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检索条件"任意字段=13th International Conference on Distributed Computing and Internet Technology, ICDCIT 2017"

共 228 条记录，以下是31-40 订阅

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Blockchain Enabled internet of things: Current Scenario and Open Challenges for Future 13th

Blockchain Enabled Internet of Things: Current Scenario and ...

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13th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Srivastava, Sanskar Anshu Bansal, Rohit Soni, Gulshan Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India Baba Mastnath University Asthal Bohar Rohtak India Department of Management Studies Vaish College of Engineering Rohtak India Department of Computer Science and Engineering School of Engineering O.P. Jindal University Chhattisgarh Raigarh India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

ISBN: (纸本)9783031274985

the modern world and its rapid progression have cemented the requirement of digitization and has started a revolution for automation. the fore runner for this is IoT or the internet of things. On a very basic level IoT can be explained as the interconnection of smart devices. the internet of things (IoT) describes the network of physical objects "things" that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. these devices range from ordinary household objects to sophisticated industrial tools. With more than 7 billion connected IoT devices today, experts are expecting this number to grow to 10 billion by 2020 and 22 billion by 2025. IoT is quickly becoming one of the most important technologies as it allows us to connect everyday objects to the internet using embedded devices and can then be used to communicate with them. this forms a connection between the physical and digital world. But along with this huge advantage comes a lack of intrinsic security measures which make this more vulnerable and open to privacy as well as security threats. To overcome this weakness technologies such as big data and cloud computing have been tried in tandem with IoT which has provided average results. Blockchain is a system of recording data such that it becomes almost impossible to change it. It is a digital ledger of transactions that is duplicated and distributed across the entire network of computers. So, in a sense it allows us to record digitized data and distribute it but not edit it. thus, most of the issues related to security and vulnerability can be solved quite easily. By integrating these technologies, it brings availability, security, and integrity to the applications. this research paper targets readers who have not been particularly invested in these topics and would like an overview of IoT, blockchain and their applications, uses and future prospects. © 202

关键词： Blockchain

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HTTP Flood DOS Attack Detection on Big Data Using Data Mining 1

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13th international conference on Information Systems and Advanced Technologies, ICISAT 2023

作者： Haddadi, Mohamed Khiat, Abdelhamid Abidi, Yasmina Derradji, Yaakoub Department of Commercial Sciences Faculty of Economic Commercial and Management Sciences M’hamed BOUGARA University of Boumerdes Independence Avenue Boumerdes35000 Algeria Networks and Distributed Systems Division Research Center on Scientic and Technical Information Ben Aknoun Algeria Department of Computer Science Faculty of Sciences University of Ferhat Abbas Setif1 Setif Algeria

ISBN: (数字)9783031605918

ISBN: (纸本)9783031605901

Nowadays, increasing use of internet connection, security becomes a huge challenge for individuals as well as governments and organizations. therefore, in the last decade, the world is moving towards green computing in the purpose either to store energy or to decrease operational costs. So, this new technology uses web servers to provide web applications to end user. Generally, these web servers become unavailable because of HTTP flood DoS attack. this paper applied data mining techniques such as WEKA and TANAGRA tools for data preprocessing in the aim to get a consistent data in one hand, and for classifying the traffic in normal behavior or anomaly using like J48, Random tree, SMO, Naïve bayes, IBK, and combined classifier in the other hand. For this aim, a well-known big data set is used which called http_csic_2010_full dataset. Results show that the two data mining tools are well close with all the classifiers, just MLP in WEKA, and CVM in TANAGRA they didn’t perform well in terms of detection accuracy. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： HTTP

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Big Data Framework Deployment and Solution Design of Agricultural internet of things

Big Data Framework Deployment and Solution Design of Agricul...

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IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Lihui Kang Dong Xie Gang Peng Kejuan Yue Jiayue Wang Mingming Wang School of information Hunan University of Humanities Science and Technology Loudi China School of computer science Hunan First Normal University Changsha China

this paper introduces the overall design scheme of agricultural internet of thing (IoT) system and the design framework of software functional modules. By analyzing the MapReduce computing framework of Hadoop, HDFS distributed file storage system, resource allocation and invocation principle of YARN, and the operation framework of Spark. We build the overall structure of the Hadoop-based Spark computing framework and deploy the Hadoop-based Spark cluster.

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AttentionFunc: Balancing FaaS Compute across Edge-Cloud Continuum with Reinforcement Learning 13

AttentionFunc: Balancing FaaS Compute across Edge-Cloud Cont...

