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Edge-enhanced analytics via latent space dimensionality reduction 7

Edge-enhanced analytics via latent space dimensionality redu...

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7th IEEE/ACM international conference on Big data computing, Applications and Technologies (BDCAT)

作者： Ndubuaku, Maryleen U. Ali, Muhammad K. Anjum, Ashiq Liotta, Antonio Reiff-Marganiec, Stephan Univ Leicester Coll Sci & Engn Leicester Leics England Univ Leicester Coll Sci & Engn Derby England Free Univ Bozen Bolzano Fac Comp Sci Bozen Bolzano Italy Univ Derby Coll Engn & Technol Derby England

ISBN: (纸本)9780738123967

With the Internet of Things technology, almost any remote sensing devices, wearables, and smart objects are equipped to transmit large volumes of data in continuous streams. In conventional cloud-centric analytics, all the raw data is transferred to a data centre and processed in real-time, near real-time, or in batches. However, this approach is usually not very responsive to real-time analytics due to the latency in transmission alongside network traffic, bandwidth and data transmission costs. To tackle this, edge-enhanced analytics ensures that raw data can be preprocessed at the edge and sent across the network channel in a more compact form. A specific category of deep learning model, autoencoder, can help to achieve this by transforming high-dimensional data into compact representation. We propose an edge-enhanced framework which deploys a deep autoencoder model on the network edge for data compression. After training of models in the cloud, the encoder part of the autoencoder is deployed on the edge for data reduction while the decoder remains on the cloud to reconstruct the data for an image classification task on the cloud. We applied supervised fine-tuning using the intrinsic dimensionality of the data to achieve an accuracy that surpasses the baseline cloud model. The solution was explored in the context of an image recognition problem using the MNIST and FASHION-MNIST datasets. The framework was validated on an event simulator to estimate the network savings of the proposed method in terms of bandwidth and latency. The edge-enhanced approach saves up to 74% bandwidth compared to the centralised analytics. In addition, real-time analytics is further improved by taking 25% less time to complete the task.

关键词： Autoencoder, Cloud computing, Deep Learning, Dimensionality Reduction, Edge computing

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Utilizing Timed Petri Nets to Guide data-Driven Fault Diagnosis of PLC-Timed Event Systems 11

Utilizing Timed Petri Nets to Guide Data-Driven Fault Diagno...

引用

Joint 11th international conference on Soft computing and intelligent Systems and 21st international Symposium on Advanced intelligent Systems (SCIS-ISIS)

作者： Cohen, Joseph Jiang, Baoyang Ni, Jun Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Foxconn Ind Internet Technol Serv Business Grp Shenzhen Peoples R China

ISBN: (纸本)9781728197326

Highly synchronized timed event systems managed by programmable logic controllers are ubiquitous in manufacturing. Discrete event system methods such as finite state machines and Petri Nets have been useful for diagnosing known faults in such systems. However, existing methods do not scale as well for practical applications where faulty behavior is not fully known and event timing may vary. With the advent of big data, a new methodology is presented that strengthens fault diagnosis capabilities utilizing machine learning and Timed Petri Nets. The hybrid approach consists of building a Timed Petri Net model of the normal process to help select discriminating features based on timed event sequences. This work focuses on modeling Timed Petri Nets to serve as knowledge representations of the nominal process behavior to guide data-driven fault diagnosis of timed event systems. A methodology based on identification of observable events is introduced, consisting of three different and complementary heuristics including demarcating periods, modeling concurrencies, and imposing synchronizations. Then, using features selected by using the Timed Petri Net, a nonlinear SVM is implemented for multiclass fault classification. The proposed framework is applied to an existing semiconductor manufacturing process with events timed via binary programmable logic controller signals. With over 97% validation accuracy achieved for fault diagnosis, the hybrid modeling approach shows promise for smart manufacturing applications.

