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Adaptive Data Replication in real-time Reliable Edge computing for Internet of things

Adaptive Data Replication in Real-Time Reliable Edge Computi...

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5th IEEE/ACM international conference on Internet-of-things Design and Implementation (ACM/IEEE IoTDI)

作者： Wang, Chao Gill, Christopher Lu, Chenyang Natl Taiwan Normal Univ Dept Comp Sci & Informat Engn Taipei Taiwan Washington Univ Dept Comp Sci & Engn St Louis MO 63110 USA

ISBN: (纸本)9781728166025

Many Internet-of-things (IoT) applications rely on timely and reliable processing of data collected from embedded sensing devices. To achieve timely response, computing tasks are executed on IoT gateways at the edge of clouds, and for fault tolerance, the gateways perform data replication to backup gateways. In this paper, we report our study of data replication strategies and a real-time and fault-tolerant edge computing architecture for IoT applications. We first analyze how both embedded devices' storage constraints and data replication frequency may impose timing constraints on data replication tasks, and we investigate correlations between execution of data replication tasks and execution of edge computing tasks. Accordingly, we propose adaptive data replication strategies and introduce a framework for real-time reliable edge computing to meet the needed levels of data loss tolerance and timeliness. We have implemented our framework and empirically evaluated the proposed strategies with baseline approaches. We set up experiments using Industrial IoT traffic configurations that have requirements on data loss and timeliness, and our experimental results show that the proposed data replication strategies and framework can ensure needed levels of data loss tolerance, save network bandwidth consumption, while maintaining the latency performance.

关键词： real time systems

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Artificial Neural Networks in Export and Import Forecasting: An Analysis of Opportunities

Artificial Neural Networks in Export and Import Forecasting:...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Mykhailo R. Luchko Nataliia Dziubanovska Oksana Arzamasova Faculty of Finance and Accounting West Ukrainian National University Ternopil Ukraine Faculty of Computer Information Technologies West Ukrainian National University Ternopil Ukraine SSU Vocational College of Economics Law and Information Technologies West Ukrainian National University Ternopil Ukraine

ISBN: (纸本)9781665426060

the paper concerns the issue of forecasting trends in trade relations between Malaysia and Ukraine using artificial neural networks. the current state of trade relations between the countries in the context of the COVID-19 pandemic has been analyzed. Considering the advantages and disadvantages of the types of neural networks built into the software product STATISTICA 10, MLP network has been chosen to build a predictive model of imports and exports of goods. the forecast values for the volumes of exports and imports of goods for the period from January 2021 to December 2022 have been calculated. Comparing the results, the researchers concluded that the artificial neural network is the most successful model for forecasting imports and exports. Suggestions for effective evaluation and forecasting of international trade indicators using the theory of time series and neural network technologies are given and the directions of further scientific research arising from this paper are formed.

关键词： COVID-19 international trade Uncertainty Pandemics time series analysis Artificial neural networks Predictive models

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Computer-aided Selection of the Optimal Gamma Correction Parameter for Image Enhancement in Video Analytics systems

Computer-aided Selection of the Optimal Gamma Correction Par...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Oleksandr Golovin V. M. Glushkov Institute of Cybernetics of the NAS of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665426060

Video analytics systems are rapidly evolving, and the effectiveness of their work depends on the quality of operations at the initial level of the entire processing process, namely the quality of segmentation of objects in the scene and their recognition. Successful performance of these procedures is primarily due to image quality, which depends on many factors: technical parameters of video sensors, low or uneven lighting, changes in lighting levels of the scene due to weather conditions, time changes in illumination, or changes in scenarios in the scene. A novel method for determining the optimal value of a gamma correction parameter, which ensures the selection of the image of the best quality in automatic mode is represented in the paper. the method is based on the use of gamma correction, which reflects properties of a human visual system, effectively reduces the negative impact of changes in scene illumination and due to simple adjustment and effective implementation is widely used in practice. the technique of selection in an automatic mode of the optimum value of the gamma parameter at which the corrected image reaches the maximum quality is developed.

