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检索条件"任意字段=17th International Conference on Distributed Computing and Internet Technology, ICDCIT 2021"

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17th international conference on distributed computing and internet technology, icdcit 2021

17th International Conference on Distributed Computing and I...

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17th international conference on distributed computing and internet technology, icdcit 2021

ISBN: (纸本)9783030656201

the proceedings contain 20 papers. the special focus in this conference is on distributed computing and internet technology. the topics include: Parameterized complexity of defensive and offensive alliances in graphs;a reconstructive model for identifying the global spread in a pandemic;cost effective method for ransomware detection: An ensemble approach;exploring Alzheimer’s disease network using social network analysis;stroke prediction using machine learning in a distributed environment;Automated diagnosis of breast cancer with roi detection using YOLO and heuristics;an efficient approach for event prediction using collaborative distance score of communities;A distributed system for optimal scale feature extraction and semantic classification of large-scale airborne LiDAR point clouds;load balancing approach for a mapreduce job running on a heterogeneous hadoop cluster;Model development in the tool use: Explorative, consolidating and analytic steps for UML and OCL models;Study the significance of ML-ELM using combined pagerank and content-based feature selection;reLink: Open information extraction by linking phrases and its applications;energy-efficient scheduling of deadline-sensitive and budget-constrained workflows in the cloud;an efficient renewable energy-based scheduling algorithm for cloud computing;a revenue-based service management algorithm for vehicular cloud computing;interference reduction in directional wireless networks;automated deadlock detection for large java libraries;DNet: An efficient privacy-preserving distributed learning framework for healthcare systems.

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distributed computing and internet technology 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Diganta Goswami Truong Anh Hoang

this book constitutes the proceedings of the 17th international conference on distributed computing and internet technology, icdcit 2020, held in Bhubaneswar, India, in January 2021.

ISBN: (数字)9783030656218

ISBN: (纸本)9783030656201

this book constitutes the proceedings of the 17th international conference on distributed computing and internet technology, icdcit 2020, held in Bhubaneswar, India, in January 2021.

关键词： Computer Communication Networks Computer System Implementation Software Engineering/Programming and Operating Systems Information Systems Applications (incl. internet)

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the Bloom Clock for Causality Testing 17th

The Bloom Clock for Causality Testing

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17th international conference on distributed computing and Intelligent technology

作者： Misra, Anshuman Kshemkalyani, Ajay D. Univ Illinois Chicago IL 60607 USA

ISBN: (纸本)9783030656201;9783030656218

Testing for causality between events in distributed executions is a fundamental problem. Vector clocks solve this problem but do not scale well. the probabilistic Bloom clock can determine causality between events with lower space, time, and message-space overhead than vector clock;however, predictions suffer from false positives. We give the protocol for the Bloom clock based on Counting Bloom filters and study its properties including the probabilities of a positive outcome and a false positive. We show the results of extensive experiments to determine how these above probabilities vary as a function of the Bloom timestamps of the two events being tested, and to determine the accuracy, precision, and false positive rate of a slice of the execution containing events in the temporal proximity of each other. Based on these experiments, we make recommendations for the setting of the Bloom clock parameters. We postulate the causality spread hypothesis from the application's perspective to indicate whether Bloom clocks will be suitable for correct predictions with high confidence. the Bloom clock design can serve as a viable space-, time-, and message-space-efficient alternative to vector clocks if false positives can be tolerated by an application.

关键词： Causality Vector clock Bloom clock Bloom filter Partial order distributed system False positive Performance

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DURATS: distributed User-centric Radio Access technology Selection framework 17

DURATS: Distributed User-centric Radio Access Technology Sel...

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17th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Mamadou, Ali Mamadou Chalhoub, Gerard Univ Clermont Auvergne LIMOS CNRS Clermont Ferrand France

ISBN: (纸本)9781665428545

Many studies have identified the numerous advantages of heterogeneous network architectures in enhancing resource efficiency and user experience. However, there are still open questions related to the decision-making process. In this paper, we present DURATS: distributed User-centric Radio Access technology Selection framework formulated as a Multiple Criteria Decision Making (MCDM) problem in the decision-making step. Realistic evaluation scenarios were conducted with a full-stack network simulator to prove the efficiency of this proposal. Our findings show the benefits of DURATS, under low and high-density network configurations, in enhancing network performance perceived by end-users while considering Quality of Service (QoS) constraints.

