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检索条件"任意字段=IEEE International Conference on Distributed Computing in Sensor Systems"

共 30820 条记录，以下是4421-4430 订阅

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Cost-optimal V2X Service Placement in distributed Cloud/Edge Environment 16

Cost-optimal V2X Service Placement in Distributed Cloud/Edge...

引用

16th international conference on Wireless and Mobile computing, Networking and Communications (ieee WiMob)

作者： Moubayed, Abdallah Shami, Abdallah Heidari, Parisa Larabi, Adel Brunner, Richard Western Univ London ON Canada Ericsson Montreal PQ Canada Edge Grav Ericsson Montreal PQ Canada

ISBN: (纸本)9781728197227

Deploying V2X services has become a challenging task. This is mainly due to the fact that such services have strict latency requirements. To meet these requirements, one potential solution is adopting mobile edge computing (MEC). However, this presents new challenges including how to find a cost efficient placement that meets other requirements such as latency. In this work, the problem of cost-optimal V2X service placement (CO-VSP) in a distributed cloud/edge environment is formulated. Additionally, a cost-focused delay-aware V2X service placement (DA-VSP) heuristic algorithm is proposed. Simulation results show that both CO-VSP model and DA-VSP algorithm guarantee the QoS requirements of all such services and illustrates the tradeoff between latency and deployment cost.

关键词： Multi-Access Edge computing (MEC) Cloud computing Intelligent Transportation systems (ITS) V2X Services V2X Service Placement

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Modeling and Analysis of Malware Propagation for Cluster-based Wireless sensor Networks 6

Modeling and Analysis of Malware Propagation for Cluster-bas...

引用

6th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Application, DependSys 2020

作者： Zhu, Xuejin Huang, Jie Qi, Chunyang Wang, Xiaowen Southeast University Purple Mountain Laboratories Department of Cyber Science and Engineering Nanjing China

ISBN: (纸本)9781728176512

Due to the limited resources of wireless sensor network nodes, they possess generally weak defense capabilities and are often the target of malware attacks. Attackers can capture or infect specific sensor nodes and propagation malware to other sensor nodes in WSNs through node communication. This can eventually infect an entire network system and even cause paralysis. Based on epidemiological theory, we propose a malware propagation model suitable for cluster-based WSNs to analyze the propagation dynamic of malware. The model focuses on the data transmission characteristics between different nodes in a cluster-based network and considers the actual application parameters of WSNs such as nodes communication radius, nodes distributed density and nodes death rate. In particular, we derive the basic reproductive number, equilibrium point and its stability of the model. We found that a basic reproductive number of less than 1 leads to the eventual disappearance of malware, which provides a deep insight into the design of defense strategies against such threats. We conducted numerical experiments to validate our theory and also examined the influence of WSN parameters on malware propagation. © 2020 ieee.

关键词： sensor nodes

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Message from the IoTI4 2021 Workshop Chairs

Proceedings - 17th Annual International Conference on Distri...

引用

Proceedings - 17th Annual international conference on distributed computing in sensor systems, DCOS 2021 2021年 xix页

作者： Lagkas, Thomas Sarigiannidis, Panagiotis Soldatos, John International Hellenic University Greece University of Western Macedonia Greece

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NoiseSenseDNN: Modeling DNN for sensor Data to Mitigate the Effect of Noise in Edge Devices 17

NoiseSenseDNN: Modeling DNN for Sensor Data to Mitigate the ...

引用

17th ieee international conference on Mobile Ad Hoc and Smart systems (ieee MASS)

作者： Sen, Tanmoy Shen, Haiying Normansell, Matthew Univ Virginia Dept Comp Sci Charlottesville VA 22908 USA

