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

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Comparative Analysis of the Efficiency of Modern Fake Detection Algorithms in Scope of Information Warfare

Comparative Analysis of the Efficiency of Modern Fake Detect...

引用

ieee international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and Applications

作者： Yevhenii Shtefaniuk Ivan Opirskyy Lviv Polytechnic National University Lviv Lviv Oblast

ISBN: (纸本)9781665426060

The problem of detecting fake (false) information passing through various channels of the Internet is becoming increasingly important task. One of the most dangerous types of such information is targeted propaganda, which always has a specific purpose and uses specially prepared resources. To protect a person from such informational influence, we can use already developed tools and algorithms. This article considers the main features of information propaganda and approaches to combating it; the effectiveness of several well-known modern algorithms for recognizing fake news; the comparative efficiency analysis of these algorithms in the context of possibility of their application for counteracting purposeful information influences is carried out. Based on the study, the most promising algorithm for recognizing information propaganda in social networks was selected. This will allow to build more efficient systems of detecting fake news that are delivered in scope of information propaganda campaigns as a part of information warfare in social media networks.

关键词： Training Social networking (online) distributed databases Manuals Feature extraction Internet Reliability

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Event-Based distributed Estimation of Stochastic systems over sensor Networks with Deception Attacks*

Event-Based Distributed Estimation of Stochastic Systems ove...

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Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Zifan Wang Yifan Liu Yougao Fan Harbin Institute of Technology Harbin P. R. China Institute of Spacecraft System Engineering Beijing China Beijing Institute of Spacecraft Environment Engineering Beijing China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

This paper addresses H ∞ state estimation problem for a class of discrete-time stochastic delayed systems subject to deception attacks over sensor networks. We consider the scenario where the sensor output and estimator output are transmitted to its corresponding estimator and other estimators, respectively, over two different digital channels. To cope with the problem of constrained bandwidth, the event-triggering ideas are implemented on both sensor-to-estimator and estimator-to-estimator channels. Meanwhile, we consider there is an attacker that possibly corrupts the measurement output. Under these circumstances, we aim to design the state estimator such that the exponential mean-square stability with H ∞ performance is satisfied. By virtue of Lyapunov-Krasovskii functional, we can derive the explicit estimator parameters by solving a set of linear matrix inequalities (LMIs). Finally, a numerical example is established to verify our theoretical results.

关键词： Stochastic systems Stability criteria Channel estimation Power electronics State estimation Numerical stability Time-varying systems

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Low-Power Anomaly Detection and Classification System based on a Partially Binarized Autoencoder for In-sensor computing

Low-Power Anomaly Detection and Classification System based ...

引用

ieee international conference on Electronics, Circuits and systems (ICECS)

作者： Paola Vitolo Gian Domenico Licciardo Luigi di Benedetto Rosalba Liguori Alfredo Rubino Danilo Pau University of Salerno SA Italy System Research and Applications STMicroelectronics MI Italy

This work proposes a new ultra low-power fault detection system, suitable for extreme edge or in-sensor computing. The system is composed of a hybrid HW/SW architecture: a hardware auto-encoder (AE) is always on at the edge for anomaly detection (AD), and of a software convolutional neural network (CNN) is activated only if the anomaly is detected for its classification. To achieve low area and energy requirements, the AE exploits an original partially binarization scheme, while the CNN shares the feature extraction module with the AE. The implementation of the AE on a Xilinx Artix-7 FPGA demonstrates that it is capable to manage in real-time sensors with a maximum Output Data Rate (ODR) of 365 kHz with a power dissipation of 122 mW. Best synthesis results with TSMC CMOS 65 nm standard cells show a power consumption of $138\ \mu\mathrm{W}/\text{MHz}$ and an area occupation of 0.49 mm 2 when real-time operations are set, enabling the possibility to integrate the complete HW accelerator in the auxiliary circuitry that typically equips inertial MEMS and on the same die. Comparisons with the current literature show that the proposed system obtains state-of-the-art performances in terms of accuracy and compactness.

关键词： Micromechanical devices Power demand Image edge detection Real-time systems Software sensors Power dissipation

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Internet of Robotic Things: A Comparison of Message Routing Strategies for Cloud-Fog computing Layers using M/M/c/K Queuing Networks

Internet of Robotic Things: A Comparison of Message Routing ...