关键词： artificial intelligence Timed Petri Nets fault diagnosis fault classification smart manufacturing programmable logic controllers support vector machines discrete event systems

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FengHuoLun: A Federated Learning based Edge computing Platform for Cyber-Physical Systems 18

FengHuoLun: A Federated Learning based Edge Computing Platfo...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Zhang, Chong Liu, Xiao Zheng, Xi Li, Rui Liu, Huai Deakin Univ Sch Informat Technol Geelong Vic Australia Macquarie Univ Dept Comp Sydney NSW Australia Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Vic Australia

ISBN: (纸本)9781728147161

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge computing platform for CPS, named "FengHuoLun". Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework;2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

关键词： Federated Learning Edge computing Cyber-Physical Systems Trustworthy Microservices

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A Survey of Application of ML and data Mining Techniques for smart Irrigation System 2

A Survey of Application of ML and Data Mining Techniques for...

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

作者： Nandanwar, Himanshu Chauhan, Anamika Pahl, Dushyant Meena, Harshit Information Technology Delhi Technological University Delhi India

ISBN: (纸本)9781728153742

This paper reviews our current research in agriculture analytics on an open-source platform using data mining and machine learning techniques. Various sensors are used to collect data that provides real-time analytics on the weather forecast, soil moisture, air temperature, PH, humidity. The smart irrigation system is paired with different hardware and development application. The science of machine learning and data mining plays a significant role here. It is a data analysis process using multiple models and algorithms to trained data directly. Machine learning and data mining to hold the irrigation system in r leaner manner. For instance, it optimizes the use of water and provides a critical amount of water and fertility to increase production efficiency, reduce manpower involvement, and reduce crop diseases. The survey tests the effect of applicable techniques and how these techniques boost efficiency. © 2020 IEEE.

关键词： data mining

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Construction and Development of High-Tech smart City

Construction and Development of High-Tech Smart City

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international conference on Machine Learning and Big data analytics for IoT Security and Privacy, SPIoT 2020

作者： Liu, Quan Guangzhou College of Commerce GuangzhouGuangdong China

ISBN: (纸本)9783030627454

Information technology is becoming more and more mature, and the construction of smart cities can achieve accurate economic and social development. Modern electronic information technology, represented by Internet of things technology, is the key force and core technology for building smart cities. This paper firstly studies the characteristics and connotation of smart cities, and then draws the main characteristics of smart city development. Then it focuses on the IoT from the aspects of architecture, transportation, tourism, and medical care briefly analyze the technology in building smart cities. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： smart city

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ICAC 2021 - 3rd international conference on Advancements in computing, Proceedings

ICAC 2021 - 3rd International Conference on Advancements in ...

引用

3rd international conference on Advancements in computing, ICAC 2021

ISBN: (纸本)9781665408622

The proceedings contain 83 papers. The topics discussed include: event-driven malicious URL extractor;APlus: an intelligent lecture delivery system using artificial intelligence learning analytics;smart intelligent floriculture assistant agent (SIFAA);smart monitoring and disease detection for robotic harvesting of tomatoes;SentinelPlus: a cost-effective cyber security solution for healthcare organizations;e-learn detector: smart behavior monitoring system to analyze student behaviors during online educational activities;automated crop harvesting, growth monitoring and disease detection system for vertical farming greenhouse;and a sensitive data leakage detection and privacy policy analyzing application for android systems (PriVot).

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Distributed Fog computing Architecture for Real-Time Anomaly Detection in smart Meter data 6

Distributed Fog Computing Architecture for Real-Time Anomaly...

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6th IEEE international conference on Big data computing Service and Applications (IEEE BigdataService)

作者： Jaiswal, Rituka Chakravorty, Antorweep Rong, Chunming Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway

ISBN: (纸本)9781728170220

The use of Fog computing for real-time Big data monitoring of power consumption is gaining popularity. In traditional systems, Cloud servers receive sensor Big data, perform predictions and detect anomalies or any threat patterns and then raise the alarms. With exponentially increasing sensor data, Cloud servers are becoming impractical to process this data because of the issues of volume, velocity, variety, network bandwidth, real-time support and security issues. Fog computing is introduced as a Distributed computing paradigm that uses intermediate computing infrastructure for processing to overcome the limitations of Cloud computing. In this paper, we propose a hierarchically Distributed Fog computing architecture to deploy machine learning based anomaly detection models for generating insights from the collected smart meter sensor data from the household. The anomaly detection is divided into two steps: model training and anomaly detection. We perform detailed analysis and evaluation of the models using standard open datasets obtained from UCI machine learning repository. The results confirm the efficacy of our proposed architecture. We used open source framework and software for our experiments.