ISBN: (纸本)9781728198668

Edge computing usage in many applications, such as transportation and healthcare, has been becoming popular nowadays. These applications often use deep learning (DL) prediction, which are highly dependent on time-series data collected by the sensors in the edge devices. However, the presence of noise in the on-device sensors negatively affects the sensing output of the DL models. Recently proposed time-series based DL approaches (e.g., SADeepSense) address this issue with the assumption that in the presence of noise, the correlation of sensor inputs in an edge device changes. In this paper, through real experiments, we notice that this assumption may not hold true in the presence of shot noise. To handle this problem, in order to further improve the prediction accuracy, we propose a DL model, namely NoiseSenseDNN, which more accurately extracts the correlation between different sensor inputs over time in the presence of both shot and white noise due to its unique architecture. We further propose a compressed version of NoiseSenseDNN that minimizes the inference time and consumed energy of the edge device while meeting the accuracy requirement. Our experiments on a workstation and a real edge device and three real traces show that NoiseSenseDNN outperforms SADeepSense in accuracy, and the compressed NoiseSenseDNN significantly reduces inference time and energy consumption while meeting the required accuracy.

关键词： Energy utilization

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FedHybrid: Hierarchical Hybrid Training for High-Performance Federated Learning

FedHybrid: Hierarchical Hybrid Training for High-Performance...

引用

ieee Communications Society conference on sensor and Ad Hoc Communications and Networks (SECON)

作者： Tao Chang Li Li Meihan Wu Wei Yu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China The 30th Research Institute of China Electronics Technology Group Corporation China

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same *** this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

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Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

Non-Cooperative Game for Edge Server Placement in Wireless B...

引用

ieee international conference on Big Data and Cloud computing (BdCloud)

作者： Xiaolong Xu Yiming Fei Xiaokang Zhou Xiaoyong Li Yong Cheng Lianyong Qi Wanchun Dou School of Computer Science Nanjing University of Information Science and Technology Nanjing China Faculty of Data Science Shiga University Tokyo Japan College of Meteorology and Oceanography National University of Defense Technology Changsha China College of Computer Science and Technology China University of Petroleum (East China) China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay.

关键词： Wireless communication COVID-19 Wireless sensor networks Computer viruses Games Body area networks sensors

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A CVAE-GAN-based Approach to Process Imbalanced Datasets for Intrusion Detection in Marine Meteorological sensor Networks

A CVAE-GAN-based Approach to Process Imbalanced Datasets for...

引用

ieee international conference on Big Data and Cloud computing (BdCloud)

作者： Xin Su Tian Tian Lei Cai Baoliu Ye Hongyan Xing College of IOT Engineering Hohai University Changzhou Jiangsu China Information Department Hohai University Nanjing Jiangsu China Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Nanjing Jiangsu China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and Technology Nanjing Jiangsu China

In marine meteorological sensor networks (MMSN), there are massive data flows transmitted within numerous nodes, resulting in serious potential consequences once any anomalous traffic implied launches an attack. Therefore, accurate identification and fast response to abnormal traffic is vital for intrusion detection system (IDS) constructions. Dataset imbalances cause classification models to erroneously bias to normal traffic, significantly restricting IDS developments and applications. This paper proposes an approach to deal with dataset imbalances in intrusion detections. This approach mitigates dataset imbalance impacts on IDSs from the data perspective, which is liable to process the input data in classification models. In this approach, CVAE-GAN is adopted as the data generation module to synthesize specified minority class samples, thus reducing dataset imbalance rate. ordering points to identify the clustering structure (OPTICS) is taken as the denoising algorithm to remove outliers and decrease the overlap extent between majority classes. An experiment on NSL-KDD dataset demonstrates that the proposed method obtains a high-quality dataset with reasonable distribution. This approach improves the classifier's identification ability for potential anomalous traffic.

关键词： Training Measurement Noise reduction Intrusion detection Clustering algorithms Optics Data models

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Modeling of a system for intelligent animal husbandry 7

Modeling of a system for intelligent animal husbandry

引用

7th international conference on Big Data, Knowledge and Control systems Engineering, BdKCSE 2021

作者： Valchev, Evgeni Malinov, Pencho Glushkova, Todorka Nikolov, Vasil Doukovska, Lyubka Monov, Vladimir Plovdiv University 'Paisii Hilendarski' Faculty of Mathematics and Informatics Plovdiv Bulgaria Agricultural University Faculty of Agronomy Department of Animal Sciences Plovdiv Bulgaria Bulgarian Academy of Sciences Institute of Information and Communication Technologies Sofia Bulgaria

ISBN: (纸本)9781665410427

Modern realities and the rapid development of digital technologies in the era of Industry 4.0 bring to the fore the task of developing intelligent systems for precision agriculture. Grazing livestock has proven its importance in obtaining biologically clean food products in an ecologically clean environment. The report examines the possibilities for developing an intelligent environment for grazing cows, presents the main directions of work on the implementation of such a system and shares the results obtained so far from the application of the developed prototype. © 2021 ieee.

