaying Zhao, Baohua Yu
Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension’s pattern and evolution within the trajectory data, thereby inadequately identifying the
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AI-empowered mobile edge computing: inducing balanced federated learning strategy over edge for balanced data and optimized computation cost J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-04 Momina Shaheen, Muhammad S. Farooq, Tariq Umer
In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing access rights, privacy, security, and the utilization of heterogeneous data sources over mobile edge computing. Edge devices generate and gather data, across the network
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Automated visual quality assessment for virtual and augmented reality based digital twins J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-26 Ben Roullier, Frank McQuade, Ashiq Anjum, Craig Bower, Lu Liu
Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods
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Detection of cotton leaf curl disease’s susceptibility scale level based on deep learning J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-26 Rubaina Nazeer, Sajid Ali, Zhihua Hu, Ghulam Jillani Ansari, Muna Al-Razgan, Emad Mahrous Awwad, Yazeed Yasin Ghadi
Cotton, a crucial cash crop in Pakistan, faces persistent threats from diseases, notably the Cotton Leaf Curl Virus (CLCuV). Detecting these diseases accurately and early is vital for effective management. This paper offers a comprehensive account of the process involved in collecting, preprocessing, and analyzing an extensive dataset of cotton leaf images. The primary aim of this dataset is to support
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Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-23 S. Gayathri, D. Surendran
Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation
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Edge intelligence-assisted animation design with large models: a survey J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-21 Jing Zhu, Chuanjiang Hu, Edris Khezri, Mohd Mustafa Mohd Ghazali
The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement in the field of computer graphics and animation. This survey aims to provide a comprehensive overview of the current state and future prospects of EI-assisted animation design, focusing on the challenges and opportunities presented by large model implementations
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Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-19 Meiyan Li, Qinyong Wang, Yuwei Liao
Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation
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Multiple objectives dynamic VM placement for application service availability in cloud networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-17 Yanal Alahmad, Anjali Agarwal
Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement
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Investigation on storage level data integrity strategies in cloud computing: classification, security obstructions, challenges and vulnerability J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-15 Paromita Goswami, Neetu Faujdar, Somen Debnath, Ajoy Kumar Khan, Ghanshyam Singh
Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup
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A secure and efficient electronic medical record data sharing scheme based on blockchain and proxy re-encryption J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-15 Guijiang Liu, Haibo Xie, Wenming Wang, Haiping Huang
With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs
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A fog-edge-enabled intrusion detection system for smart grids J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-14 Noshina Tariq, Amjad Alsirhani, Mamoona Humayun, Faeiz Alserhani, Momina Shaheen
The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant
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Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-14 Mohammad Zunnun Khan, Mohd Shoaib, Mohd Shahid Husain, Khair Ul Nisa, Mohammad. Tabrez Quasim
Cloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to
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Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-13 Junyan Chen, Wei Xiao, Hongmei Zhang, Jiacheng Zuo, Xinmei Li
Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve
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Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-12 Jing Chen, Shengyi Yang, Ting Gao, Yue Ying, Tian Li, Peng Li
The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining
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Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-09 Yue Pan, Andia Foroughi
Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact
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Predicting the individual effects of team competition on college students’ academic performance in mobile edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-09 Huiling Zhang, Huatao Wu, Zhengde Li, Wenwen Gong, Yan Yan
Mobile edge computing (MEC) has revolutionized the way of teaching in universities. It enables more interactive and immersive experiences in the classroom, enhancing student engagement and learning outcomes. As an incentive mechanism based on social identity and contest theories, team competition has been adopted and shown its effectiveness in improving students’ participation and motivation in college