关键词： Image quality Image sensors Histograms Image segmentation Visual analytics Video sequences Lighting

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Implementation of Effective Evidence-based Monitoring of the Object State by Means of Wireless Network Object systems

Implementation of Effective Evidence-based Monitoring of the...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Bohdan Shevchuk Orest Ivakhiv Mykhaylo Geraimchuk Glushkov Institute of Cybernetics National Academy of Sciences of Ukraine Kyiv Ukraine Lviv Polytecnic National University Lviv Ukraine National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (纸本)9781665426060

Methodological bases of effective realization of operative analysis of the long-term monitoring object state with use of processor means of information and energy-efficient object and onboard systems of wireless monitoring networks are offered. Taking into account the minimization of calculations in the places of monitoring signal input by aperture or zone control of both calculated indicators and signal characteristics changes, the formation of Boolean variable information models of object behavior is proposed. the promptly determined information states of the longterm monitoring objects are fully corresponded to the current functional and operational object state. the calculation of simple and informative indicators and signals characteristics using essential samples of signals parameters is offered. the proposed information technology is focused on the application in various fields of human activity in order to obtain reliable evidence to prevent and neutralize threatening conditions, events and phenomena..

关键词： time-frequency analysis Wireless networks Apertures Minimization Energy efficiency Entropy Reliability

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Online Pattern Recognition of time-series Gas Sensor Data with Adaptive 2D-CNN Ensemble

Online Pattern Recognition of Time-series Gas Sensor Data wi...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Vishakha Pareek Santanu Chaudhury Sanjay Singh CSIR - Central Electronics Engineering Research Institute Pilani India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad India Indian Institute of Technology Delhi India

ISBN: (纸本)9781665426060

Intelligent sensor system design demands the efficient pattern recognition method that uses raw time-series sensor data to identify the target gas with fast and accurate results. the presented work uses gas sensor array response (open sampling setting) for 10 bio-marker gases. the responses are obtained at different concentrations. We propose a 2D convolution neural network (2D-CNN) based adaptive ensemble network for gas identification. the Spatio-temporal correlation of sensor array responses inspired us to design deep-learning-based gas identification networks. the network uses raw time-series gas sensor array data and identifies the target gas mixtures with improved accuracy despite sensor drift. Experimental results show that the proposed methods are an effective technique with identification accuracy approximately 91% for identifying gas mixture for smart sensor system application. the proposed method outperforms and provides higher identification accuracy than comparable various machine learning and deep learning methods.

关键词： Deep learning Gases Adaptive systems Neural networks Data acquisition Pattern recognition Gas detectors

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How Can Machine Learning Impact on Wireless Network and IoT? - A Survey 11

How Can Machine Learning Impact on Wireless Network and IoT?...

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11th international conference on computing, Communication and Networking Technologies, ICCCNT 2020

作者： Laha, Swarnalina Chowdhury, Nilanjan Karmakar, Raja Techno International New Town Department of Information Technology 700156 India

ISBN: (纸本)9781728168517

Machines can be trained by human beings by feeding them massive amount of data, like examples and instructions. they have to learn without human interventions. the Internet of things (IoT) is still in its outset and is in the developing front. IoT consisting of devices/machines with diverse sensors associated together in wireless networks, needs great amount of security for avoiding unauthorized access to the network as it collects and transmit data packets. With industrial fields showing much interest in the forefront, intrusion threats can make it vulnerable. In this paper, we are addressing several positive measures regarding the use of machine learning (ML) modalities like supervised learning (SL), unsupervised learning (UL), semi- supervised learning (SSL), reinforcement learning (RL), support vector machines (SVM) which are used for training IoT devices. this paper also addresses a brief survey of the scenarios where machine learning algorithms have been applied in various IoT devices and networks such as signal authentication, security, network flow, information retrieval, traffic management, spectrum sensing. It also indicates the necessity of surveying the scattered works on machine and deep learning applications for various aspects like security, congestion control over wireless computer networks, embedded sensor systems for IoT applications. Several limitations of existing researches and a number of open research issues which fellow researchers may find useful in the future have also been discussed. © 2020 IEEE.

