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BeaCloud: A Generic Architecture for Sustainable smart City using Bluetooth Beacons 22

BeaCloud: A Generic Architecture for Sustainable Smart City ...

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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： Hasan, Raiful Hasan, Ragib University of Alabama at Birmingham Dept. of Computer Science BirminghamAL35294 United States

ISBN: (纸本)9781728176499

In recent years, Bluetooth beacons have been widely used in numerous application domains, including smart cities, assistive technologies, and intelligent transportation management. Researchers or developers associated with these domains frequently require diverse systems to implement or test their prototype related innovations. They need to deploy beacons for the specific environment every time;such customized systems typically cannot often be reused. Hence, the cost of implementation increases, and multiple systems generate redundant data. In this paper, we propose BeaCloud-an architecture which provides a common platform of multiple beacon-based systems. BeaCloud enables inter-system communication and allows easy and secure access to data for the system administrators. The proposed architecture presents a cost-effective model that offers reduced cost and hardware. Also, BeaCloud reduces the data redundancy by up to 40%. To demonstrate the feasibility of BeaCloud, we implemented a testbed of three testing sites and evaluated the system's performance. © 2020 IEEE.

关键词： smart city

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Management Innovation of Network Society Based on Big data

Management Innovation of Network Society Based on Big Data

引用

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

作者： Zhao, Yue Department of Economics and Management Yunnan Technology and Business University KunmingYunnan China

ISBN: (纸本)9783030627423

In the process of rapid development, due to the imperfection of the relevant system structure, the network society management and the development of network information cannot keep up with the matching facilities, which is a serious threat. The management of the network society. The research purpose of this paper is to study the safety and innovation methods of network society management in the continuous development of big data and network. This article analyzes the network system, file transfer and remote call (Telnet), SQL injection and SQL vulnerabilities, DDOS attacks and other network technologies. And the analysis of network management technical measures in which VLAN technology strengthens network management, which strengthens network management through VLAN technology, and improves network security performance. Through the binding of IP address and MAC address to the computers in the entire network that use static IP access. It can solve the problems of network social management and propose innovative points and innovative methods. The experimental research results show that the establishment of an effective network social management security defense system is of great value to the security of users and network applications. Through comprehensive network social management education to improve the security awareness of network users, a complete Network security system. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Network management

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An Ensemble Learning Approach for Privacy-Quality-Efficiency Trade-Off in data analytics

An Ensemble Learning Approach for Privacy-Quality-Efficiency...

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2020 international conference on smart Electronics and Communication, ICOSEC 2020

作者： Peethambaran, Geetha Naikodi, Chandrakant Suresh, L. Cambridge Institute of Technology Dept of Cse Bangalore India Davangere India Cambridge Institute of Technology Bangalore India

ISBN: (纸本)9781728154619

Privacy is an issue of concern in the electronic era where data has become a primary source of investment for businesses and organizations. The value generated from data is put to use in a number of ways for economic benefit. Customer profiling is one such instance, where data collected is used for targeted marketing, personalized purchase recommendations and customized product deliveries. In such applications, the risk of individual sensitive information disclosure always prevails, affecting the privacy of individuals involved. Hence privacy preserving analysis demands suppressing or transforming data before it is published for analysis, thus curbing data leak. Subsequently, data quality degrades, and operative analytics is affected. With Big data, algorithms that offer a reasonable qualityprivacy trade off need enhancements in terms of efficiency and scalability. In this paper, the work proposed uses a privacy based composite classifier model to analyze the accuracy of classification. The diverse characteristics of algorithms in the composite classifier are found to balance the classification accuracy that is likely to get affected by privacy model. Further, the model's performance with respect to execution time is then evaluated using the parallel computing framework Spark.. © 2020 IEEE.

关键词： Efficiency

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Automatic Classification of Carnatic Music Instruments Using MFCC and LPC 3rd

Automatic Classification of Carnatic Music Instruments Using...