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Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-08 Mamoona Humayun, Amjad Alsirhani, Faeiz Alserhani, Momina Shaheen, Ghadah Alwakid
Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET
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Harmfulness metrics in digital twins of social network rumors detection in cloud computing environment J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-08 Hao Li, Wu Yang, Wei Wang, Huanran Wang
Social network rumor harm metric is a task to score the harm caused by a rumor by analyzing the spreading range of the rumor, the users affected, the repercussions caused, etc., and then the harm caused by the rumor. Rumor hazard metric models can help rumor detection digital twins to understand and analyze user behaviors and assist social network network managers to make more informed decisions. However
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Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-07 Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam, Karam M. Sallam, Abdelaziz Foul, Ibrahim A. Hameed
The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network
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Challenges in remote sensing based climate and crop monitoring: navigating the complexities using AI J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-06 Huimin Han, Zehua Liu, Jiuhao Li, Zhixiong Zeng
The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO2 concentrations. It also interacts with other ecological stresses like
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Graph convolution networks for social media trolls detection use deep feature extraction J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-06 Muhammad Asif, Muna Al-Razgan, Yasser A. Ali, Long Yunrong
This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate
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DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-05 Qi Liu, Jiawei Sun, Yonghong Zhang, Xiaodong Liu
In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing
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A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-03 Yue Yang, Jieren Cheng, Zhaowu Liu, Huimin Li, Ganglou Xu
With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification
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Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-02 Junli Fang, Tao Feng, Xian Guo, Rong Ma, Ye Lu
Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized
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Secure semantic search using deep learning in a blockchain-assisted multi-user setting J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-30 Shahzad Khan, Haider Abbas, Muhammad Binsawad
Deep learning-based semantic search (DLSS) aims to bridge the gap between experts and non-experts in search. Experts can create precise queries due to their prior knowledge, while non-experts struggle with specific terms and concepts, making their queries less precise. Cloud infrastructure offers a practical and scalable platform for data owners to upload their data, making it accessible to intended
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An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-30 Jiaxing Guo, Dongliang Zhao, Chunxiang Gu, Xi Chen, Xieli Zhang, Mengcheng Ju
Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application
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MSFANet: multi-scale fusion attention network for mangrove remote sensing lmage segmentation using pattern recognition J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-26 Lixiang Fu, Jinbiao Chen, Zhuoying Wang, Tao Zang, Huandong Chen, Shulei Wu, Yuchen Zhao
Mangroves are ecosystems that grow in the intertidal areas of coastal zones, playing crucial ecological roles and possessing unique economic and social values. They have garnered significant attention and research interest. Semantic segmentation of mangroves is a fundamental step for further investigations. However, mangrove remote sensing images often have large dimensions, with a substantial portion
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Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-25 Gan Huang, Ihsan Ullah, Hanyao Huang, Kyung Tae Kim
Software-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things
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Improving efficiency of DNN-based relocalization module for autonomous driving with server-side computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-25 Dengbo Li, Hanning Zhang, Jieren Cheng, Bernie Liu
The substantial computational demands associated with Deep Neural Network (DNN)-based camera relocalization during the reasoning process impede their integration into autonomous vehicles. Cost and energy efficiency considerations may dissuade automotive manufacturers from employing high-computing equipment, limiting the adoption of advanced models. In response to this challenge, we present an innovative
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A new method of dynamic network security analysis based on dynamic uncertain causality graph J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-24 Chunling Dong, Yu Feng, Wenqian Shang
In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph
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Task offloading exploiting grey wolf optimization in collaborative edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-23 Nawmi Nujhat, Fahmida Haque Shanta, Sujan Sarker, Palash Roy, Md. Abdur Razzaque, Md. Mamun-Or-Rashid, Mohammad Mehedi Hassan, Giancarlo Fortino
