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Improving Performance of Smart Education Systems by Integrating Machine Learning on Edge Devices and Cloud in Educational Institutions J. Grid Comput. (IF 5.5) Pub Date : 2024-03-14 Shujie Qiu
Educational institutions today are embracing technology to enhance education quality through intelligent systems. This study introduces an innovative strategy to boost the performance of such procedures by seamlessly integrating machine learning on edge devices and cloud infrastructure. The proposed framework harnesses the capabilities of a Hybrid 1D Convolutional Neural Network (CNN) and Long Short-Term
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Cost-efficient Workflow as a Service using Containers J. Grid Comput. (IF 5.5) Pub Date : 2024-03-11 Kamalesh Karmakar, Anurina Tarafdar, Rajib K. Das, Sunirmal Khatua
Workflows are special applications used to solve complex scientific problems. The emerging Workflow as a Service (WaaS) model provides scientists with an effective way of deploying their workflow applications in Cloud environments. The WaaS model can execute multiple workflows in a multi-tenant Cloud environment. Scheduling the tasks of the workflows in the WaaS model has several challenges. The scheduling
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Adaptive Scheduling Framework of Streaming Applications based on Resource Demand Prediction with Hybrid Algorithms J. Grid Comput. (IF 5.5) Pub Date : 2024-03-09 Hongjian Li, Wei Luo, Wenbin Xie, Huaqing Ye, Xiaolin Duan
Spark Streaming is currently one of the mainstream stream processing frameworks which process real-time stream data by using micro-batch approach. However, there are some issues with its default task scheduling process, such as the high cost of cluster usage due to inappropriate executor placement strategy in heterogeneous cluster environments. Meanwhile, most of the current scheduling studies focus
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Multi-Agent Systems for Collaborative Inference Based on Deep Policy Q-Inference Network J. Grid Comput. (IF 5.5) Pub Date : 2024-02-29 Shangshang Wang, Yuqin Jing, Kezhu Wang, Xue Wang
This study tackles the problem of increasing efficiency and scalability in deep neural network (DNN) systems by employing collaborative inference, an approach that is gaining popularity because to its ability to maximize computational resources. It involves splitting a pre-trained DNN model into two parts and running them separately on user equipment (UE) and edge servers. This approach is advantageous
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Dueling Double Deep Q Network Strategy in MEC for Smart Internet of Vehicles Edge Computing Networks J. Grid Comput. (IF 5.5) Pub Date : 2024-02-29 Haotian Pang, Zhanwei Wang
Advancing in communication systems requires nearby devices to act as networks when devices are not in use. Such technology is mobile edge computing, which provides enormous communication services in the network. In this research, we explore a multiuser smart Internet of Vehicles (IoV) network with mobile edge computing (MEC) assistance, where the first edge server can assist in completing the intense
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Work Scheduling in Cloud Network Based on Deep Q-LSTM Models for Efficient Resource Utilization J. Grid Comput. (IF 5.5) Pub Date : 2024-02-28 Yanli Xing
Edge computing has emerged as an innovative paradigm, bringing cloud service resources closer to mobile consumers at the network's edge. This proximity enables efficient processing of computationally demanding and time-sensitive tasks. However, the dynamic nature of the edge network, characterized by a high density of devices, diverse mobile usage patterns, a wide range of applications, and sporadic
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Dynamic Multi-Resource Fair Allocation with Elastic Demands J. Grid Comput. (IF 5.5) Pub Date : 2024-02-27 Hao Guo, Weidong Li
In this paper, we study dynamic multi-resource maximin share fair allocation based on the elastic demands of users in a cloud computing system. In this problem, users do not stay in the computing system all the time. Users are assigned resources only if they stay in the system. To further improve the utilization of resources, the model in this paper allows users to dynamically select the method of
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Joint Task Offloading Based on Distributed Deep Reinforcement Learning-Based Genetic Optimization Algorithm for Internet of Vehicles J. Grid Comput. (IF 5.5) Pub Date : 2024-02-26 Hulin Jin, Yong-Guk Kim, Zhiran Jin, Chunyang Fan, Yonglong Xu
