scholarly journals Towards Energy-Efficient Framework for IoT Big Data Healthcare Solutions

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Chong Feng ◽  
Muhammad Adnan ◽  
Arshad Ahmad ◽  
Ayaz Ullah ◽  
Habib Ullah Khan
Keyword(s):  
Big Data ◽  
Fog Computing ◽  
Wearable Sensors ◽  
Data Communication ◽  
Energy Usage ◽  
Process Data ◽  
Data Infrastructure ◽  
Efficient Storage ◽  
Huge Data ◽  
The Internet Of Things

The aim of the Internet of things (IoT) is to bring every object (wearable sensors, healthcare sensors, cameras, home appliances, smart phones, etc.) online. These different objects generate huge data which consequently lead to the need of requirements of efficient storage and processing. Cloud computing is an emerging technology to overcome this problem. However, there are some applications (healthcare) which need to process data in real time to improve its performance and require low latency and delay. Fog computing is one of the promising solutions which facilitate healthcare domain in terms of reducing the delay multihop data communication, distributing resource demands, and promoting service flexibility. In this study, a fog-based IoT healthcare framework is proposed in order to minimize the energy consumption of the fog nodes. Experimental results reveal that the performance of the proposed framework is efficient in terms of network delay and energy usage. Furthermore, the authors discussed and suggested important services of big data infrastructure which need to be present in fog devices for the analytics of healthcare big data.

scholarly journals IoT-Fog-Cloud system architecture for big data analysis and cybersecurity systems: a review of fog computing, the introduction to the Internet of Things audit

2020 ◽  
Author(s):  
D. R. Kolisnyk ◽  
◽  
K. S. Misevych ◽  
S. V. Kovalenko
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
System Architecture ◽  
Fog Computing ◽  
Big Data Analysis ◽  
Future Research ◽  
The Internet ◽  
Security Issues ◽  
The Internet Of Things

The article considers the issues of system architecture IoT-Fog-Cloud, considers the interaction between the three levels of IoT, Fog and Cloud for the effective implementation of programs for big data analysis and cybersecurity. The article also discusses security issues, solutions and directions for future research in the field of the Internet of Things and nebulous computing.

Data Mining for the Internet of Things: Literature Review and Challenges

2021 ◽  
Vol 9 (VII) ◽  
pp. 3351-3362
Author(s):  
Dr. Mohd Zuber
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Internet Of Things ◽  
The Internet ◽  
Mining System ◽  
Research Issues ◽  
Data Mining Algorithms ◽  
Huge Data ◽  
Mining Algorithms ◽  
The Internet Of Things

The huge data generate by the Internet of Things (IOT) are measured of high business worth, and data mining algorithms can be applied to IOT to take out hidden information from data. In this paper, we give a methodical way to review data mining in knowledge, technique and application view, together with classification, clustering, association analysis and time series analysis, outlier analysis. And the latest application luggage is also surveyed. As more and more devices connected to IOT, huge volume of data should be analyzed, the latest algorithms should be customized to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

scholarly journals Power of Big Data System for Storing and Processing Huge Data

2019 ◽  
pp. 138-142
Author(s):  
S. Natarajan ◽  
S. Rajarajesware ◽  
Suresh Ram R
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data System ◽  
Data Systems ◽  
Data Infrastructure ◽  
Work From Home ◽  
Architecture Framework ◽  
Huge Data ◽  
The People

Big data uses storage of huge data with some approaches and techniques to manage and process them. During the past few years the number of persons using internet, email and other internet-based applications has been growing tremendously. Big Data is mainly characterized by 3V’s (Volume, Velocity and, Variety). The Big Data Architecture Framework (BDAF) is proposed to address all aspects of the Big Data Ecosystem. BDAF includes components such as Big Data Infrastructure, Big Data Analytics, Data structures & models, Big Data Lifecycle Management and Big Data Security. Nowadays the volume of data used by the people throughout the world is increasing enormously and exponentially. So, the need for storing, processing and protecting large volume of data has been becoming a great challenge in the modern hyper-connected world. On the basis of work from home concept lot of software professionals are doing their jobs with their internet connected systems for development, implementation, testing and maintenance of various softwares. These professionals and experts are sending and receiving lot of data to various locations to their clients, higher authorities and other officials frequently depending upon their requirements. The traditional data management models are not efficient for today’s exponentially growing data from variety of industries. This challenging task of storing and managing huge volume of data is achieved in Big Data Systems. In this paper we try to give an overview of Big Data Analytics system for storing and processing huge volume of various types of data. Overwhelming the security threats due to various factors like viruses, worms, etc are also great challenges to protect huge volume of data in a big data system.