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ieee international conference on systems, Man and Cybernetics

作者： Leonel Feitosa Lucas Santos Glauber Gonçalves Tuan Anh Nguyen Jae-Woo Lee Francisco Airton Silva Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil Konkuk Aerospace Design-Airworthiness Research Institute (KADA) Konkuk University Seoul South Korea Konkuk University Seoul South Korea

ISBN: (纸本)9781665442084

The Internet of Robotic Things (IoRT) has emerged as a game-changing player in the fourth industrial revolution (Industry 4.0) in which the robotic automation technologies are integrated with and reinforced by advanced computing paradigms like cloud/fog/IoT in order to maximize productivity of production chains of smart robotic agents in factories. A single robotic agent can comprise hundreds of sensors and actuators, and production processes performed by multiple agents can demand high computational costs, possibly only via remote resources in cloud or fog computing layers. In this context, it is paramount of importance to assimilate the performance of such computing paradigms for production chains of robotic agents in industrial factories and to provide IoRT system designers with assessment tools to comprehend and anticipate the operational performance of their projects at different development stages. This paper proposes an M/M/c/K model for the performance evaluation of IoRT systems integrated with fog-cloud computing paradigms. Focusing on routing strategies, we demonstrated that the proposed model allows evaluating how different configurations of components in an IoRT architecture impact the performance of the system using the performance metrics including mean response time, component utilization, number of messages, and drop rate. The performance modeling and evaluation of a specific IoRT in this paper can help computing system administrators to design and adopt advanced cloud/fog/IoT computing paradigms to maximize manufacturing productivity in industrial factories.

关键词： Productivity Cloud computing Service robots Computational modeling Routing Robot sensing systems Load management

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E-Voting systems using Blockchain: An Exploratory Literature Survey 2

E-Voting Systems using Blockchain: An Exploratory Literature...

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2nd international conference on Inventive Research in computing Applications, ICIRCA 2020

作者： Vivek, S.K. Yashank, R.S. Prashanth, Yashas Yashas, N. Namratha, M. B.M.S. College of Engineering Bengaluru India

ISBN: (纸本)9781728153742

E-Voting or electronic voting is a means for the election process to be conducted without the use of traditional paper ballots. The e-voting process, to be implemented in a large-scale scenario, requires the addressing of concerns concerning the security and reliability of such a system. The Blockchain technology, introduced by Satoshi Nakamoto using the cryptographic currency Bitcoin in 2008, opens up possibilities of designing and developing a secure, transparent and decentralized system with the absence of a third party for access and control, in the election procedure of casting and counting of votes. © 2020 ieee.

关键词： distributed ledger

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sensor Activity Control Strategy for Random Access Wireless sensor Network systems

Sensor Activity Control Strategy for Random Access Wireless ...

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2020 international Computer Symposium, ICS 2020

作者： Tsai, Rong-Guei Shih, Guan-Rong Tsai, Pei-Hsuan Putian University School of Information Engineering Putian City China National Cheng Kung University Institute of Manufacturing Information and Systems Tainan City Taiwan

ISBN: (纸本)9781728192550

Various investigations on wireless sensor networks (WSNs) have been conducted. However, only a few of these studies were related to quality of service (QoS). QoS control for WSNs is defined as the control of the number of active sensors. Effective control of the number of active sensors is important to prevent wastage in power resources and bandwidth as well as to satisfy the application demands. In a real network environment, controlling the number of active sensors is a difficult task because of the death and renewal of sensors, which cause frequent changes in the nodes. Two distributed algorithms were previously proposed for QoS control of WSNs. Two methods can be used for the effective control of sensor status: the Gur Game and ACK algorithms. This research proposed a new control algorithm called the sensor Activity Control Strategy (SACS), which provides high-power saving of sensors. The SACS requires less time that the Gur Game and ACK algorithms to achieve the targets. © 2020 ieee.

