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A Novel Edge-Supported Cost-Efficient Resource Management Approach for smart Grid System

A Novel Edge-Supported Cost-Efficient Resource Management Ap...

引用

international conference on computing, analytics and Networking (ICCAN)

作者： Mishra, Jyotirmaya Sheetlani, Jitendra Reddy, K. Hemant K. Roy, Diptendu Sinha Gandhi Inst Engn & Technol Dept Comp Sci & Engn Gunupur India Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci & Engn Sehore Madhya Pradesh India Natl Inst Sci & Technol Dept Comp Sci & Engn Berhampur India Natl Inst Technol Dept Comp Sci & Engn Shillong Meghalaya India

ISBN: (纸本)9789811078712;9789811078705

The smart grids, a new-generation power supply system, have the capacity to lowering the cost, can increase service provision tremendously, and make surroundings greener as compared to conventional power supply systems. To interact with the physical world and widen its capabilities, integrated smart grid cyber-physical system (SG-CPS) can be used for computation, communication, and control. To support smart grid (SG), cloud components are employed for storing and processing users' power demand and control flow information generated at different control components like smart meter (SM), home energy management (HEM), phasor measurement units (PMUs), and soon. But storing smart grid data to cloud and processing incurs unacceptable delays. This paper addresses quality-of-service (QoS) requirements of SGs by integrating fog computing along with cloud computing infrastructure for realizing an Edge computing integrated smart Grid (EC-iSG). To that end, this paper presents novel heuristics for resource management of such integrated infrastructure that accounts for parameters such as uplink and downlink communication costs, cost for VM deployment, and cost for communicating among base stations. The results presented demonstrate the efficacy of the proposed methodology.

关键词： smart grid Fog computing Cloud computing Cost optimization HEM PMU

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BITS: Blockchain based intelligent Transportation System with Outlier Detection for smart City 18

BITS: Blockchain based Intelligent Transportation System wit...

引用

IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Maskey, Shirshak Raja Badsha, Shahriar Sengupta, Shamik Khalil, Ibrahim Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA RMIT Univ Dept Comp Sci & Software Engn Melbourne Vic Australia

ISBN: (纸本)9781728147161

With the rise of smart cities, transportation systems are getting smarter every day. An intelligent Transportation System (ITS) should be secure, autonomous, capable of discerning safeness levels at the roads, and provide services to improve human experience. To reach the gold standard, the ITS faces several issues such as centralization, trust, and data integrity. The Transportation System and the data generated from the vehicles can be intercepted, manipulated and corrupted with coordinated attacks. Moreover, every system might have bad actors who want to manipulate the system or data to his or her favor by exploiting the system. In order to guarantee data integrity, immutability, and availability for the ITS, we propose Blockchain based architecture with outlier detection to prevent malicious activity by the vehicles while preserving integrity in sharing information. The Outlier Detection is designed to reside before the consensus process, to identify and prevent participation of malicious vehicles in consensus process or block mining. In our proposed Blockchain based intelligent Transportation system with Outlier Detection for smart City (BITS), we used machine learning to detect the anomaly in the data. The proposed model can be used in various applications of ITS such as traffic monitoring, criminal activity profiling, accident detection and reporting, etc.

关键词： Anomaly detection

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Big data analytics in Health Care 2nd

Big Data Analytics in Health Care

引用

2nd international conference on computing, Communications and data Engineering (CCODE)

作者： Fatima, Tahmeena Jyothi, Singaraju SPMVV Dept Comp Sci Tirupati Andhra Pradesh India

ISBN: (纸本)9789811501357;9789811501340

In today's world, data is growing exponentially and widespread accessibility of data led to analyze and visualize data effectively using analytical techniques in healthcare industry. Big data analytics play a vital role and provides long-term benefits in tremendously handling huge explosive data. In this paper, we present an overview of different big data platform tools and different technologies that support big data analytics in health care. It also describes different steps involved in big data analytics process and also presents ways to improve health care by considering various facts by using big data analytics. As big data analytics has the potential to provide useful insight in health care, this article uses a review methodology to categorize the uses of big data in health care. This study provides a baseline to assess the essential prospects of computational health informatics and the use of big data in health care in understanding different scopes of big data platforms.

关键词： Big data Healthcare Big data analytics Review of big data platforms and tools

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intelligent Deep Reinforcement Learning based Resource Allocation in Fog network 26

Intelligent Deep Reinforcement Learning based Resource Alloc...