关键词： Heterogeneous network architectures RAT selection MCDM

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A distributed System for Optimal Scale Feature Extraction and Semantic Classification of Large-Scale Airborne LiDAR Point Clouds 17th

A Distributed System for Optimal Scale Feature Extraction an...

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17th international conference on distributed computing and Intelligent technology

作者： Singh, Satendra Sreevalsan-Nair, Jaya Int Inst Informat Technol Graph Visualizat Comp Lab 26-C Elect City Bangalore 560100 Karnataka India

ISBN: (纸本)9783030656201;9783030656218

Airborne LiDAR (Light Detection and Ranging) or aerial laser scanning (ALS) technology can capture large-scale point cloud data, which represents the topography of large regions. the raw point clouds need to be managed and processed at scale for exploration and contextual understanding of the topographical data. One of the key processing steps is feature extraction from pointwise local geometric descriptors for object-based classification. the state of the art involves finding an optimal scale for computing the descriptors, determined using descriptors across multiple scales, which becomes computationally intensive in the case of big data. Hence, we propose the use of a widely used big data analytics framework integration of Apache Spark and Cassandra, for extracting features at optimal scale, semantic classification using a random forest classifier, and interactive visualization. the visualization involves real-time updates to the selection of regions of interest, and display of feature vectors upon a change in the computation of descriptors. We show the efficacy of our proposed application through our results in the DALES aerial LiDAR point cloud.

关键词： Big data framework Apache Spark Cassandra LiDAR point cloud Multiscale feature extraction Semantic classification

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A Reference Implemenation for RPL Attacks Using Contiki-NG and COOJA 17

A Reference Implemenation for RPL Attacks Using Contiki-NG a...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Algahtani, Faya Tryfonas, theo Oikonomou, George Univ Bristol Elect & Elect Engn Bristol Avon England

ISBN: (纸本)9781665439299

RPL-based IoT networks are vulnerable to routing attacks as well as flooding attacks. Developing security countermeasures requires knowledge of possible attacks, their timing, and combinations. Most implementations of RPL related attacks only consider individual attacks triggered when their simulation starts. Furthermore, nodes which to be compromised are preselected before a simulation starts and cannot later be changed. In this paper, we present a Contiki-NG implementation of most known RPL attacks all of which is shared on a public Github repository. In addition, we designed a framework in COOJA to facilitate simulating hybrid RPL attacks with different settings in terms of duration and severity.

关键词： internet of things Contiki-NG COOJA RPL Security

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Deep Reinforcement Learning Based Resource Allocation for Heterogeneous Networks 17

Deep Reinforcement Learning Based Resource Allocation for He...

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17th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Yang, Helin Zhao, Jun Lam, Kwok-Yan Garg, Sahil Wu, Qingqing Xiong, Zehui Nanyang Technol Univ Strateg Ctr Res Privacy Preserving Technol & Syst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Univ Quebec Ecole Technol Super Elect Engn Dept Montreal PQ Canada Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China Singapore Univ Technol & Design Pillar Informat Syst Technol & Design Singapore Singapore

ISBN: (纸本)9781665428545

this paper investigates the problem of distributed resource management (i.e., joint device association, spectrum allocation, and power allocation) in two-tier heterogeneous networks without any central controller. Considering the fact that the network is highly complex with large state and action spaces, a multi-agent dueling deep-Q network-based algorithm combined with distributed coordinated learning is proposed to effectively learn the optimized intelligent resource management policy, where the algorithm adopts dueling deep network to learn the action-value distribution by estimating both the state-value and action advantage functions. Under the distributed coordinated learning manner and dueling architecture, the learning algorithm can rapidly converge to the optimized policy. Simulation results demonstrate that the proposed distributed coordinated learning algorithm outperforms other existing learning algorithms in terms of learning efficiency, network data rate, and QoS satisfaction probability.

