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检索条件"任意字段=6th International Conference on Distributed Computing and Internet Technology"

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GreenCloud AI: Framework for Sustainable distributed Data Management in Cloud Ecosystems 6

GreenCloud AI: Framework for Sustainable Distributed Data Ma...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kamath, Shashank K.M. Parkar, Trupti Vinayak Gogri, Dhaval Jani, Yash Jani, Arth Patil, Shalmali Amazon SeattleWA United States Microsoft RedmondWA United States Walmart FremontCA United States Docusign FremontCA United States Amazon VancouverBC Canada Amazon DallasTX United States

ISBN: (纸本)9798331522667

the rapid expansion of cloud computing and data-intensive applications has led to significant environmental concerns due to increasing energy consumption in data centers. this paper introduces the concept of "GreenCloud AI," a theoretical framework for sustainable and energy-efficient distributed data management in cloud ecosystems. the proposed conceptual model envisions the integration of artificial intelligence techniques to address the challenge of dynamic data placement optimization in heterogeneous cloud storage environments. GreenCloud AI, as a theoretical construct, proposes leveraging machine learning algorithms to predict workload patterns, analyze energy consumption profiles, and inform decision-making processes for data allocation across diverse storage tiers and geographically distributed data centers. By considering factors such as varying energy costs, carbon intensities, renewable energy availability, and regulatory compliance, the framework aims to conceptually achieve a balance between energy efficiency, carbon emission reduction, and system performance. this research contributes to the field of sustainable informatics by outlining potential approaches for energy-efficient data management in cloud ecosystems, providing a foundation for future research and development in creating more environmentally responsible cloud infrastructure. © 2025 IEEE.

关键词： distributed cloud

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Security Vulnerability Detection and Defense of Smart Home Systems Based on the internet of things 6th

Security Vulnerability Detection and Defense of Smart Home S...

引用

6th international conference on Cyber Security Intelligence and Analytics, CSIA 2024

作者： Zhao, Zhenghui Chen, Miao Chongqing Medical and Pharmaceutical College Chongqing401331 China

ISBN: (纸本)9783031882869

With the widespread application of IoT (internet of things) technology in smart homes, the security protection of systems has received more attention. In response to the problems of incomplete vulnerability detection and outdated defense mechanisms in traditional methods, this article proposed an efficient and comprehensive vulnerability detection and defense framework. By periodically scanning all terminal devices in the smart home system through edge devices, known vulnerabilities in the system were identified. through edge computing technology and AI (Artificial Intelligence) algorithm detection, lightweight distributed defense mechanisms were deployed between intelligent devices to ensure that when an attack occurred, it can quickly respond locally and reduce the system response time. the experimental results showed that all devices in the edge computing-based scheme had a high coverage rate of more than 90%. In terms of the average response time, the edge computing defense framework combined with AI algorithm was 7.2 s, far lower than the traditional defense methods. the experimental results prove that this research is efficient in improving vulnerability detection and defense response. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Response time (computer systems)

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Optimization of cloud computing task scheduling based on whale optimization algorithm with quasi-opposition-based and nonlinear factor 6

Optimization of cloud computing task scheduling based on wha...

引用

6th international conference on internet of things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Xuan, Xiong Li, Shaobo Yu, Liya Wu, Fengbin Zhang, Xingxing An, Xue College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University College of Mechanical Engineering Guiyang550025 China

ISBN: (纸本)9798350386974

Task scheduling has a significant impact on the resource availability and operation cost of the system in cloud computing. In order to improve the efficiency of task execution in the cloud for a given computational resource, the improved whale optimization algorithm is applied to the multi-objective optimization model for cloud task scheduling. Specifically, the quasi-opposition-based learning strategy is added in the initiation phase to increase the diversity of the population. Meanwhile, a novel nonlinear convergence factor was introduced to augment the local search capability. Experimental results indicate that the improved whale optimization algorithm converges faster and searches with higher precision in finding the optimal task scheduling solution compared to existing meta-heuristic algorithms. In addition, the modified algorithm achieves superior system resource usage when dealing with both small and large tasks. © 2024 IEEE.

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A DRL-Based Intelligent Task Offloading and Caching Strategy for IIoT in MEC 6

A DRL-Based Intelligent Task Offloading and Caching Strategy...