关键词： Wireless sensor networks

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MeLoN: distributed Deep Learning meets the Big Data Platform

MeLoN: Distributed Deep Learning meets the Big Data Platform

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Autonomic computing and Self-Organizing systems Companion (ACSOS-C), ieee international conference on

作者： Dae-Cheol Kang Seoungbeom Heo Hyeounji Jang Hyeock-Jin Lee Minkyoung Cho Jik-Soo Kim Myongji University Yongin Republic of Korea

Recent advancements in Artificial Intelligence have brought “Deep Learning” frameworks to be a cornerstone for the 4th Industrial Revolution along with “Big Data” platform technologies such as Apache Hadoop. However, efficient processing of deep learning applications has become challenging as the overall sizes of data and model increase rapidly. To address this problem, we can leverage big data platforms that have successfully provided stable storage and data processing capability during the past decade. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring m

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Comparison between Communication Technology used in Smart Building 8

Comparison between Communication Technology used in Smart Bu...

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8th international conference on Information Technology and Multimedia, ICIMU 2020

作者： Syarmila Bt Sameon, Sera Yussof, Salman Jorgensen, Bo Norregaard College of Computing and Informatics Universiti Tenaga Nasional Kajang Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Malaysia Center for Energy Informatics University of Southern Denmark Name Odense Denmark

ISBN: (纸本)9781728173108

Smart Building and its applications have become one of the essential ways of delivering a better quality of life and environment nowadays. The smart construction of building use the technology to share information between application by automate various processes, such as lighting, heating, ventilation and air conditioning (HV AC), security and other systems. A smart building and supervisory control systems infrastructure uses wired, wireless, and smart sensor networks to collect data according to the needs and services of the business. This paper presents a review and a comparison between the various communications technologies commonly used in smart building. Choosing an appropriate technology is crucial in smart building in ensuring the automated process run well. © 2020 ieee.

关键词： Wireless sensor networks

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Low-memory and high-performance CNN inference on distributed systems at the edge 21

Low-memory and high-performance CNN inference on distributed...

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Proceedings of the 14th ieee/ACM international conference on Utility and Cloud computing Companion

作者： Erqian Tang Todor Stefanov Leiden University Leiden the Netherlands

ISBN: (纸本)9781450391634

Nowadays, some applications need CNN inference on resource-constrained edge devices that may have very limited memory and computation capacity to fit a large CNN model. In such application scenarios, to deploy a large CNN model and perform inference on a single edge device is not feasible. A possible solution approach is to deploy a large CNN model on a (fully) distributed system at the edge and take advantage of all available edge devices to cooperatively perform the CNN inference. We have observed that existing methodologies, utilizing different partitioning strategies to deploy a CNN model and perform inference at the edge on a distributed system, have several disadvantages. Therefore, in this paper, we propose a novel partitioning strategy, called Vertical Partitioning Strategy, together with a novel methodology needed to utilize our partitioning strategy efficiently, for CNN model inference on a distributed system at the edge. We compare our experimental results on the YOLOv2 CNN model with results obtained by the existing three methodologies and show the advantages of our methodologies in terms of memory requirement per edge device and overall system performance. Moreover, our experimental results on other representative CNN models show that our novel methodology utilizing our novel partitioning strategy is able to deliver CNN inference with very reduced memory requirement per edge device and improved overall system performance at the same time.

关键词： convolutional neural networks high-performance CSDF distributed system at the edge low-memory

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An Approach for Data Pipeline with distributed Query Engine for Industrial Applications 25

An Approach for Data Pipeline with Distributed Query Engine ...

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25th ieee international conference on Emerging Technologies and Factory Automation, ETFA 2020

作者： Ghosh Chowdhury, Arnab Illian, Marvin Wisniewski, Lukasz Jasperneite, Jürgen Technische Hochschule Ostwestfalen-Lippe InIT - Institute Industrial It Lemgo Germany Paderborn University Computer Networks Group Paderborn Germany Fraunhofer IOSB-INA Lemgo Germany

ISBN: (纸本)9781728189567

The data driven services in industrial automation systems are transforming the world of automation industry by optimizing industrial processes and providing Value Added Services (VASs) with the grace of Industry 4.0, Big Data and Artificial Intelligence (AI). A demand driven data pipeline is essential to connect different industrial data sources in a shop floor with different data storage systems for service provisioning. This paper analyzes an experimental approach and corresponding challenges to optimize computing resource allocation in industrial applications to construct such demand driven data pipeline to provide data driven services through an open source, flexible and extensible distributed query engine known as Presto, which can perform interactive analytical queries for different purposes such as condition monitoring, asset management or many others. © 2020 ieee.

关键词： Industry 4.0

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