The growing number of individual vehicles and intelligent transportation systems have accelerated the development of Internet of Vehicles (IoV) technologies. The Internet of Vehicles (IoV) refers to a highly interactive network containing data regarding places, speeds, routes, and other aspects of vehicles. Task offloading was implemented to solve the issue that the current task scheduling models and
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Decentralized AI-Based Task Distribution on Blockchain for Cloud Industrial Internet of Things J. Grid Comput. (IF 5.5) Pub Date : 2024-02-24 Amir Javadpour, Arun Kumar Sangaiah, Weizhe Zhang, Ankit Vidyarthi, HamidReza Ahmadi
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Employing RNN and Petri Nets to Secure Edge Computing Threats in Smart Cities J. Grid Comput. (IF 5.5) Pub Date : 2024-02-22
Abstract The Industrial Internet of Things (IIoT) revolution has led to the development a potential system that enhances communication among a city's assets. This system relies on wireless connections to numerous limited gadgets deployed throughout the urban landscape. However, technology has exposed these networks to various harmful assaults, cyberattacks, and potential hacker threats, jeopardizing
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A Probabilistic Deadline-aware Application Offloading in a Multi-Queueing Fog System: A Max Entropy Framework J. Grid Comput. (IF 5.5) Pub Date : 2024-02-22
Abstract Cloud computing and its derivatives, such as fog and edge computing, have propelled the IoT era, integrating AI and deep learning for process automation. Despite transformative growth in healthcare, education, and automation domains, challenges persist, particularly in addressing the impact of multi-hopping public networks on data upload time, affecting response time, failure rates, and security
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Edge Computing Empowered Smart Healthcare: Monitoring and Diagnosis with Deep Learning Methods J. Grid Comput. (IF 5.5) Pub Date : 2024-02-21
Abstract Nowadays, data syncing before switchover and migration are two of the most pressing issues confronting cloud-based architecture. The requirement for a centrally managed IoT-based infrastructure has limited scalability due to security problems with cloud computing. The fundamental factor is that health systems, such as health monitoring, etc., demand computational operations on large amounts
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Dynamic Resource Management in MEC Powered by Edge Intelligence for Smart City Internet of Things J. Grid Comput. (IF 5.5) Pub Date : 2024-02-13 Xucheng Wan
The Internet of Things (IoT) has become an infrastructure that makes smart cities possible. is both accurate and efficient. The intelligent production industry 4.0 period has made mobile edge computing (MEC) essential. Computationally demanding tasks can be delegated from the MEC server to the central cloud servers for processing in a smart city. This paper develops the integrated optimization framework
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Joint Task Offloading and Multi-Task Offloading Based on NOMA Enhanced Internet of Vehicles in Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2024-02-12 Jie Zhao, Ahmed M. El-Sherbeeny
With the rapid development of technology, the Internet of vehicles (IoV) has become increasingly important. However, as the number of vehicles on highways increases, ensuring reliable communication between them has become a significant challenge. To address this issue, this paper proposes a novel approach that combines Non-Orthogonal Multiple Access (NOMA) with a time-optimized multitask offloading
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Dependent Task Scheduling Using Parallel Deep Neural Networks in Mobile Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2024-02-12 Sheng Chai, Jimmy Huang
Conventional detection techniques aimed at intelligent devices rely primarily on deep learning algorithms, which, despite their high precision, are hindered by significant computer power and energy requirements. This work proposes a novel solution to these constraints using mobile edge computing (MEC). We present the Dependent Task-Offloading technique (DTOS), a deep reinforcement learning-based technique
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An IoT-based Covid-19 Healthcare Monitoring and Prediction Using Deep Learning Methods J. Grid Comput. (IF 5.5) Pub Date : 2024-02-09 Jianjia Liu, Xin Yang, Tiannan Liao, Yong Hang