scholarly journals Big Data Security Architecture using Split and Merge Method

2018 ◽  
Vol 11 (1) ◽  
pp. 268
Author(s):  
Archana RA ◽  
Ravindra S Hegadi ◽  
Manjunath T N
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Retrieval ◽  
Security Architecture ◽  
Volume Data ◽  
Source Information ◽  
Process Data ◽  
Data Infrastructure ◽  
User Data ◽  
Split And Merge

<p>Due to rapid growth of unstructured data in contemporary information world, there is an essence of big data infrastructure for many applications spread across domains, due to the different source information type and huge volume, data ingestion and data retrieval is important activity during this process data security is a vital to protect user data, in connection with this, authors proposed a big data security architecture using split and merge security method in big data environment using hadoop.This work will help Data security professionals and organizations implementing big data projects.</p>

scholarly journals Energy and Depression Management System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1902-1907
Author(s):  
Gaurav Soni
Keyword(s):  
Web Application ◽  
Energy Requirement ◽  
Emerging Technology ◽  
The Internet ◽  
Efficient Storage ◽  
Huge Data ◽  
Daily Energy ◽  
Depression Management ◽  
Mental And Physical Health ◽  
The Internet Of Things

The aim of the Internet of things (IoT) is to bring every object online. These different objects generate huge data which consequently lead to the need of requirements of efficient storage and processing. Cloud computing is an emerging technology to overcome this problem. The pandemic due to COVID-19 has caused great impact on people’s approach to have proper lifestyle. People these days are found inactive, unhappy and less energetic, because of their busy routine and continual ignorance of overall health. By keeping a track of their mental and physical health, one could achieve better response and hence expected lifestyle. Our solution is to detect, analyze and deliver a solution to treat depression and assist people with fulfilling their daily energy requirement for being more active and enthusiastic. Our solution is a Soft-Ui Web Application that gives smooth UI/UX experience to users showcasing fluctuations in energy and playing games to get cognitive features’ result. The hardware is a wearable wrist band made with NodeMCU embedded with accelerometer and heart rate sensors. An analytical report is generated and updated in real time and user could download as per their convenience.

Fault-Tolerant Strategies in the Tree-Based Fog Computing Model

2020 ◽  
Vol 11 (4) ◽  
pp. 72-91
Author(s):  
Ryuji Oma ◽  
Shigenari Nakamura ◽  
Tomoya Enokido ◽  
Makoto Takizawa
Keyword(s):  
Fault Tolerant ◽  
Fog Computing ◽  
Minimum Energy ◽  
Sensor Data ◽  
The Internet ◽  
Process Data ◽  
General Process ◽  
Computing Model ◽  
The Internet Of Things ◽  
Parent Node

In the Fog Comput$ing (FC) model of the Internet of Things (IoT), application processes to handle sensor data are distributed to fog nodes and servers. In the Tree-based FC (TBFC) model proposed by the authors, fog nodes are hierarchically structured. In this article, the authors propose a TBFC for a General Process (TBFCG) model to recover from the faults of fog nodes. If a node gets faulty, the child nodes are disconnected. The authors propose Minimum Energy in the TBFCG tree (MET) and selecting Multiple Parents for recovery in the TBFCG tree (MPT) algorithms to select a new parent node for the disconnected nodes. A new parent node has to process data from not only the disconnected nodes, but also its own child nodes. In the evaluation, the energy consumption and execution time of a new parent node can be reduced by the proposed algorithms.