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international conference on data Management, analytics and Innovation (ICDMAI)

作者： Shetty, Surendra Hegde, Sarika NMAM Inst Technol Nitte Dept Comp Applicat Deralakatte 574110 Karnataka India NMAM Inst Technol Nitte Dept CSE Deralakatte 574110 Karnataka India

ISBN: (纸本)9789813299498;9789813299481

With a large collection of digital music in recent days, the challenge is to organize and access the music efficiently. Research in the field of Music Information Retrieval (MIR) focuses on these challenges. In this paper, we develop a system which automatically identifies the instrument in a given Carnatic music on ten different types of instruments. We extract the well-known features namely, MFCC and LPC, and analyze the capability of these features in distinguishing different instruments. Then, we apply, the classification techniques like, Artificial Neural Network, Support Vector Machine, and Bayesian classifiers on those features. We compare the performances of those algorithms along with different features for Carnatic music instruments identification.

关键词： Audio data mining Carnatic music Instrument classification MIR

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Deep Q-learning Approach for Congestion Problem In smart Cities 4

Deep Q-learning Approach for Congestion Problem In Smart Cit...

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4th international conference on intelligent computing in data Sciences (ICDS)

作者： Faqir, Nada En-Nahnahi, Noureddine Boumhidi, Jaouad Sidi Mohamed Ben Abdellah Univ Fez LISAC Lab Fes Morocco

ISBN: (纸本)9781728180847

Traffic congestion is a critical problem in urban area. In this study, our objective is the control of traffic lights in an urban environment, in order to avoid traffic jams and optimize vehicle traffic;we aim to minimize the total waiting time. Our system is based on a new paradigm, which is deep reinforcement learning;it can automatically learn all the useful characteristics of traffic data and develop a strategy optimizing adaptive traffic light control. Our system is coupled to a microscopic simulator based on agents (Simulation of Urban MObility - SUMO) providing a synthetic but realistic environment in which the exploration of the results of potential regulatory actions can be carried out.

关键词： Traffic control Adaptive systems traffic optimization urban mobility

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Research on the Development Trend of Online Education Industry Considering the Influence of Big data and Artificial Intelligence

Research on the Development Trend of Online Education Indust...

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international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Fu, Yong Sichuan Agr Univ Yaan 625014 Sichuan Peoples R China

ISBN: (纸本)9783030152352;9783030152345

With the concept of lifelong education being widely recognized, people's demand for education and training is growing, and online education has gradually become one of the important ways for people to meet the needs of education and training. Through analysis, it is found that online education also faces the development bottleneck of the lack of on-site teaching sense, systemic needs to be improved, the course completion rate is low, the quality of the course is difficult to guarantee, the teacher's teaching pressure is high, and lack of social recognition, which restricts the further development. With the help of current technology hotspots - big data and artificial intelligence, we can effectively break through the above bottlenecks and make the online education industry maintain rapid development. This paper analyzes the root causes of the problems in the current online education and discusses the solutions to improve online education.

关键词： Online education industry Big data Artificial intelligence Online education development trend

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Trust Provisioning in the Transport Infrastructure 3rd

Trust Provisioning in the Transport Infrastructure

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3rd international conference on intelligent Human Systems Integration (IHSI) - Integrating People and intelligent Systems

作者： Cadzow, Scott C3L Cryptalabs London England

ISBN: (纸本)9783030395124;9783030395117

In any security process the role of trust anchors and trust roots have a complex interaction and applying them to the transport domain where many different forms of trust relationship have to work together is going to be one of greatest of the problems to surmount. ITS and CAV are data centric information systems in which the provenance of the data in the system is key to the success of the system. Provenance and integrity are underpinnings of trust, but in CAV and ITS there is no a priori knowledge of the source and value of data. This means that the provenance of a signal or message from the infrastructure is unlikely to be tested in advance and the verification of trust needs to be applied on demand for each message. Determining trust distribution in the transport infrastructure is one of the biggest unanswered questions regarding the viability of CAV.

关键词： Integrity Trust Provenance Connected and Autonomous Vehicles (CAV) intelligent Transport Systems (ITS) Trust networks smart infrastructure

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Electrical Internet of Things - EIoT: A Platform for the data Management in Electrical Systems 4th

Electrical Internet of Things - EIoT: A Platform for the Dat...