The Internet of Things (IoT) is developing a more significant transformation in the healthcare industry by improving patient care with reduced cost of treatments. Main aim of this research is to monitor the Covid-19 patients and report the health issues immediately using IoT. Collected data is analyzed using deep learning model. The technological advancement of sensor and mobile technologies came up
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Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network J. Grid Comput. (IF 5.5) Pub Date : 2024-02-08 Mengqi Wang, Jiayuan Mao, Wei Zhao, Xinya Han, Mengya Li, Chuanjun Liao, Haomiao Sun, Kexin Wang
Smart cities cannot function without autonomous devices that connect wirelessly and enable cellular connectivity and processing. Edge computing bridges mobile devices and the cloud, giving mobile devices access to computing, memory, and communication capabilities via vehicular ad hoc networks (VANET). VANET is a time-constrained technology that can handle requests from vehicles in a shorter amount
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Marine Goal Optimizer Tuned Deep BiLSTM-Based Self-Configuring Intrusion Detection in Cloud J. Grid Comput. (IF 5.5) Pub Date : 2024-02-05 Sanchika Abhay Bajpai, Archana B. Patankar
A Self-configuring intrusion detection system (IDS) present in the cloud monitors the suspicious activities affecting the user’s system and data by intruding on the stored resources. The traditional IDS environments analyze the available information for malicious detection, which makes clear that the manual analysis and attempts result in system failure. Thus, an automatic attack detection framework
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A Combined Approach of PUF and Physiological Data for Mutual Authentication and Key Agreement in WMSN J. Grid Comput. (IF 5.5) Pub Date : 2024-02-02 Shanvendra Rai, Rituparna Paul, Subhasish Banerjee, Preetisudha Meher, Gulab Sah
Wireless Medical Sensor Network (WMSN) is a kind of Ad-hoc Network that is used in the health sector to continuously monitor patients’ health conditions and provide instant medical services, over a distance. This network facilitates the transmission of real-time patient data, sensed by resource-constrained biosensors, to the end user through an open communication channel. Thus, any modification or
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E-Commerce Logistics and Supply Chain Network Optimization for Cross-Border J. Grid Comput. (IF 5.5) Pub Date : 2024-02-02 Wenxia Ye
E-commerce is a growing industry that primarily relies on websites to provide services and products to businesses and customers. As a brand-new international trade, cross-border e-commerce offers numerous benefits, including increased accessibility. Even though cross-border e-commerce has a bright future, managing the global supply chain is crucial to surviving the competitive pressure and growing
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A Federated Deep Reinforcement Learning-based Low-power Caching Strategy for Cloud-edge Collaboration J. Grid Comput. (IF 5.5) Pub Date : 2024-01-29 Xinyu Zhang, Zhigang Hu, Yang Liang, Hui Xiao, Aikun Xu, Meiguang Zheng, Chuan Sun
In the era of ubiquitous network devices, an exponential increase in content requests from user equipment (UE) calls for optimized caching strategies within a cloud-edge integration. This approach is critical to handling large numbers of requests. To enhance caching efficiency, federated deep reinforcement learning (FDRL) is widely used to adjust caching policies. Nonetheless, for improved adaptability
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Automated Pallet Racking Examination in Edge Platform Based on MobileNetV2: Towards Smart Manufacturing J. Grid Comput. (IF 5.5) Pub Date : 2024-01-27
Abstract Pallet racking is a critical element of the production, storage, and distribution networks businesses worldwide use. Ongoing inspections and maintenance are required to ensure the workforce's safety and the stock's protection. Currently, certified inspectors manually examine racks, which causes operational delays, service charges, and missing damages because of human error. As businesses move
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Hybridized Black Widow-Honey Badger Optimization: Swarm Intelligence Strategy for Node Localization Scheme in WSN J. Grid Comput. (IF 5.5) Pub Date : 2024-01-26 K Johny Elma, Praveena Rachel Kamala S, Saraswathi T
The evolutionary growth of Wireless Sensor Networks (WSN) exploits a wide range of applications. To deploy the WSN in a larger area, for sensing the environment, the accurate location of the node is a prerequisite. Owing to these traits, the WSN has been effectively implemented with devices. Using various localization techniques, the information related to node location is obtained for unknown nodes
