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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...

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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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Big data and Economics Research

Big Data and Economics Research

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

作者： Zhang, Wanting School of Economics Shanghai University 99 Shangda Road Baoshan District Shanghai200000 China

ISBN: (纸本)9783030627423

Big data technology is a new digital technology, which is as famous as the Internet of things and artificial intelligence in the past 20 years. It is an important technology that will profoundly change the way of human production and life. For economic researchers, the popularization and development of big data will bring subversive changes to the acquisition, collation and timeliness of data in economic research, and will play a positive role in promoting the development of economic research methods. In terms of the availability of data sources, the combination of big data technology and computer technology can make researchers easily obtain the required data, no matter the structured data we usually use or the unstructured data we don’t often use. In terms of the timeliness of data acquisition, big data technology makes the timeliness of economic data possible and effectively solves the lag in economic research Post problem: in terms of the value of data acquisition, although massive data reduces the value density of data, the quality of data captured by big data technology is far higher than that of ordinary data acquisition methods. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data acquisition

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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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Machine Learning Algorithm in smart Farming for Crop Identification 1

引用

international conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Vindya, N. D. Vedamurthy, H. K. SIT Dept CSE Tumakuru India

ISBN: (数字)9783030372187

ISBN: (纸本)9783030372187;9783030372170

Agriculture is the main supporting sector of Indian economy and most of the rural population's livelihood depends on it. Crop yielding has been decreasing day by day because it's growth mainly depends on monsoon which is highly unpredictable in India. Further, the crop growth also depends on soil parameters, which varies due to ground contamination. For increasing the crop productivity, there is a need to incorporate newtechnologies in the field of agriculture. By utilizing new technologies like Machine Learning and Internet of Things applications in the field of agriculture will be enhance the financial growth of the country. Here, we have presented a framework which mainly focuses on agriculture in Tumakuru district of Karnataka that uses Naive Bayes classification method to suggest the farmers with the best suitable crop for sowing in that particular environmental condition.

关键词： Machine learning Gaussian Naive Bayes data analytics Prediction

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Prevalence of HCV in Shopian: Using data Visualization (data Mining) 3rd

Prevalence of HCV in Shopian: Using Data Visualization (Data...

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3rd international conference on intelligent computing and Communication (ICICC)

作者： Ahmad, Zubair Seethlani, Jitendra Gul, Sheikh Babar Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci Sehore 466001 MP India Mini Secretariat Chief Med Off Block B Shopian 192303 J&K India

ISBN: (纸本)9789811510847;9789811510830

data mining is an important subfield of data Science and is also called knowledge discovery within a database. In this paper, we actually use data mining visualization techniques to analyze the huge database of District Shopian and assume prevalence rate of HCV in Specific Area (Vehil) of District Shopian (J&K). Health Department of District Shopian uses different types of registers to maintain the data of patients. During survey, we go through the registers to collect data of symptomatic HCV patients analyze it by maintaining the disease trends. data mining analytical tools were being used to detect prevalence of HCV infection distribution and also identify the reasons that contributed to such widespread of HCV in the area of Vehil

关键词： Descriptive analytics Predictive analytics data visualization techniques hepatic C CASE STUDY

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UAV Deployment Design for Maximizing Effective data with Delay Constraint in a smart Farm

UAV Deployment Design for Maximizing Effective Data with Del...

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IEEE/CIC international conference on Communications in China (ICCC)

作者： Zhao, Junwei Wang, Ying Fei, Zixuan Wang, Xue Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781728173276

data transmission and real-time processing of Internet of Things (IoT) devices in remote unmanned area are becoming a challenging issue. In this paper, an Unmanned Aerial Vehicle (UAV) enabled computing system is investigated in a smart farm, where multiple UAVs are deployed to make intelligent decisions based on data collected by sensors. First, considering the delay constraint of data processing, the concept of effective data is introduced. Next, the effective data of farm monitoring devices (FMDs) is maximized by jointly optimizing computing and communication resources allocation and the deployment of UAVs. The formulated problem is a combinatorial optimization problem that is hard to tackle. Furthermore, this problem is transformed into two sub-problems and a two-layer iterative optimization algorithm is proposed to get an approximate optimal solution. Finally, simulation results demonstrate that the proposed algorithm has a significant increase in terms of effective data.

关键词： smart farm delay constraint effective data maximization multi-UAVs deployment mobile edge computing

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international conference on Computational Methods and data Engineering, ICMDE 2020

International Conference on Computational Methods and Data E...

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international conference on Computational Methods and data Engineering, ICMDE 2020

ISBN: (纸本)9789811568756

The proceedings contain 50 papers. The special focus in this conference is on Computational Methods and data Engineering. The topics include: On roman domination of graphs using a genetic algorithm;general variable neighborhood search for the minimum stretch spanning tree problem;tabu-embedded simulated annealing algorithm for profile minimization problem;deep learning-based asset prognostics;evaluation of two feature extraction techniques for age-invariant face recognition;XGBoost: 2D-object recognition using shape descriptors and extreme gradient boosting classifier;Comparison of principle component analysis and stacked autoencoder on NSL-KDD dataset;maintainability configuration for component-based systems using fuzzy approach;development of petri net-based design model for energy efficiency in wireless sensor networks;Hybrid ANFIS-GA and ANFIS-PSO based models for prediction of type 2 diabetes mellitus;lifting wavelet and discrete cosine transform-based super-resolution for satellite image fusion;biologically inspired intelligent machine and its correlation to free will;weather status prediction of Dhaka City using machine learning;image processing: What, how and future;a study of efficient methods for selecting quasi-identifier for privacy-preserving data mining;day-ahead wind power forecasting using machine learning algorithms;query relational databases in Punjabi language;machine learning algorithms for big data analytics;fault classification using support vectors for unmanned helicopters;EEG signal analysis and emotion classification using bispectrum;social network analysis of youtube: A case study on content diversity and genre recommendation;Slack feedback analyzer (SFbA);a review of tools and techniques for preprocessing of textual data;A U-shaped printed UWB antenna with three band rejection;model for predicting academic performance through artificial intelligence.

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Big data Processing and Artificial Intelligence at the Network Edge 22

Big Data Processing and Artificial Intelligence at the Netwo...

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22nd international conference on Transparent Optical Networks (ICTON)

作者： Olmos, J. J. Vegas Cugini, Filippo Buining, Fred O'Mahony, Niamh Truong, Thuy Liss, Liran Oved, Tzahi Binshtock, Zac Goldenberg, Dror Mellanox Technol Hakidma 23 IL-2069200 Yokneam Israel Conzorcio Nazl Interuniv Telecommunicaz CNIT Natl Lab Photon Networks Via G Moruzzi 1 I-56124 Pisa Italy Hiro MicroDataCtr Voorburg Netherlands Dell EMC Cork Ireland

ISBN: (纸本)9781728184234

The delivery of computing services including storage, databases, networking, software, analytics, intelligence and more is moving from the cloud towards the edge;among all technical requirements, the edge needs to be able to natively support artificial intelligence processes to enable new technological paradigms, such as smart Factory 4.0, healthcare assisted living, smart cities and advanced robotics, for example In this paper, we will present the enabling technologies to will be developed within the "Big data processing and artificial intelligence at the network edge" (BRAINE) project, one of the largest joint activities in Europe in the area of edge computing enabling artificial intelligence. BRAINE covers the full stack of a system solution, from subsystem integration up to artificial intelligence service provisioning, and includes four use cases of direct impact in the European ecosystem.

关键词： edge computing artificial intelligence computing infrastructure extreme scale analytics

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