引用

26th international conference on High Performance computing, data and analytics (HiPCW)

作者： Divya, V Sri, Leena R. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai Tamil Nadu India

ISBN: (纸本)9781728148946

The increase in the smart devices have brought about the intelligence to the applications at the edge. The current day devices are rich in computations and communications which are in most cases are left under-utilized. These devices can be used to provide edge intelligence when deployed for computations on the streaming data from the IoT devices. To reduce the latency of sending the data to the cloud in case of real-time applications, a new paradigm of edge computing was introduced which meets the emerging challenge of handling latency aware applications on the fly. The other research issue to be handled with utmost importance is the load balancing which can be realized effectively by proper proactive resource allocation. Our proposed work involves the construction of SDN based Fog infrastructure where in the research issue was tested and evaluated. The proposed Deep Reinforcement Learning algorithm helps in intelligent action selection based on the past experience data and the dynamic network parameters. Finally, the proposed work was compared with the state of the art OSPF algorithm in terms of service time and the load variance on increasing task allocation.

关键词： intelligent Fog Infrastructure Software Defined Networking Deep Reinforcement Learning Resource allocation

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An Enterprise Architecture based on Cloud, Fog and Edge computing for an Airfield Lighting Management System 24

An Enterprise Architecture based on Cloud, Fog and Edge Comp...

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24th IEEE international Enterprise Distributed Object computing conference (IEEE EDOC)

作者： Mijuskovic, Adriana Bemthuis, Rob Aldea, Adina Havinga, Paul Univ Twente Enschede Netherlands

ISBN: (数字)9781728164717

ISBN: (纸本)9781728164717

Followed by the introduction of IoT and new sustainable technologies, energy management, Quality of Service and decrease of communication costs become important and complex for enterprise systems at airports. The aviation authorities' reports reveal that the airport ICT investments are mainly focused on travel safety, mobile commerce, and new technologies. The main idea behind a smart airport is to deploy IoT network managed through a Cloud-Fog-Edge paradigm for a smart platform and optimize the airport's efficiency. An IoT cloud-based platform solution supports multiple types of data, advanced analytics, artificial intelligence, and machine learning techniques. However, cloud computing has certain limitations such as increased delay in data reporting, increased latency in accessing user network, limited customization, increased reliance on external network and data privacy. Fog-Cloud and Edge-Cloud paradigms can overcome the weaknesses of cloud computing architectures. Therefore, to understand the organizational impact of combining the usage of cloud, fog, and edge computing, we created an enterprise architecture that can be applied in a smart airport demonstration study. The enterprise architecture modelling was done by using ArchiMate and validated by means of an expert assessment and prototype implementation.

关键词： enterprise architecture smart airport solutions cloud computing fog computing edge computing

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An Efficient Edge Analytical Model on Docker Containers for Automated Monitoring of Public Restrooms in India

An Efficient Edge Analytical Model on Docker Containers for ...

引用

IEEE international conference on Advanced Networks and Telecommunications Systems (IEEE ANTS)

作者： Gore, Rajasi Banerjea, Shashwati Tyagi, Neeraj Saurav, Sanu Acharya, Deepesh Verma, Vishesh Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Dept Prayagraj 211004 Uttar Pradesh India

ISBN: (纸本)9781728192901

The Government of India proposed "Clean India Mission" in 2014 that has led to the establishment of many public restrooms across the country. The purpose of this mission is to provide hygienic toilets to prevent open defecation and eradicate water-borne diseases. These restrooms are assigned human resources. However, the majority of them are in bad condition for usage. For increasing the availability of toilets, there is a requirement of a smart sanitation system for its automated monitoring. Moreover, rural and semi-urban restroom locations suffer from limited network connectivity. Thus, restroom state analysis needs to be performed near the site for seamless monitoring. This paper proposes an edge analytics framework based on the Internet of Things (IoT) and fog computing to analyze restroom data at the network edge and provide intelligent decisions. The machine learning model runs over Docker containers for analyzing data for the resource-constraint fog nodes. The choice of a machine learning model for the analysis depends on the model's performance in classification and on Docker containers over Raspberry Pi. Results show Adaptive Neuro-Fuzzy Inference System (ANFIS) is suitable over Docker containers for handling restroom data at the edge of the network.

关键词： Internet of Things Fog computing Edge analytics Raspberry Pi Docker Container

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Towards Agent-Based Traffic Simulation Using Live data from Sensors for smart Cities 1

引用

20th international Workshop on Multi-Agent-Based Simulation (MABS)

作者： Qian, Yan Barthelemy, Johan Perez, Pascal Univ Wollongong SMART Infrastruct Facil Wollongong NSW Australia

ISBN: (数字)9783030668884

ISBN: (纸本)9783030668877;9783030668884

The smart City and Internet-of-Things revolutions enable the collection of various types of data in real-time through sensors. This data can be used to improve the decision tools and simulations used by city planners. This paper presents a new framework for real-time traffic simulation integrating an agent-based methodology with live CCTV and other sensor data while respecting the privacy regulations. The framework simulates traffic flows of pedestrians, vehicles and bicycles and their interactions. The approach has been applied in Liverpool (NSW, Australia) showing promising preliminary results and can easily ingest additional sensor data, e.g. air quality.