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13th international conference on the internet of things, IoT 2023

作者： Li, Kexin Nastic, Stefan Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China China Distributed System Group TU Wien Austria

ISBN: (纸本)9798400708541

Serverless computing is emerging as a promising paradigm to manage compute in Edge-Cloud continuum. However, distributing and balancing the computational load (serverless functions) across the continuum remains a significant challenge. In this paper, we introduce AttentionFunc-a novel framework for decentralized and efficient function offloading and computation balancing in the Edge-Cloud continuum. the AttentionFunc framework strives to introduce a fully decentralized decision-making model that accounts for the multi-objective nature of serverless workflows, the limitations of shared resources in the Edge-Cloud environment, and the dynamic behaviors such as resource contentions or cooperations among serverless functions. In addition, AttentionFunc incorporates an innovative multi-Agent offloading model based on the Markov Decision Process (MDP), designed to minimize functions' execution time and costs. the application of MDP allows the framework to efficiently address these issues using deep reinforcement learning approaches, with an aim to significantly improve function completion latency. Furthermore, AttentionFunc pioneers an attention-based optimization mechanism for multi-Agent deep reinforcement learning. this mechanism permits DRL agents to reach a consensus with minimal coordination information, leading to substantial reductions in communication and computation overhead. We evaluate AttentionFunc and compare it against select relevant state-of-the-Art approaches. Our experiments and simulations show that AttentionFunc outperforms state-of-the-Art approaches in terms of 1) the completion latency (up to 44.2% reduction), 2) the function success rate (up to 43.3% increase). Additionally, we provide the results of many experiments with different MEC scenarios to highlight the components of our approach that influence the results. We conclude that our approach reduces the low-latency challenge faced by most offloading models and improves the successful completio

关键词： Reinforcement learning

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Joint Computation and Communication Resources Allocation in Industrial internet of things with Edge computing

Joint Computation and Communication Resources Allocation in ...

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IEEE international conference on Cyber technology in Automation, Control, and Intelligent Systems

作者： Mingyue Sun Zhenghang Lian Yazhou Yuan Kai Ma Xiaoyuan Luo Institute of electrical Engineering Yanshan University Qinhuangdao China

As the scale and functional capabilities of Industrial internet of things (IIoT) deployments continue to expand, the amount of data at the device end on the field site is growing exponentially, which leads to unreliable communication quality and limited network resources. To improve the performance of the communication network, this paper proposes a cloud-edge-end three-layer communication architecture that integrates edge computing technology (ECT) with cooperative communication technology. Edge servers (ES) with computing capabilities are deployed in the factory as data forwarding relays to reduce the packet loss rate of the communication network. Under the constraints of communication resources, a bandwidth release model is established to analyze the mapping relationship between computing power and bandwidth. In addition, we adopt the Stackelberg game to characterize the interaction between the factory and the ES, and construct an optimization problem that maximizes the factory utility under the constraint of the total amount of network resources. Finally, a distributed iterative algorithm based on the backward induction method is used to achieve the joint optimization allocation of computing power and bandwidth. Simulation results demonstrate that the proposed solution can effectively allocate resources and improve the overall efficiency of the factory.

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A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents System and Blockchain 13

A Secure Integrated Fog Cloud-IoT Architecture based on Mult...

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13th international conference on Agents and Artificial Intelligence (ICAART)

作者： Gharbi, Chaima Hsairi, Lobna Zagrouba, Ezzeddine Univ Tunis Manar Higher Inst Comp Sci Lab Informat Modeling & Informat & Knowledge Proc Ariana Tunisia Univ Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9789897584848

Nowadays, the integration of Cloud computing and the internet of things (Cloud-IoT) has drawn attention as new technologies in the Future internet. Cloud-IoT accommodates good solutions to address real-world problems by offering new services in real-life scenarios. Nonetheless, the traditional Cloud-IoT will be probably not going to give suitable service to the user as it handles enormous amounts of data at a single server. Furthermore, the Cloud-IoT shows huge security and privacy problems that must be solved. To address these issues, we propose an integrated Fog Cloud-IoT architecture based on Multi-Agents System and Blockchain technology. Multi-Agents System has proven itself in decision-making aspects, distributed execution, and its effectiveness in acting in the event of an intrusion without user intervention. On the other side, we propose Blockchain technology as a distributed, public, authentic ledger to record the transactions. the Blockchain represents a great advantage to the next generation computing to ensures data integrity and to allows low latency access to large amounts of data securely. We evaluated the performance of our proposed architecture and compared it with the existing models. the result of our evaluation shows that performance is improved by reducing the response time.

关键词： Cloud-IoT Fog computing Multi-Agents System Blockchain

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Federated Learning for Sleep Stage Classification on Edge Devices via a Model-Agnostic Meta-Learning-Based Pre-Trained Model 13

Federated Learning for Sleep Stage Classification on Edge De...