引用

6th international conference on Communications, Information System and Computer Engineering, CISCE 2024

作者： Deng, Yunfeng Peng, Anjie School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350353174

In IIoT (Industrial internet of things) systems, diverse smart devices generate various types of computational tasks, often with low latency requirements. Leveraging computation offloading techniques in MEC (Mobile Edge computing), these tasks can be sent to MEC servers for auxiliary computing. However, the dynamic changes in the environment and the increasing number of smart devices in the system lead to competition for the limited resources on the servers, making it crucial to develop rational offloading strategies. Motivated by this, to ensure efficient and stable operation of IIoT systems, we design intelligent offloading and task caching strategies for single-edge mobile networks to reduce the overall execution latency of intelligent tasks. this paper establishes a system model from three perspectives: task offloading, task caching, and task queue scheduling, and formulates a joint optimization problem for task offloading and task caching. We transform the interaction process of the system into a Markov decision process (MDP) and propose a low-latency scheduling strategy based on DRL (Deep Reinforcement Learning). At each time slot, this strategy collects dynamic environment information and reference task priority values to make offloading and caching decisions. Simulation results demonstrate that the proposed algorithm achieves rapid and stable convergence and effectively reduces the completion latency of intelligent tasks. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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Recovery Method of Missing Multivariable Time-varying Data Associated with Damaged Sensors by Graph Convolution Networks for Edge-computing based Industrial internet of things 6

Recovery Method of Missing Multivariable Time-varying Data A...

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6th international conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Wang, Guizhen Zhang, Zening Bai, Chenglin Yang, Lishan Liaocheng University School of Physics Science and Information Engineering Liaocheng China

ISBN: (纸本)9798350388374

Dealing with high-dimensional non-Euclidean data associated with the edge computing based industrial internet of things (EC-IIoT) has developed rapidly and gained considerable attention, which could capture the underlying structure via a graph and analyze the relationship of dataset by the convolution networks. In this paper, we propose a recovery method of missing multivariable time-varying data for the EC-IIoT, which estimates the true signal from an observation corrupted by missing part by graph convolution networks. the simulation result demonstrates that the proposed recovery method could reconstruct the missing sensor data that following Pearson correlation coefficient, identical distribution and linear coefficient. © 2024 IEEE.

关键词： edge computing graph convolution network Industrial internet of things missing data recovery

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Optimized Operation Model of Urban Energy internet with Multi-Category Energy Subsystems via Model Predictive Control 6

Optimized Operation Model of Urban Energy Internet with Mult...

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6th international conference on Power and Energy technology, ICPET 2024

作者： Yang, Yusong Zhang, Runfan He, Tong Bie, Zhaohong Li, Gengfeng Ren, Xinyi School of Electrical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

ISBN: (纸本)9798350375855

Upon connecting numerous renewable power sources to the power grid, the inherent high randomness and volatility in renewable energy often result in challenges such as unbalanced power supply, diminished power quality, and unstable operational statuses. these challenges significantly elevate the complexity of urban energy operation and management. Moreover, with each micro energy network system catering to different energy preferences, conventional management practices within a single energy category must evolve to address multi-category energy optimization and control. this paper proposes an optimization and control framework for the urban energy internet, encompassing multi-category energy subsystems. Firstly, it formulates an optimization model for the urban energy internet aimed at maximizing integrated value across various energy categories. Secondly, it employs a model predictive control algorithm that relies on a two-time prediction domain for continuous optimization. Validation of the proposed optimization method is conducted using the modified IEEE 30-bus system distributed micro energy network. the results demonstrate that the proposed method optimizes the micro energy network with respect to their preferences. © 2024 IEEE.

关键词： Clean energy

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Auto-Scaling Service Strategy for distributed Satellite System 6

Auto-Scaling Service Strategy for Distributed Satellite Syst...

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6th IEEE international conference on Electronics and Communication Engineering, ICECE 2023

作者： Jin, Yifeng Liu, Wei Tao, Ying Zhang, Lei Gao, Zihe Shi, Yulin China Academy of Space Technology Institute of Telecommunication and Navigation Satellites Beijing China

ISBN: (纸本)9798350328721

In this paper, we investigate a scenario where a distributed satellite system (DSS) serves as a distributed computing platform within the space-terrestrial integrated network (STIN), catering to ground users. To enhance the scalability of the distributed computing system and adapt to variations in user request traffic, we propose an auto-scaling service strategy based on traffic prediction. the prediction is implemented using the long short-term memory recurrent neural network (LSTM-RNN) method. Numerical results are presented to demonstrate the superiority of our proposed traffic prediction algorithm compared to existing approaches, while experimental findings confirm that our auto-scaling service strategy effectively enhances system performance in terms of service quality. © 2023 IEEE.