The emergence of mobile edge computing (MEC) has brought cloud services to nearby edge servers facilitating penetration of real-time and resource-consuming applications from smart mobile devices at a high rate. The problem of task offloading from mobile devices to the edge servers has been addressed in the state-of-the-art works by introducing collaboration among the MEC servers. However, their contributions
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Cloud-SMPC: two-round multilinear maps secure multiparty computation based on LWE assumption J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-22 Yun Luo, Yuling Chen, Tao Li, Chaoyue Tan, Hui Dou
Cloud computing has data leakage from all parties, security protection of private data, and existing solutions do not provide a trade-off between security and overhead. With distributed data communication due to data barriers, information interaction security and data computation security have become challenges for secure computing. Combining cloud computing with secure multiparty computation can provide
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Blockchain-enabled supervised secure data sharing and delegation scheme in Web3.0 J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-22 Hongmin Gao, Pengfei Duan, Xiaofeng Pan, Xiaojing Zhang, Keke Ye, Ziyuan Zhong
Web3.0 represents the ongoing evolution of blockchain technology, placing a strong emphasis on establishing a decentralized and user-controlled Internet. Current data delegation solutions for Web3.0 predominantly rely on attribute-based encryption algorithms (ABE) but lack the essential capabilities for processing ciphertext. Additionally, the attribute-based ciphertext transformation algorithm (ABCT)
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Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-18 Huimin Han, Harold Neira-Molin, Asad Khan, Meie Fang, Haitham A. Mahmoud, Emad Mahrous Awwad, Bilal Ahmed, Yazeed Yasin Ghadi
In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural network (GCN) for enhanced analytical precision. The EEG-GCN
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Innovative deep learning techniques for monitoring aggressive behavior in social media posts J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-16 Huimin Han, Muhammad Asif, Emad Mahrous Awwad, Nadia Sarhan, Yazeed Yasid Ghadi, Bo Xu
The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM)
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SLA-ORECS: an SLA-oriented framework for reallocating resources in edge-cloud systems J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-15 Shizhan Lan, Zhuoxi Duan, Song Lu, Bin Tan, Shi Chen, Yeyu Liang, Shan Chen
The emergence of the Fifth Generation (5G) era has ushered in a new era of diverse business scenarios, primarily characterized by data-intensive and latency-sensitive applications. Edge computing technology integrates the information services environment with cloud computing capabilities at the edge of the network. However, the evolving landscape of business models necessitates a unified edge architecture
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An improved ACO based service composition algorithm in multi-cloud networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-15 Liu Bei, Li Wenlin, Su Xin, Xu Xibin
In recent years, with the rapid development of mobile communication networks, some new services such as cloud virtual reality, holographic communication, and etc. continue to emerge. Service composition has been researched in cloud computing. however, as the fast development of edge clouds, the service components can be deployed on the edge clouds to reduce the composition latency, so the more flexible
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Multivariate time series collaborative compression for monitoring systems in securing cloud-based digital twin J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-10 Zicong Miao, Weize Li, Xiaodong Pan
With the booming of cloud-based digital twin systems, monitoring key performance indicators has become crucial for ensuring system security and reliability. Due to the massive amount of monitoring data generated, data compression is necessary to save data transmission bandwidth and storage space. Although the existing research has proposed compression methods for multivariate time series (MTS), it
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The key security management scheme of cloud storage based on blockchain and digital twins J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-10 Jie Huang, Jiangyi Yi
As a secure distributed ledger technology, blockchain has attracted widespread attention from academia and industry for its decentralization, immutability, and traceability characteristics. This paper proposes a cloud storage key security management scheme based on blockchain. To resist brute-force attacks launched by adversaries on ciphertexts, the scheme uses an oblivious pseudo-random function (OPRF)
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Small object Lentinula Edodes logs contamination detection method based on improved YOLOv7 in edge-cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-10 Xuefei Chen, Shouxin Sun, Chao Chen, Xinlong Song, Qiulan Wu, Feng Zhang
A small object Lentinus Edodes logs contamination detection method (SRW-YOLO) based on improved YOLOv7 in edge-cloud computing environment was proposed to address the problem of the difficulty in the detection of small object contaminated areas of Lentinula Edodes logs. First, the SPD (space-to-depth)-Conv was used to reconstruct the MP module to enhance the learning of effective features of Lentinula