关键词： Internet of things

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ICE: An Intelligent Cognition Engine with 3D NAND-Based In-Memory computing for Vector Similarity Search Acceleration 22

ICE: An Intelligent Cognition Engine with 3D NAND-Based In-M...

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Proceedings of the 55th Annual IEEE/ACM international Symposium on Microarchitecture

作者： Han-Wen Hu Wei-Chen Wang Yuan-Hao Chang Yung-Chun Lee Bo-Rong Lin Huai-Mu Wang Yen-Po Lin Yu-Ming Huang Chong-Ying Lee Tzu-Hsiang Su Chih-Chang Hsieh Chia-Ming Hu Yi-Ting Lai Chung-Kuang Chen Han-Sung Chen Hsiang-Pang Li Tei-Wei Kuo Meng-Fan Chang Keh-Chung Wang Chun-Hsiung Hung Chih-Yuan Lu Macronix International Co. Ltd. and Department of Electrical Engineering National Tsing Hua University Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology and Macronix International Co. Ltd. Institute of Information Science Academia Sinica Macronix International Co. Ltd Macronix International Co. Ltd. and Graduate Institute of Electronics Engineering National Taiwan University Department of Computer Science and Information Engineering National Taiwan University and Graduate Institute of Electronics Engineering National Taiwan University Department of Electrical Engineering National Tsing Hua University

ISBN: (纸本)9781665462723

Vector similarity search (VSS) for unstructured vectors generated via machine learning methods is a promising solution for many applications, such as face search. With increasing awareness and concern about data security requirements, there is a compelling need to store data and process VSS applications locally on edge devices rather than send data to servers for computation. However, the explosive amount of data movement from NAND storage to DRAM across memory hierarchy and data processing of the entire dataset consume enormous energy and require long latency for VSS applications. Specifically, edge devices with insufficient DRAM capacity will trigger data swap and deteriorate the execution performance. To overcome this crucial hurdle, we propose an intelligent cognition engine (ICE) with cognitive 3D NAND, featuring non-volatile in-memory computing (nvIMC) to accelerate the processing, suppress the data movement, and reduce data swap between the processor and storage. this cognitive 3D NAND features digital nvIMC techniques (i.e., ADC/DAC-free approach), high-density 3D NAND, and compatibility with standard 3D NAND products with minor modifications. To facilitate parallel INT8/INT4 vector-vector multiplication (VVM) and mitigate the reliability issue of 3D NAND, we develop a bit-error-tolerance data encoding and a two's complement-based digital accumulator. VVM can support similarity computations (e.g., cosine similarity and Euclidean distance), which are required to search "the most similar data" right where they are stored. In addition, the proposed solution can be realized on edge storage products, e.g., embedded Multimedia Card (eMMC). the measured and simulated results on real 3D NAND chips show that ICE enhances the system execution time by 17× to 95× and energy efficiency by 11× to 140×, compared to traditional von Neumann approaches using state-of-the-art edge systems with MobileFaceNet on CASIA-WebFace dataset. To the best of our knowledge, this work demo

关键词： 3D NAND in-memory computing vector similarity search unstructured data search

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Cyber-Physical systems in Smart City: Challenges and Future Trends for Strategic Research 5th

Cyber-Physical Systems in Smart City: Challenges and Future ...

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5th international conference on Advanced Intelligent systems and Informatics (AISI)

作者： Juma, Mazen Shaalan, Khaled British Univ Dubai Fac Engn & Informat Technol Block 111st & 2nd Floor Dubai U Arab Emirates