关键词： sensor networks

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Hathi: An MCDM-based Approach to Capacity Planning for Cloud-hosted DBMS 13

Hathi: An MCDM-based Approach to Capacity Planning for Cloud...

引用

13th ieee/ACM international conference on Utility and Cloud computing (UCC)

作者： Domaschka, Jorg Volpert, Simon Seybold, Daniel Ulm Univ Inst Informat Resource Management Ulm Germany

ISBN: (纸本)9780738123943

The evolution of distributed Database Management systems (DBMSs) has led to heterogeneity in DBMS technologies. Particularly DBMSs applying a shared-nothing approach enable distributed operation and support fine-grained configurations of distribution characteristics such as replication degree and consistency. Overall, the operation of such DBMSs on IaaS clouds leads to a large configuration space involving different cloud providers, cloud resources and pricing models. The selection of a specific configuration impacts nonfunctional features such as performance, availability, consistency, but also costs of the deployment. In consequence, these need to be traded-off against each other and a suitable configuration needs to be found, satisfying technical and operational aspects. Yet, due to the strong interdependency between different non-functional features as well as the large number of DBMSs, configuration options, and cloud providers, a manual analysis and comparison is not possible. In this paper, we present Hathi, an evaluation-driven Multi Criteria Decision Making (MCDM) framework for planning of cloud-hosted distributed DBMS. By specifying DBMS configurations, workloads, and cloud offers, Hathi automatically performs experiments and evaluates their results. These are then matched against a list of user-defined preferences using an MCDM algorithm. Our evaluation shows that Hathi is able of performing largescale evaluation scenarios involving multiple DBMS in various cluster sizes, cloud providers, and cloud offers. Hathi can weight the resulting data and derives deployment recommendations with respect to throughput, latency, cost, consistency, availability, and stability.

关键词： NoSQL cloud distributed DBMS decision making performance

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Arktos: A Hyperscale Cloud Infrastructure for Building distributed Cloud

Arktos: A Hyperscale Cloud Infrastructure for Building Distr...

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ieee international conference on Utility and Cloud computing (UCC)

作者： Ying Huang Yunwen Bai FengE Li Xiaoning Ding Qian Chen Deepak Vij Peng Du Ying Xiong Futurewei Technologies Inc. Santa Clara California USA Bytedance Inc. Seattle Washington USA

Scalability and management cost in cloud computing are few of the top challenges for the cloud providers and large enterprises. In this paper, we present Arktos, a cloud infrastructure platform for managing large-scale compute clusters and running millions of application instances as containers and/or virtual machines (VM). Arktos is envisioned as a stepping-stone from current “ single-region” focused cloud infrastructure towards next generation distributed infrastructure in the public and/or private cloud environments. We present details related to the Arktos system architecture and features, important design decisions, and the results and analysis of the performance benchmark testing. Arktos achieves high scalability by partitioning its architecture into two independent components, the resource partition (RP) and the tenant workload partition (TP), with each component scaling independently. Our performance testing using a benchmark tool demonstrates that Arktos with just two RPs and two TPs system setting can already manage a cluster of 50K compute nodes and is able to run 1.5 million workload containers with 5 times system throughput (QPS) 1 compared with an existing container management system. Three key characteristics differentiate Arktos from other open source cloud platforms such as OpenStack and Kubernetes. Firstly, Arktos architecture is a truly scalable architecture that supports a very large cluster by scaling to more RPs and TPs in the system, Secondly, it unifies the runtime infrastructure to run and manage both VM and container applications natively, therefore eliminating the cost of managing separate technology stacks for VMs and containers. Lastly, Arktos has a unique “ virtual cluster” style multi-tenancy design that provides both strong tenancy isolation, including network isolation and transparent resource view.

关键词： Cloud computing Costs Runtime Scalability systems architecture Computer architecture Containers

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