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An adaptive routing strategy in P2P-based Edge Cloud J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-10 Biao Dong, Jinhui Chen
P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered
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An efficient hybrid optimization of ETL process in data warehouse of cloud architecture J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-08 Lina Dinesh, K. Gayathri Devi
In big data, analysis data is collected from different sources in various formats, transforming into the aspect of cleansing the data, customization, and loading it into a Data Warehouse. Extracting data in other formats and transforming it to the required format requires transformation algorithms. This transformation stage has redundancy issues and is stored across any location in the data warehouse
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A resource competition-based truthful mechanism for IoV edge computing resource allocation with a lowest revenue limit J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-08 Jixian Zhang, Zhemin Wang, Athanasios V. Vasilakos, Weidong Li
Resource allocation in Internet of Vehicles (IoV) edge computing is currently a research hotspot. Existing studies focus on social welfare or revenue maximization. However, there is little research on lowest revenue guarantees, which is a problem of great concern to resource providers. This paper presents the innovative concept of the lowest revenue limit, which enables service providers to preset
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Towards explainability for AI-based edge wireless signal automatic modulation classification J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-08 Bo Xu, Uzair Aslam Bhatti, Hao Tang, Jialin Yan, Shulei Wu, Nadia Sarhan, Emad Mahrous Awwad, Syam M. S., Yazeed Yasin Ghadi
With the development of artificial intelligence technology and edge computing technology, deep learning-based automatic modulation classification (AI-based AMC) deployed at edge devices using centralised or distributed learning methods for optimisation has emerged in recent years, and has made great progress in the recognition accuracy and recognisable range of wireless signals. However, the lack of
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A knowledge-graph based text summarization scheme for mobile edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-06 Zheng Yu, Songyu Wu, Jielin Jiang, Dongqing Liu
As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient
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BGNBA-OCO based privacy preserving attribute based access control with data duplication for secure storage in cloud J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-05 M. Pavithra, M. Prakash, V. Vennila
Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based
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Edge-cloud computing cooperation detection of dust concentration for risk warning research J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-05 Qiao Su, Hongsu Wang, Haiyang Zhao, Yan Chu, Jie Li, Xuan Lyu, Zijuan Li
An edge-cloud computing collaborative dust concentration detection architecture is proposed for real-time operation of intelligent algorithms to reduce the warning delay. And, an end-to-end three-channel convolutional neural network (E2E-SCNN) method is proposed in the paper to facilitate intelligent monitoring and management of dust concentration in tobacco production workshops. This model, which
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Context-aware environment online monitoring for safety autonomous vehicle systems: an automata-theoretic approach J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-03 Yu Zhang, Sijie Xu, Hongyi Chen, Uzair Aslam Bhatt, Mengxing Huang
Intelligent Transport System (ITS) is a typical class of Cyber-Physical Systems (CPS), and due to the special characteristics of such systems, higher requirements are placed on system security. Runtime verification is a lightweight verification technique which is used to improve the security of such systems. However, current runtime verification methods often ignore the effects of the physical environment
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A Transformer-based network intrusion detection approach for cloud security J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-02 Zhenyue Long, Huiru Yan, Guiquan Shen, Xiaolu Zhang, Haoyang He, Long Cheng
The distributed architecture of cloud computing necessitates robust defense mechanisms to secure network-accessible resources against a diverse and dynamic threat landscape. A Network Intrusion Detection System (NIDS) is pivotal in this context, with its efficacy in cloud environments hinging on its adaptability to evolving threat vectors while mitigating false positives. In this paper, we present
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Enhancing trust transfer in supply chain finance: a blockchain-based transitive trust model J. Cloud Comp. (IF 3.418) Pub Date : 2024-01-02 Chang Shu, Yuling Chen, Chaoyue Tan, Yun Luo, Hui Dou
Artificial intelligence and blockchain technology have become indispensable in the era of the digital economy, particularly in the field of financial financing. However, when it comes to supply chain finance (SCF), existing models primarily focus on risk identification and credit evaluation, neglecting the critical aspects of trust transfer continuity and reliability within the chain. To address this