关键词： distributed computer systems

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An Object Detection System for Automatic Driving: MEC-YOLO Based on "Cloud-Edge-End" 6

An Object Detection System for Automatic Driving: MEC-YOLO B...

引用

6th international conference on Natural Language Processing (ICNLP)

作者： Liang, Siyuan Zhang, Xu Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710121 Peoples R China

ISBN: (纸本)9798350349122;9798350349115

the massive machine type of communication (mMTC) of 5G is the basis for IoT (internet of things) development, which can promote automobile industry upgrades: intelligent and green. Automatic driving (AD) is an important trend in intelligent vehicles. With the development of computer vision (CV) technology, pure vision sensor-based solutions are dominating the AD of intelligent vehicles. Due to CV hardware platform energy and cost consumption constraints, meeting the detection speed and accuracy of vehicle AD requirements have become an obvious challenge, especially for new energy vehicles. Combining the advantages of edge computing and IoT based on mMTC, this paper proposes a system called mobile edge computing-you only look once (MEC-YOLO) to solve this problem, which is an energy-efficient and low-cost instance-level vision perception system for AD. In this system, the local hardware platform uses the improved object detection (OD) network to extract features from the locally collected visual data, processes them into a feature extraction queue, uploads them to the edge network nodes to complete the OD task, and transmits the prediction results and trains weights back to the local devices. In addition, the cloud computing center resource can be invoked to supplement edge nodes when the edge computing capacity is insufficient. Furthermore, we develop an AD platform equipped with Jetson for system validation, which improved the detection speed by 93.2% and reduced the GPU resource occupation rate by 90.8% compared to YOLOv4 on the KITTI dataset. the test results verified that the MEC-YOLO proposed in this paper has the advantages of fast detection speed, high accuracy, and low resource occupation rate compared with the traditional OD scheme.

关键词： automatic driving mobile edge computing object detection MEC-YOLO

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Blockchain Interoperability through Bridges: A Token Transfer Perspective 6

Blockchain Interoperability through Bridges: A Token Transfe...

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6th international conference on Blockchain computing and Applications, BCCA 2024

作者： Olivieri, Luca Mukherjee, Aradhita Chaki, Nabendu Cortesi, Agostino Ca’ Foscari University of Venice Venice Italy University of Calcutta Kolkata India

ISBN: (纸本)9798350351538

the landscape of blockchain has grown rapidly in recent years and also the need for data exchange among different blockchain networks. In particular, blockchain bridges have emerged as a solution to create a way for cross-chain communications, improve blockchain interoperability, and transfer of tokens and economic assets. However, their development is not trivial and requires considering several critical aspects. In this paper, we overview the bridge technology focusing on one of its main purposes, i.e. token transfer. then, we investigate the most popular token transfer mechanisms and the main pitfalls and challenges that developers face when implementing bridges. © 2024 IEEE.

关键词： distributed ledger

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Exploring the Potential of Blockchain in internet of Robotic things: Advancements, Challenges, and Future Directions 6

Exploring the Potential of Blockchain in Internet of Robotic...

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6th international conference on Networking and Advanced Systems, ICNAS 2023

作者： Echikr, Abdessamed Yachir, Ali Kerrache, Chaker Abdelaziz Sahraoui, Zakaria Ecole Militaire Polytechnique Algiers Algeria Amar Telidji University Laghouat Algeria

ISBN: (纸本)9798350319170

In recent years, the concept of internet of Robotic things (IoRT) has gained significant attention, driven by the convergence of robotics, cloud computing, and diverse IoT devices and services. IoRT systems enable the autonomous exchange of data and decision-making within virtual infrastructures. However, managing and controlling diverse robotics applications that interact with IoT devices poses a major challenge in IoRT systems. the conventional centralized approach falls short in providing adequate security and privacy for the IoRT network, particularly due to the mobility of robots and the limited computational and energy resources of IoT devices. To address these limitations, this paper explores the integration of blockchain technology into IoRT networks. Blockchain, known for its distributed ledger based on peer-to-peer communication and trustworthy operations, offers a promising solution. this study provides a comprehensive overview of this integration, starting with an examination of the fundamental characteristics and challenges faced by IoRT networks. It then presents a survey of blockchain technology, highlighting its diverse applications in IoRT systems. Based on our findings, we will propose a new architecture for IoRT that utilizes Blockchain technology to address security and privacy concerns and enhance the overall performance of the network. © 2023 IEEE.

关键词： Blockchain

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