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13th IEEE international conference on Consumer Electronics - Berlin, ICCE-Berlin 2023

作者： Moon, SungHwan Kim, Tae Seong Ryu, Jihye Lee, Won Hee Kyung Hee University Department of Software Convergence Yongin Korea Republic of

ISBN: (纸本)9798350324150

Sleep stage classification using physiological data obtained from consumer wearable devices has gained significant attention. However, data privacy regulations prevent the collection of individual data for training the models. Federated learning (FL) enables collaborative training across distributed networks of edge devices without sharing private data. this paper presents a federated learning approach that leverages computational resources of internet of thing (IoT) edge devices for sleep stage classification. Given the limited computational power and resources on edge devices, we evaluate the impact of model initialization in FL using pre-trained weights of model-agnostic meta-learning (MAML) models. We evaluate the performance of on-device sleep stage classification by comparing local models to different FL settings initialized with random weights (FL-Random) and pre-trained weights (FL-ML). FL-ML enables faster model convergence, yielding better classification performance (up to 22.6%) in a small number of training rounds, compared to local models. Starting from FL-ML reduces the training time required to reach a target accuracy and allows for the training of more accurate models, compared to initializing FL from random weights or using local models. Our study highlights the feasibility of FL initialized with pre-trained weights in real-world IoT settings for sleep stage classification on edge devices. © 2023 IEEE.

关键词： internet of things

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Aerial computing Offloading by distributed Deep Learning in Collaborative Satellite-terrestrial Networks 13

Aerial Computing Offloading by Distributed Deep Learning in ...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Li, Haofei Chen, Chen Li, Cong Liu, Lei Gui, Guan Xidian University State Key Laboratory of Integrated Services Networks Xi'an China State Grid JiLin Province Electric Power Company Limited Information Communication Company Changchun China Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing China

ISBN: (纸本)9781665407854

With the rapid development of the internet of things (IoT) and its derivative technologies such as Aerial IoT (AIoT), the number of smart devices and their computing requirements have exploded. It makes it difficult for the existing ground base stations to guarantee the user's computing requirements and low latency requirements. the edge computing architecture of orbiting satellites, which provides feasible solutions to the above challenges. In this paper, we design a computing offloading strategy for users to minimize the weighted sum of latency and energy consumption in the cloud-edge-ground three layers hybrid AIoT architecture. Tasks can be calculated locally, on the satellite side, or forwarded to the cloud computing center. then, the collaborative satellite-terrestrial network distributed offloading algorithm(CNDO) is proposed based on parallel neural networks to give multiple optimal offloading decisions in AIoT. Experimental results show that this scheme has a better performance compared with other comparison schemes. © 2021 IEEE.

关键词： computation offloading

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Enhanced Dynamic Load Balancing Mechanism in Cloud Environment with Modified Grey Wolf Optimization Algorithm

Enhanced Dynamic Load Balancing Mechanism in Cloud Environme...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： M. Vidhya R. Devi Department of Computer Science VISTAS Chennai India

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

Cloud is very effective technology, which works over the internet provides services such as servers, storage, networking, workstations, virtual environment etc. this technology supports the distributed environment with different servers to complete the tasks. On demand of these services, the way to balance the heavy load and allow scheduling the task plays vital role. To attain enormous performance in virtual environment, many algorithms to handle those are already developed. We can implement virtualization on operating systems to balance the work load and attain better overall performance. Based on the availability of resources, we can avail more than one container to fasten the execution completion. Incoming tasks should be schedule to all available servers in equal manner based on resource availability is called load balancing. Among multiple containers, the incoming requests are assigned to available one is called task scheduling. In this paper, Improved Grey Wolf Optimization (GWO) is implemented for load balancing and SJF is implemented for task scheduling, through which we can obtain maximum throughput with minimal makespan.

关键词： Cloud computing Schedules Heuristic algorithms Containers Load management throughput Workstations Servers Virtualization Optimization

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distributed Secure Consensus for Second-Order Multi-Agent Systems under Replay Attacks 2021

Distributed Secure Consensus for Second-Order Multi-Agent Sy...

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2021 13th international conference on Machine Learning and computing, ICMLC 2021

作者： Wang, Ling Wu, Zhihai Engineering Research Center of Internet of Things Technology Applications of Moe School of Internet of Things Engineering Jiangnan University Wuxi214122 China

ISBN: (纸本)9781450389310

this paper is concerned with secure consensus of second-order discrete-time multi-agent systems under replay attacks. State information is transmitted over a communication network and each agent may be attacked by an adversary who is able to replay the state information sent from its neighbors maliciously. A consensus protocol based on distributed model predictive control is developed in the presence of replay attacks. the sufficient conditions for multi-agent systems to achieve secure consensus under replay attacks is derived. the effectiveness of the consensus protocol based on distributed model predictive control is illustrated through numerical examples. © 2021 ACM.

关键词： Multi agent systems

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