ISBN: (纸本)9783030311292;9783030311285

Modern cities today compete with each other to be smarter, maintain a more sustainable with high-quality living, acquire talents, and provide jobs. this digital transformation through agile drivers will help address the increasing challenges of urbanization in a couple of decades. the Cyber-Physical System (CPS) is becoming pervasive in every aspect of smart city daily life and considered as one of the four fundamental conceptual approaches of the fourth generation industrial revolution (Industry 4.0). CPS used to describe the next generation of a diverse spectrum of complicated, multidisciplinary, physically comprehending engineered systems that integrates embedded cyber aspects into the physical world. It implants computation technologies, communication control, the convergence of information, and physical processes together with strategic importance internationally. CPS is still a vast research area. As a result, it opens venues for applications across multiple scales. this paper presents an in-depth survey of the related works, focusing on the design and how it relates to different research fields, current concepts, and real-life applications to understand CPS more precisely. Further, it enumerates an extensive set of CPS challenges and opportunities, introducing visionary ideas, research strategies, and future trends expected for future-oriented technological solutions, like cloud computing, Internet of things, and Big Data. these technological solutions are to play a critical role in CPS research and have significant impacts on the smart city.

关键词： Cyber-physical systems Smart city Industry 4.0 Big Data Cloud computing Internet of things Research strategies Future trends

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Parallel Implementation of H.265 Intra-Frame Coding Based on FPGA Heterogeneous Platform 22

Parallel Implementation of H.265 Intra-Frame Coding Based on...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Chen, Wenjie He, Qunfang Li, Shen Xiao, Bo Chen, Mingsong Chai, Zhilei East China Normal University National Trusted Embedded Software Engineering Technology Research Center Shanghai China East China Normal University MoE Engineering Research Center for Software/Hardware Co-design Technology Shanghai China Jiangnan University School of IoT Engineering Wuxi China

ISBN: (纸本)9781728176499

the new video coding algorithm H.265 achieved twice compression rate than the prevailing H.264, but the encoding speed is reduced several times, making it difficult for the real-time applications such as video surveillance and remote meeting. In this paper, we design a parallel intra-frame mode decision architecture based on FPGA to accelerate the coding. We prove that the mode decision problem of multi-level large coding unit of H.265 can be transformed into the accumulation of multiple single-level small coding unit mode decision. Furthermore, we design how mode decision is accelerated by fine-grained parallelism within coding unit, and pipelining among coding units, then implement them on FPGA. the experimental results show that the proposed architecture achieves maximum 93.6\times acceleration compare with the ARM A53 platform, with an insignificant loss of video quality. © 2020 IEEE.

关键词： Field programmable gate arrays (FPGA)

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IoT-enabled Smart Energy Metering Solution with Soft-UPS for Developing Countries

IoT-enabled Smart Energy Metering Solution with Soft-UPS for...

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IEEE international Workshop on Intelligent Data Acquisition and Advanced computing systems: Technology and applications

作者： Mahnoor Aftab Aamna Nasir Hameed Ali Samiq Muhammad Bilal Shah Muhammad Adeel Pasha Nauman Ahmad Zaffar Amrut Ganesh Dant Axel Sikora Lahore University of Management Sciences (LUMS) Pakistan Institute of Reliable Embedded Systems and Communication Electronics Offenburg University Germany

ISBN: (纸本)9781665426060

Due to its potential in improving the efficiency of energy supply, smart energy metering (SEM) has become an area of interest with the surge in Internet of things (IoT). SEM entails remote monitoring and control of the sensors and actuators associated with the energy supply system. this provides a flexible platform to conceive and implement new data driven Demand Side Management (DSM) mechanisms. the IoT enablement allows the data to be gathered and analyzed at requisite granularity. In addition to efficient use of energy resources and provisioning of power, developing countries face an additional challenge of temporal mismatch in generation capacity and load factors. this leads to widespread deployment of inefficient and expensive Uninterruptible Power Supply (UPS) solutions for limited power provisioning during resulting blackouts. Our proposed “Soft-UPS” allows dynamic matching of load and generation through a combination of managed curtailment. this eliminates inefficiencies in the energy and power value chain and allows a data-driven approach to solving a widespread problem in developing countries, simultaneously reducing both upfront and running costs of conventional UPS and storage. A scalable and modular platform is proposed and implemented in this paper. the architecture employs “WiMODino” using LoRaWAN with a “Lite Gateway” and SQLite repository for data storage. Role based access to the system through an android application has also been demonstrated for monitoring and control.

关键词： Actuators Demand side management Developing countries Sensor systems Smart grids Internet of things Surges

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