Big Data and Its Visualization With Fog Computing

2018 ◽  
Vol 1 (2) ◽  
pp. 51-82 ◽  
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Fog Computing ◽  
Real Data ◽  
The United States ◽  
United States Department ◽  
Highway Traffic ◽  
Process Data ◽  
National Highway Traffic Safety ◽  
Processing Application

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This article discusses what is Big Data, and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. Big Data can be discrete or a continuous stream of data, and can be accessed using many types and kinds of computing devices ranging from supercomputers, personal work stations, to mobile devices and tablets. Discussion is presented of how fog computing can be performed with cloud computing as a mechanism for visualization of Big Data. An example of visualization techniques for Big Data transmitted by devices connected by Internet of Things (IoT) is presented for real data from fatality analysis reporting system (FARS) managed by the National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation (USDoT). Big Data web-based visualization software are discussed that are both JavaScript-based and user interface-based. Challenges and opportunities of using Big Data with fog computing are also discussed.

scholarly journals Lightweight Coordination Patterns for Applications of the Internet of Things

2020 ◽  
Vol 25 (2) ◽  
pp. 117-123
Author(s):  
Waseem Akhtar Mufti
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Internet Of Things ◽  
Computer Network ◽  
Programming Model ◽  
Data Communication ◽  
The Internet ◽  
Automatic Data ◽  
Distributed Cluster ◽  
The Internet Of Things

AbstractApplications of the Internet of Things (IoT) are famously known for connecting devices via the internet. The main purpose of IoT systems (wireless or wired) is to connect devices together for data collection, buffering and data gateway. The collected large size of data is often captured from remote sources for automatic data analytics or for direct decision making by its users. This paper applies the programming pattern for Big Data in IoT systems that makes use of lightweight Java methods, introduced in the recently published work on ClientNet Distributed Cluster. Considering Big Data in IoT systems means the sensing of data from different resources, the network of IoT devices collaborating in data collection and processing; and the gateways servers where the resulting big data is supposed to be directed or further processed. This mainly involves resolving the issues of Big Data, i.e., the size and the network transfer speed along with many other issues of coordination and concurrency. The computer network that connects IoT may further include techniques such as Fog and Edge computing that resolve much of the network issues. This paper provides solutions to these problems that occur in wireless and wired systems. The talk is about the ClientNet programming model and its application in IoT systems for orchestration, such as coordination, data communication, device identification and synchronization between the gateway servers and devices. These devices include sensors attached with appliances (e.g., home automations, supply chain systems, light and heavy machines, vehicles, power grids etc.) or buildings, bridges and computers running data processing applications. As described in earlier papers, the introduced ClientNet techniques prevent from big data transfers and streaming that occupy more resources (hardware and bandwidth) and time. The idea is motivated by Big Data problems that make it difficult to collect it from different resources through small devices and then redirecting it. The proposed programming model of ClientNet Distributed Cluster stores Big Data on the nearest server coordinated by the nearest coordinator. The gateways and the systems that run analytics programs communicate by running programs from other computers when it is essentially required. This makes it possible to let Big Data rarely move across a communication network and allow only the source code to move around the network. The given programming model greatly simplifies data communication overheads, communication patterns among devices, networks and servers.

scholarly journals An Extensive Analysis on Big Data

2021 ◽  
pp. 591-595
Author(s):  
M. Lakshmi Priya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
The Internet ◽  
Extensive Analysis ◽  
Huge Data ◽  
Heterogeneous Datasets ◽  
Different Sources ◽  
The Internet Of Things

In recent years, data has rapidly developed because of the growth of the internet, the internet of things, cloud computing, and various technologies. The size of data processed and transmitted over the internet is drastically increasing. Big data refers to a database that handles huge data in real-time yet growing exponentially with time. Big data analytics uses advanced techniques on large heterogeneous datasets that are collected from different sources, and in various sizes. Big data can manage and process the data beyond the ability of a relational database.

scholarly journals Introduction to Urban Big Data Infrastructure

2021 ◽  
pp. 543-545
Author(s):  
Michael F. Goodchild
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Social Issues ◽  
Geospatial Data ◽  
The Internet ◽  
Data Infrastructure ◽  
Spatial Search ◽  
3D Data ◽  
The Social ◽  
The Internet Of Things

AbstractThis chapter provides a brief introduction to Part IV of the book and its focus on urban big data infrastructure. Eight chapters (Chaps. 31 to 38) explore the various dimensions of the topic, ranging from massive archives of 3D data and the Internet of Things to spatial search and the social issues of privacy that are raised by big geospatial data.

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