关键词： Heterogeneous wireless networks distributed resource management dueling deep reinforcement learning

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Energy-Efficient Scheduling of Deadline-Sensitive and Budget-Constrained Workflows in the Cloud 17th

Energy-Efficient Scheduling of Deadline-Sensitive and Budget...

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17th international conference on distributed computing and Intelligent technology

作者： Tarafdar, Anurina Karmakar, Kamalesh Khatua, Sunirmal Das, Rajib K. Univ Calcutta Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9783030656201;9783030656218

Due to the rapid advancement of Cloud computing, more and more users are running their scientific and business workflow applications in the Cloud. the energy consumption of these workflows is high, which negatively affects the environment and also increases the operational costs of the Cloud providers. Moreover, most of the workflows are associated with budget constraints and deadlines prescribed by Cloud users. thus, one of the main challenges of workflow scheduling is to make it energy-efficient for Cloud providers. At the same time, it should prevent budget and deadline violations for Cloud users. To address these issues, we consider a heterogeneous Cloud environment and propose an energy-efficient scheduling algorithm for deadline-sensitive workflows with budget constraints. Our algorithm ensures that the workflow is scheduled within the budget while reducing energy consumption and deadline violation. It utilizes Dynamic Voltage and Frequency Scaling (DVFS) to adjust the voltage and frequency of the virtual machines (VMs) executing tasks of the workflow. these adjustments help to achieve significant energy savings. Extensive simulation using real-world workflows and comparison with some state-of-art approaches validate the effectiveness of our proposed algorithm.

关键词： Workflow scheduling Cloud computing Energy-efficiency Budget constraints Deadline

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Decentralized Dedicated Intrusion Detection Security Agents for IoT Networks 17

Decentralized Dedicated Intrusion Detection Security Agents ...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Ioannou, Christiana Charalambus, Andronikos Vassiliou, Vasos Univ Cyprus Dept Comp Sci Nicosia Cyprus CYENS Ctr Excellence Nicosia Cyprus

ISBN: (纸本)9781665439299

Security breaches are an imminent threat in the internet of things (IoT) as smart diversified devices are now interconnected to serve a specific application. General security guidelines may fail to prevent attacks from penetrating the network and as a result an attack may immerse in the network causing irreversible damage. Detecting the attack at an early stage can minimize the effects of the attack. Using the Support Vector Machine (SVM) supervised machine learning technique in Intrusion Detection Systems (IDS) has shown that routing layer attacks can be detected by monitoring node and network activity. the current work extends on the topic of SVM detection models, by introducing Decentralized Dedicated IDS agents placed at key positions within the network to monitor it and raise an alarm when a malicious node is within its vicinity. the detectors were trained and evaluated with three main attacks and variations of them and achieve high classification and accuracy rates.

关键词： Intrusion Detection Anomaly Detection internet of things Sniffers SVM Machine Learning

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Research on Marketing System Construction of internet Platform Based on Big Data technology 17

Research on Marketing System Construction of Internet Platfo...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Wu, Dianyi Long, Siwei Zhou, Xiaolin Zhou, Yan Commun Univ China Dept Advertising Beijing Peoples R China

ISBN: (纸本)9781728186160

With the rapid change in the media environment and the development of data technology, big data has increasingly become a core role and concept in the field of marketing operation. For the internet platform, marketing is its core profit model, and the use of data to improve marketing effectiveness is an issue that internet platforms always pay attention to. the path to obtain big data, mine the value of big data, and support marketing operations based on big data is becoming critical in the development of internet platforms. this paper takes Google as a research case, and deeply analyzes the data source, data mining technology, user labeling system of Google platform, and the marketing ecology built on this basis. through the analysis of Google, we finally came to the conclusion: for the internet platform, big data technology is an important factor in the marketing system construction, but the integration of technology and marketing ecology is more important, and the internet platform must realize the depth integration of data and marketing system ecology to effectively improve the efficiency of marketing operations.

关键词： Marketing System Big Data internet Platform Google

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