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DRL-based Task and Computational Offloading for Internet of Vehicles in Decentralized Computing J. Grid Comput. (IF 5.5) Pub Date : 2024-01-25 Ziyang Zhang, Keyu Gu, Zijie Xu
This paper focuses on the problem of computation offloading in a high-mobility Internet of Vehicles (IoVs) environment. The goal is to address the challenges related to latency, energy consumption, and payment cost requirements. The approach considers both moving and parked vehicles as fog nodes, which can assist in offloading computational tasks. However, as the number of vehicles increases, the action
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Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters J. Grid Comput. (IF 5.5) Pub Date : 2024-01-23
Abstract Autoscaling enables container cluster orchestrators to automatically adjust computational resources, such as containers and Virtual Machines (VMs), to handle fluctuating workloads effectively. This adaptation can involve modifying the amount of resources (horizontal scaling) or adjusting their computational capacity (vertical scaling). The motivation for our work stems from the limitations
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On-Chain and Off-Chain Data Management for Blockchain-Internet of Things: A Multi-Agent Deep Reinforcement Learning Approach J. Grid Comput. (IF 5.5) Pub Date : 2024-01-20 Y. P. Tsang, C. K. M. Lee, Kening Zhang, C. H. Wu, W. H. Ip
The emergence of blockchain technology has seen applications increasingly hybridise cloud storage and distributed ledger technology in the Internet of Things (IoT) and cyber-physical systems, complicating data management in decentralised applications (DApps). Because it is inefficient for blockchain technology to handle large amounts of data, effective on-chain and off-chain data management in peer-to-peer
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Intrusion Detection using Federated Attention Neural Network for Edge Enabled Internet of Things J. Grid Comput. (IF 5.5) Pub Date : 2024-01-20 Xiedong Song, Qinmin Ma
Edge nodes, which are expected to grow into a multi-billion-dollar market, are essential for detection against a variety of cyber threats on Internet-of-Things endpoints. Adopting the current network intrusion detection system with deep learning models (DLM) based on FedACNN is constrained by the resource limitations of this network equipment layer. We solve this issue by creating a unique, lightweight
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3D Lidar Target Detection Method at the Edge for the Cloud Continuum J. Grid Comput. (IF 5.5) Pub Date : 2024-01-19 Xuemei Li, Xuelian Liu, Da Xie, Chong Chen
In the internet of things, machine learning at the edge of cloud continuum is developing rapidly, providing more convenient services for design developers. The paper proposes a lidar target detection method based on scene density-awareness network for cloud continuum. The density-awareness network architecture is designed, and the context column feature network is proposed. The BEV density attention
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AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2024-01-19 Avishek Sinha, Samayveer Singh, Harsh K. Verma
In recent times, edge computing has arisen as a highly promising paradigm aimed at facilitating resource-intensive Internet of Things (IoT) applications by offering low-latency services. However, the constrained computational capabilities of the IoT nodes present considerable obstacles when it comes to efficient task-scheduling applications. In this paper, a nature-inspired coati optimization-based
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Optimizing Accounting Informatization through Simultaneous Multi-Tasking across Edge and Cloud Devices using Hybrid Machine Learning Models J. Grid Comput. (IF 5.5) Pub Date : 2024-01-18 Xiaofeng Yang
Accounting informatization is a crucial component of enterprise informatization, significantly impacting operational efficiency in accounting and finance. Advances in information technology have introduced automation techniques that accelerate the processing of accounting information cost-effectively. Integrating artificial intelligence, cloud computing, and edge computing is pivotal in streamlining
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CMSV: a New Cloud Multi-Agents for Self-Driving Vehicles as a Services J. Grid Comput. (IF 5.5) Pub Date : 2024-01-13 Aida A. Nasr