关键词： Traffic simulation Agent-based modelling data-driven simulation Edge computing smart city intelligent video analytics

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Transactional data analytics for Inferring Behavioural Traits in Ethereum Blockchain Network 16

Transactional Data Analytics for Inferring Behavioural Trait...

引用

IEEE 16th international conference on intelligent Computer Communication and Processing (ICCP)

作者： Bhargavi, M. S. Katti, Sushmitha M. Shilpa, M. Kullcami, Vaishnavi P. Prasad, Supraja Bangalore Inst Technol Dept Comp Sci & Engn Bengaluru Karnataka India

ISBN: (纸本)9781728190808

Ethereum is a blockchain based development platform for users to build and deploy decentralized applications and smart contracts. Transactions substituting money over Ethereum space are carried out using Ethereum's cryptocurrency 'Ether'. Though the decentralization and public ledger recording of the transactions proves its limpidity, the anonymity of the users, hiding their true identity behind the addresses echoes the need for discerning the behavioural traits in transactions. The appalling nature of Ethereum transactions to have both, security and threat at a comparable level, demands analytics for inferring traits for better perspectives and insights. This research work focuses on characteristic analysis of Ethereum transaction space for inferring behavioural traits in supervised and unsupervised context. In an unsupervised environment, raw transaction data is extradited to a tabular transaction structure by selecting appropriate features. The data is further clustered and validated to form coherent groupings of similar patterns. The clusters are characteristically analyzed based on the features through Radar plots for inferring behavioural traits. In supervised context, labelled transactions are represented using histograms and feature-based characteristic analysis is performed to infer traits. Such analytics lay foundations for future deeper analysis that can be used to discover trends and patterns to understand the transaction network, enhancement of trading strategies, identification of but activities, anomaly detection and several others.

关键词： Ethereum Blockchain analytics Clustering Behaviour

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Visual Object Detection based LiDAR Point Cloud Classification

Visual Object Detection based LiDAR Point Cloud Classificati...

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IEEE international conference on Big data and smart computing (BigComp)

作者： Muhammad, Sualeh Gon-Woo, Kim Chungbuk Natl Univ Intelligent Robot Lab Control & Robot Engn Dept Cheongju South Korea

ISBN: (纸本)9781728160344

The environmental perception plays a pivotal role in autonomous driving tasks and demands robustness in cluttered dynamic environments such as complex urban scenarios. LiDAR being one of the popular perceptual sensor, suffers with large number of inaccurate object detections. This work is an extension of an ongoing research on multiple object detection and tracking. Where, Neural Network based approach is considered for visual detection to aid the LiDAR point cloud processing, and to address the inherent shortcoming of the sensor. It is inferred that the proposed framework would perform in real-time on an embedded platform. In addition, the separate processing of visual and LiDAR sensor data will enable switching to a light weight LiDAR only setup in runtime when required.

关键词： Object detection LiDAR visual-LiDAR calibration point cloud classification

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Predictive and Prescriptive analytics on Clinical data using Improved K-NNtechnique

Predictive and Prescriptive Analytics on Clinical Data using...

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2022 international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2022

作者： Kunjachen, Liza M Kavitha, R. SRM Institute of Science & Technology Department of Information Technology Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

In recent years, the impactof pandemic situation there is a strong requirementoflateral thinking within the Healthcare sector. There is a consistent hesitancy for people to use hospital services because of social distancing, covid 19 circumstances, people prefer telemedicine system are rapidly increased. Health care provider provides with better treatment. Anartificial intelligence based dataanalytics shared by the patient through the IoT devices provides high security. In case of any emergencies Health care provider must be able to access the AI based Electronic Health records immediately and consuming less time in taking decisions before the patient could arrive. In this proposed improved KNN technique, preprocessing step is to produce an accurateoutput with additional efficient data and classification enhancement. The experimental results show that the improvedK-Nearest Neighbor (KNN) algorithm improves the accuracy and efficiency of *** from this, Trend analysis can be done to understand the various disease outbreaks which come under the data *** experimental results of the proposed technique foradvanced data analytics in healthcaretreatment helps better treatment at low cost efficiently and effectively. © 2022 IEEE.

关键词： data analytics

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