关键词： Fog computing Real-time Anomaly detection Big data analytics Machine Learning Ensemble Learning Power Consumption Prediction Distributed computing

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Adaptability of professional setting and local industrial structure of applied undergraduate colleges in dongguan under the background of big data

Adaptability of professional setting and local industrial st...

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Wang, Yan Guangdong University of Science and Technology Dongguan523083 China

ISBN: (纸本)9789811525674

Recently, new technologies such as the Internet of Things, cloud computing, and big data have penetrated into various fields of urban life, which have brought great changes to people’s lives. "Dongguan Big data Development Plan (2016–2020)", the document clearly points out that the combination of Dongguan "smart City" and "Dongguan Manufacturing 2025" will promote the rapid development of big data industry, and the Internet of Things and cloud computing will grow into the pillars of Dongguan Industry. This paper analyzes the data of the four major applied undergraduate colleges in Dongguan, such as the professional setting, the number of enrolled students, and the scale of professional setting, and the connection between the pillar industries and emerging industries in Dongguan. The professional settings of the four local undergraduate colleges in Dongguan are inferior to the pillar industries in Dongguan. This provides a reference for better service and local economy in local undergraduate colleges in Dongguan. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Big data

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Componentry Analysis of intelligent Transportation Systems in smart Cities towards a Connected Future 22

Componentry Analysis of Intelligent Transportation Systems i...

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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： Trivedi, Priyanka Zulkernine, Farhana Queen's University School of Computing Kingston Canada

ISBN: (纸本)9781728176499

intelligent Transportation System (ITS) is positioned at the confluence of the two most powerful currents of our time, information technology empowered by artificial intelligence and the ever-improving transportation technologies. This confluence is ushering in an era of transportation services that are inclusive, safer, greener, and more efficient at the same time. Like any other big change, this is strongly disruptive as it upends the concept of ownership and availability while challenging authorities all over the world to reimagine logistics, people movement, and environment protection. This paper examines the ITS through its components, architecture, and related applications. It dives deeper into the hood to examine how these components are, both individually and collectively, taking us towards the connected future and smart cities. Finally, it attempts to put a perspective by evaluating the challenges and opportunities inherent in the deployment and propagation of the ITS. © 2020 IEEE.

关键词： smart city

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Latency Minimization for Computation Offloading with Hybrid Multiple Access Methods

Latency Minimization for Computation Offloading with Hybrid ...

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2020 international conference on computing, Networking and Communications, ICNC 2020

作者： Liu, Lina Sun, Bo Wu, Yuan Tsang, H.K. Danny Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China

ISBN: (纸本)9781728149059

Mobile edge computing (MEC) has been considered as a promising technique to support computation-intensive and latency-sensitive multimedia applications (e.g., video analytics and real-time target tracking) in future 5G/6G systems, by enabling mobile terminals to offload computation workloads to edge servers. In this paper, we study a computation offloading scenario with hybrid multiple access methods to minimize the offloading latency. Particularly, we present a hybrid Non-orthogonal Multiple Access (NOMA)-Orthogonal Multiple Access (OMA) transmission, which covers three offloading strategies, namely, hybrid NOMA, pure NOMA and pure OMA. Based on the successive convex approximation technique, we then design an efficient algorithm to solve the complicated non-convex problem and rigorously prove its convergence. Extensive numerical tests show that our scheme can always help mobile terminals to flexibly choose the best offloading strategy according to mobile terminals' power and energy budgets. © 2020 IEEE.

关键词： computation offloading

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