The development of autonomous vehicles has changed the way of transmitting goods to users, with the potential to improve road safety and efficiency. One of the effective issues of self-driving vehicles is path planning issue. In this paper, a new system is proposed for addressing the potential of using multi-agent systems (MAS) to address path planning problems in autonomous vehicles. The system is
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Hybrid Fuzzy Neural Network for Joint Task Offloading in the Internet of Vehicles J. Grid Comput. (IF 5.5) Pub Date : 2024-01-09 Bingtao Liu
The Internet of Vehicles (IoV) technology is progressively maturing because of the growth of private cars and the establishment of intelligent transportation systems. The development of smart cars has, therefore, been followed by a parallel rise in the volume of media and video games in the automobile and a massive increase in the need for processing resources. Smart cars cannot process the enormous
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A Cloud-Edge-Based Multi-Objective Task Scheduling Approach for Smart Manufacturing Lines J. Grid Comput. (IF 5.5) Pub Date : 2024-01-08 Huayi Yin, Xindong Huang, Erzhong Cao
The number of task demands created by smart terminals is rising dramatically because of the increasing usage of industrial Internet technologies in intelligent production lines. Speed of response is vital when dealing with such large activities. The current work needs to work with the task scheduling flow of smart manufacturing lines. The proposed method addresses the limitations of the current approach
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Parking Cooperation-Based Mobile Edge Computing Using Task Offloading Strategy J. Grid Comput. (IF 5.5) Pub Date : 2024-01-08 XuanWen, Hai Meng Sun
The surge in computing demands of onboard devices in vehicles has necessitated the adoption of mobile edge computing (MEC) to cater to their computational and storage needs. This paper presents a task offloading strategy for mobile edge computing based on collaborative roadside parking cooperation, leveraging idle computing resources in roadside vehicles. The proposed method establishes resource sharing
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Accounting Information Systems and Strategic Performance: The Interplay of Digital Technology and Edge Computing Devices J. Grid Comput. (IF 5.5) Pub Date : 2023-12-29 Xi Zhen, Li Zhen
With the rapid development of digital technologies, scholars and industries are pushing into the information age, where data processing is the accounting industry's major challenge. This study aimed to analyze the use of these digital technologies for strategic performance attainment and mediating the accounting information system (AIS). Further, this study also explores the moderation of the DT and
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Novel Transformation Deep Learning Model for Electrocardiogram Classification and Arrhythmia Detection using Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2023-12-30 Yibo Han, Pu Han, Bo Yuan, Zheng Zhang, Lu Liu, John Panneerselvam
The diagnosis of the cardiovascular disease relies heavily on the automated classification of electrocardiograms (ECG) for arrhythmia monitoring, which is often performed using machine learning (ML) algorithms. However, current ML algorithms are typically deployed using cloud-based inferences, which may not meet the reliability and security requirements for ECG monitoring. A newer solution, edge inference
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Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms J. Grid Comput. (IF 5.5) Pub Date : 2023-12-29 Katerina Mitropoulou, Panagiotis Kokkinos, Polyzois Soumplis, Emmanouel Varvarigos
The orchestration of cloud computing infrastructures is challenging, considering the number, heterogeneity and dynamicity of the involved resources, along with the highly distributed nature of the applications that use them for computation and storage. Evidently, the volume of relevant monitoring data can be significant, and the ability to collect, analyze, and act on this data in real time is critical
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Development of Analytical Offloading for Innovative Internet of Vehicles Based on Mobile Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2023-12-28 Ming Zhang
The current task offloading technique needs to be performed more effectively. Onboard terminals cannot execute efficient computation due to the explosive expansion of data flow, the quick increase in vehicle population, and the growing scarcity of spectrum resources. As a result, this study suggests a task-offloading technique based on reinforcement learning computing for the Internet of Vehicles edge