引用

Future Technologies conference (FTC)

作者： Gil, Santiago Zapata-Madrigal, German D. Garcia-Sierra, Rodolfo Univ Nacl Colombia Fac Minas Grp T&T Medellin Colombia Enel Codensa SA ESP Enel Grp Bogota Colombia

ISBN: (纸本)9783030325237;9783030325220

The arrival of the IoT has benefited multiple industrial sectors;one of them is the electrical industry. It makes the IoT an attractive platform for the smart grid, because it improves the monitoring, analysis, availability, autonomy, and control of grid systems, from distribution to transmission. In this work, it is proposed the Electrical Internet of Things - EIoT, a platform for the data management in electrical systems. The EIoT platform relies on the LPWAN technologies to connect electrical things geographically distributed. This approach attempts to cover Fire Protection Systems in Substations, Control and Monitoring for the Demand Management, Electrical Asset Management, and intelligent Power Management and Monitoring in Refrigerators applications, but other smart grid applications fit as well. The EIoT platform implemented LoRa, a LPWAN technology, and its LoRaWAN protocol to connect electrical elements remotely in a first stage. The prototyping phase of the EIoT platform was developed using Mbed-enabled LoRa nodes coupled to electrical elements with the corresponding instrumentation, a LoRaWAN network, and a custom application server;then, all the components of an IoT network were considered. Some results of the implementation are presented, which demonstrate the suitability of the EIoT platform to address several applications of the electrical industry. The results also expose promising opportunities to deal with data processing and analytics, integrate more IoT protocols, and consolidate a complete IoT ecosystem in the electrical industry and the smart grids in further researches.

关键词： IoT LPWAN Electrical and power systems Control and Monitoring Fire protection systems smart grids

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BRF: Network Traffic Analysis based on Bagging Ensemble with Random Forest Classifier

BRF: Network Traffic Analysis based on Bagging Ensemble with...

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international conference on Contemporary computing and Informatics (IC3I)

作者： N. Deeban P.Shyamala Bharathi Department of VLSI Microelectronics Institute of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

The fifth-generation (5G) network provides support for a wide variety of systems, including applications that demand the highest level of security as well as dependable communication. Because of recent advancements in smart devices, we are currently experiencing an explosion in the generation of data as well as a heterogeneity that necessitates the development of new network solutions for improved traffic analysis and comprehension. In order to automatically manage the massive amounts of data, these solutions need to be both intelligent and scalable. It is now feasible and easy to deploy machine learning (ML) to solve complex problems, and its effectiveness has been validated in a number of different domains. This is due to the progress that has been made in high-performance computing. It is anticipated that the deployment of 5G wireless communication systems will start in the year 2020. The traffic management for 5G networks will present significant technical challenges due to the introduction of new use cases, new technologies, and new network architectures. In this paper, we have concentrated on analyzing network data traffic for 5G Network using an ensemble method known as Bagging ensemble with Random Forest. Bootstrap sampling is utilized in the standard ensemble method known as “bagging.” The random forest technique is an improvement on the bagging method that can result in better variable selection. First, we will go over the concept of bagging, and then we will go over the enhancement that brought about random forest. The random forest algorithm is objective because it consists of multiple trees, each of which is trained on a different subset of the data. When you have both categorical and numerical features to work with, the random forest algorithm performs particularly well. On the basis of the test data, the classification accuracy is an average of 96 percent, significantly outperforming other methods.

关键词： 5G mobile communication Telecommunication traffic Network architecture Classification algorithms Ensemble learning Security smart devices

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The Effect of Big data on the Quality of Decision-Making in Abu Dhabi Government Organisations 3rd

The Effect of Big Data on the Quality of Decision-Making in ...

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international conference on data Management, analytics and Innovation (ICDMAI)

作者： Alkatheeri, Yazeed Ameen, Ali Isaac, Osama Nusari, Mohammed Duraisamy, Balaganesh Khalifa, Gamal S. A. Lincoln Univ Coll Petaling Jaya Selangor Malaysia

ISBN: (纸本)9789811393648;9789811393631

One of the important research topics and areas that is attracting significant interest and attention globally is 'big data'. While big data contribute towards the quality of decision-making, it also assists in the development of and extending the knowledge in this area by harnessing available technology. This research presents and discusses the literature related to the quality of big data and its impact on the quality of decision-making. Adescriptive methodology approach was also adopted by reviewing the literature of published and unpublished scientific research along with a survey in the form of a questionnaire involving participants from Abu Dhabi Police Agencies to collect their views and opinions in this area. The results from the literature review and survey led to proposing a theoretical, conceptual model according to the quantitative and numerical methodology. The findings of this research have revealed that the quality of big data predicts the quality of decision-making and that the quality of big data in Abu Dhabi Governmental Organisations (ADGO) plays a significant role in the quality of decision-making.

关键词： Big data Information systems Mediating factor ADGO

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