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Edge Computing with Fog-cloud for Heart Data Processing using Particle Swarm Optimized Deep Learning Technique J. Grid Comput. (IF 5.5) Pub Date : 2023-12-23 Sheng Chai, Lantian Guo
Chronic illnesses such as heart disease, diabetes, cancer, and respiratory diseases are complex and pose a significant threat to global health. Processing heart data is particularly challenging due to the variability of symptoms. However, advancements in smart wearable devices, computing technologies, and IoT solutions have made heart data processing easier. This proposed model integrates Edge-Fog-Cloud
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DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies J. Grid Comput. (IF 5.5) Pub Date : 2023-12-19 Yu Song, Xin He, Xiwang Tang, Bo Yin, Jie Du, Jiali Liu, Zhongbao Zhao, Shigang Geng
Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide
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Markovian with Federated Deep Recurrent Neural Network for Edge—IoMT to Improve Healthcare in Smart Cities J. Grid Comput. (IF 5.5) Pub Date : 2023-12-19 Yuliang Gai, Yuxin Liu, Minghao Li, Shengcheng Yang
The architectural design of smart cities should prioritize the provision of critical medical services. This involves establishing improved connectivity and leveraging supercomputing capabilities to enhance the quality of services (QoS) offered to residents. Edge computing is vital in healthcare applications by enabling low network latencies necessary for real-time data processing. By implementing edge
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Healthcare and Fitness Services: A Comprehensive Assessment of Blockchain, IoT, and Edge Computing in Smart Cities J. Grid Comput. (IF 5.5) Pub Date : 2023-12-15 Yang-Yang Liu, Ying Zhang, Yue Wu, Man Feng
Edge computing, blockchain technology, and the Internet of Things have all been identified as key enablers of innovative city initiatives. A comprehensive examination of the research found that IoT, blockchain, and edge computing are now major factors in how efficiently smart cities provide healthcare. IoT has been determined to be the most used of the three technologies. In this observation, edge
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Integration of a Lightweight Customized 2D CNN Model to an Edge Computing System for Real-Time Multiple Gesture Recognition J. Grid Comput. (IF 5.5) Pub Date : 2023-12-15 Hulin Jin, Zhiran Jin, Yong-Guk Kim, Chunyang Fan
Abstract The human-machine interface (HMI) collects electrophysiology signals incoming from the patient and utilizes them to operate the device. However, most applications are currently in the testing phase and are typically unavailable to everyone. Developing wearable HMI devices that are intelligent and more comfortable has been a focus of study in recent times. This work developed a portable, eight-channel
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Cost-Availability Aware Scaling: Towards Optimal Scaling of Cloud Services J. Grid Comput. (IF 5.5) Pub Date : 2023-12-07 Andre Bento, Filipe Araujo, Raul Barbosa
Cloud services have become increasingly popular for developing large-scale applications due to the abundance of resources they offer. The scalability and accessibility of these resources have made it easier for organizations of all sizes to develop and implement sophisticated and demanding applications to meet demand instantly. As monetary fees are involved in the use of the cloud, one of the challenges
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Deep Learning-Based Multi-Domain Framework for End-to-End Services in 5G Networks J. Grid Comput. (IF 5.5) Pub Date : 2023-12-04 Yanjia Tian, Yan Dong, Xiang Feng
Over the past few years, network slicing has emerged as a pivotal component within the realm of 5G technology. It plays a critical role in effectively delineating network services based on a myriad of performance and operational requirements, all of which draw from a shared pool of common resources. The core objective of 5G technology is to facilitate simultaneous network slicing, thereby enabling
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Deep Reinforcement Learning and Markov Decision Problem for Task Offloading in Mobile Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2023-12-04 Xiaohu Gao, Mei Choo Ang, Sara A. Althubiti
Mobile Edge Computing (MEC) offers cloud-like capabilities to mobile users, making it an up-and-coming method for advancing the Internet of Things (IoT). However, current approaches are limited by various factors such as network latency, bandwidth, energy consumption, task characteristics, and edge server overload. To address these limitations, this research propose a novel approach that integrates
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Smart Financial Investor’s Risk Prediction System Using Mobile Edge Computing J. Grid Comput. (IF 5.5) Pub Date : 2023-12-04 Caijun Cheng, Huazhen Huang
The financial system has reached its pinnacle because of economic and social growth, which has propelled the financial sector into another era. Public and corporate financial investment operations have significantly risen in this climate, and they now play a significant part in and impact the efficient use of market money. This finance sector will be affected by high-risk occurrences because of the
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A Bibliometric Analysis of Convergence of Artificial Intelligence and Blockchain for Edge of Things J. Grid Comput. (IF 5.5) Pub Date : 2023-12-04 Deepak Sharma, Rajeev Kumar, Ki-Hyun Jung
The convergence of Artificial Intelligence (AI) and Blockchain technologies has emerged as a powerful paradigm to address the challenges of data management, security, and privacy in the Edge of Things (EoTs) environment. This bibliometric analysis aims to explore the research landscape and trends surrounding the topic of convergence of AI and Blockchain for EoTs to gain insights into its development
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Efficient Prediction of Makespan Matrix Workflow Scheduling Algorithm for Heterogeneous Cloud Environments J. Grid Comput. (IF 5.5) Pub Date : 2023-11-28 Longxin Zhang, Minghui Ai, Runti Tan, Junfeng Man, Xiaojun Deng, Keqin Li
Leveraging a cloud computing environment for executing workflow applications offers high flexibility and strong scalability, thereby significantly improving resource utilization. Current scholarly discussions heavily focus on effectively reducing the scheduling length (makespan) of parallel task sets and improving the efficiency of large workflow applications in cloud computing environments. Effectively
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AI and Blockchain Assisted Framework for Offloading and Resource Allocation in Fog Computing J. Grid Comput. (IF 5.5) Pub Date : 2023-11-27 Mohammad Aknan, Maheshwari Prasad Singh, Rajeev Arya
The role of Internet of Things (IoT) applications has increased tremendously in several areas like healthcare, agriculture, academia, industries, transportation, smart cities, etc. to make human life better. The number of IoT devices is increasing exponentially, and generating huge amounts of data that IoT nodes cannot handle. The centralized cloud architecture can process this enormous IoT data but
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An Auto-Scaling Approach for Microservices in Cloud Computing Environments J. Grid Comput. (IF 5.5) Pub Date : 2023-11-27 Matineh ZargarAzad, Mehrdad Ashtiani
Recently, microservices have become a commonly-used architectural pattern for building cloud-native applications. Cloud computing provides flexibility for service providers, allowing them to remove or add resources depending on the workload of their web applications. If the resources allocated to the service are not aligned with its requirements, instances of failure or delayed response will increase
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Sustainable Environmental Design Using Green IOT with Hybrid Deep Learning and Building Algorithm for Smart City J. Grid Comput. (IF 5.5) Pub Date : 2023-11-27 Yuting Zhong, Zesheng Qin, Abdulmajeed Alqhatani, Ahmed Sayed M. Metwally, Ashit Kumar Dutta, Joel J. P. C. Rodrigues
Smart cities and urbanization use enormous IoT devices to transfer data for analysis and information processing. These IoT can relate to billions of devices and transfer essential data from their surroundings. There is a massive need for energy because of the tremendous data exchange between billions of gadgets. Green IoT aims to make the environment a better place while lowering the power usage of
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Hybrid Immune Whale Differential Evolution Optimization (HIWDEO) Based Computation Offloading in MEC for IoT J. Grid Comput. (IF 5.5) Pub Date : 2023-11-21 Jizhou Li, Qi Wang, Shuai Hu, Ling Li
The adoption of User Equipment (UE) is on the rise, driven by advancements in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), the Internet of Things (IoT), and Artificial Intelligence (AI). Among these, MEC stands out as a pivotal aspect of the 5G network. A critical challenge within the realm of MEC is task offloading. This involves optimizing conflicting factors like execution time, energy
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Secured SDN Based Task Scheduling in Edge Computing for Smart City Health Monitoring Operation Management System J. Grid Comput. (IF 5.5) Pub Date : 2023-11-22 Shuangshuang Zhang, Yue Tang, Dinghui Wang, Noorliza Karia, Chenguang Wang
Health monitoring systems (HMS) with wearable IoT devices are constantly being developed and improved. But most of these gadgets have limited energy and processing power due to resource constraints. Mobile edge computing (MEC) must be used to analyze the HMS information to decrease bandwidth usage and increase reaction times for applications that depend on latency and require intense computation. To
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Preservation of Sensitive Data Using Multi-Level Blockchain-based Secured Framework for Edge Network Devices J. Grid Comput. (IF 5.5) Pub Date : 2023-11-17 Charu Awasthi, Prashant Kumar Mishra, Pawan Kumar Pal, Surbhi Bhatia Khan, Ambuj Kumar Agarwal, Thippa Reddy Gadekallu, Areej A. Malibari
The proliferation of IoT devices has influenced end users in several aspects. Yottabytes (YB) of information are being produced in the IoT environs because of the ever-increasing utilization capacity of the Internet. Since sensitive information, as well as privacy problems, always seem to be an unsolved problem, even with best-in-class in-formation governance standards, it is difficult to bolster defensive
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A Novel Approach to Cloud Resource Management: Hybrid Machine Learning and Task Scheduling J. Grid Comput. (IF 5.5) Pub Date : 2023-11-13 Hong Zhou
Cloud enterprises are currently facing difficulties managing the enormous amount of data and varied resources in the cloud because of the explosive expansion of the cloud computing system with numerous clients, ranging from small business owners to large corporations. Cloud computing’s performance may need more effective resource planning. Resources must be distributed equally among all relevant stakeholders
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MapReduce with Deep Learning Framework for Student Health Monitoring System using IoT Technology for Big Data J. Grid Comput. (IF 5.5) Pub Date : 2023-11-10 Md. Mobin Akhtar, Abdallah Saleh Ali Shatat, Mukhtar Al-Hashimi, Abu Sarwar Zamani, Mohammed Rizwanullah, Sara Saadeldeen Ibrahim Mohamed, Rashid Ayub
The efficient well-being and health interventions of students are ensured by better knowledge of student’s health and fitness factors. Effective Health Monitoring (HM) systems are introduced by using the Internet of Things (IoT) technology and efficient medical services are given by using the personalized health care systems. The sensors used in the IoT may create large amounts of data, which poses
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Novel Edge Computing-Based Privacy-Preserving Approach for Smart Healthcare Systems in the Internet of Medical Things J. Grid Comput. (IF 5.5) Pub Date : 2023-11-10 Lingbin Meng, Daofeng Li
This paper presents the Edge-Based Privacy Preserving Approach (EBPPA) for healthcare applications that store private user data on cloud servers, and perform computation operations for patient diagnoses. The increasing cyber-attacks on hospital systems and the exposure of mathematical operations on cloud-stored data to untrusted entities pose significant data privacy and security risks. To address
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H-Storm: A Hybrid CPU-FPGA Architecture to Accelerate Apache Storm J. Grid Comput. (IF 5.5) Pub Date : 2023-11-07 Hamid Nasiri, Armin Darjani, Nima Kavand, Maziar Goudarzi
The era of big data has led to the exponential growth of the amount of real-time data. Nowadays, traditional centralized solutions and parallelism techniques in distributed systems cannot satisfy the processing requirements of emerging applications. To overcome this inability, distributed stream processing (DSP) frameworks have emerged to utilize parallelism techniques and facilitate large-scale real-time
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Modeling Distributed and Configurable Hierarchical Blockchain over SDN and Fog-Based Networks for Large-Scale Internet of Things J. Grid Comput. (IF 5.5) Pub Date : 2023-11-03 Salman Azeez Syed, Deepak Kumar Sharma, Gautam Srivastava
With an emphasis on the role of automation in Industry 4.0, there has been widespread growth in the adoption of industrial Internet of Things (IoT). However, as IoT networks become larger with billions of heterogeneous devices, scalability and efficient data routing become issues, especially in the context of existing blockchain models. Managing network traffic, latency as well as facilitating high