Development of big data and IoT is rapidly increasingly and affecting all the major technologies and the business by increasing the benefits for the individual and organisations. The increasing growth of data of IoT devices has played a major role for use of Big Data. Big data is categorize into three aspects (a) Variety (b) Volume (c) Velocity. These categories are introduced by Gartner to describe the elements of big data challenges. Various opportunities are presented by the capability to analyze and utilize huge amounts of IoT data, including applications in smart cities, smart transport and grid systems, energy smart meters, and remote patient healthcare monitoring devices.
The more popularity of Internet of Things day by day has made a big data analytics challenging because of the processing and collection of data through different sensors in the IoT network. The data are totally different from normal big data collected through systems in terms of characteristics because of the various sensors and objects involved during data collection, which include heterogeneity, noise, variety, and rapid growth.
Inhaltsverzeichnis (Table of Contents)
- 1. INTRODUCTION
- 2. The IoT
- 2.1 ARCHITECTURE FOR IOT (Internet of Things)
- 3. BIG DATA
- 3.1 ARCHITECTURE FOR BIG DATA
- 4. Fog Computing
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This work aims to explore the intersection of the Internet of Things (IoT), big data, and fog computing. It investigates the challenges and opportunities presented by the rapidly growing volume of data generated by IoT devices and explores how big data analytics techniques can be applied to extract valuable insights.
- The characteristics and challenges of IoT data analytics
- The role of big data in processing and analyzing IoT data
- The architecture and functionalities of IoT systems
- The application of fog computing to address latency issues in IoT
- The potential applications of IoT data analytics across various sectors
Zusammenfassung der Kapitel (Chapter Summaries)
1. INTRODUCTION: This introductory chapter sets the stage by highlighting the rapid growth of big data and the Internet of Things (IoT), emphasizing their interconnectedness and the resulting opportunities and challenges. It introduces the three Vs of big data (Variety, Volume, Velocity) as defined by Gartner and underscores the unique characteristics of IoT data, such as heterogeneity and noise, differentiating it from traditional big data. The chapter also briefly touches upon the objectives of big data analytics, focusing on improved understanding, efficient decision-making, and the extraction of knowledge through data mining techniques. The potential of big data analytics for various applications, including smart cities and healthcare, is also mentioned. The significant market growth predicted for big data analytics by IDC is cited to further establish the importance of the subject matter.
2. The IoT: This chapter provides a comprehensive overview of the Internet of Things (IoT), defining it as a network of interconnected physical objects embedded with sensors and software. It explains how IoT enables remote control and sensing of objects, leading to improved lives and the creation of a smarter society. The chapter highlights the increasing use of IoT in various sectors like smart offices, retail, and healthcare. It also touches upon the projected exponential growth in the number of connected IoT devices by 2025, underscoring its significance as an emerging technology. A key section focuses on the architecture of IoT systems, including components such as sensors, gateways, edge IT, and data centers/clouds, depicting the flow of data and control across different stages. Different paradigms of IoT are briefly mentioned, demonstrating the wide variety of applications and their evolving nature.
3. BIG DATA: This chapter delves into the concept of big data, defining it as the massive volume of structured, semi-structured, and unstructured data generated by various sources, exceeding the capabilities of traditional database systems. The chapter explores different perspectives on defining big data, referencing studies from McKinsey Global Institute and "The Digital Universe," highlighting the challenges in managing and analyzing such vast datasets. The chapter also touches on Business Intelligence (BI) analytics and its limitations when dealing with datasets larger than available memory, providing insights into the evolution and significance of big data technologies and its applications for extracting valuable business insights.
4. Fog Computing: This chapter focuses on fog computing as a solution to address the latency challenges presented by the increasing number of IoT devices. It explains the concept of fog computing, its key advantages, and how it extends cloud computing to the edge of the network. The chapter details the types of applications that benefit from fog computing, emphasizing the need for low latency, geo-distribution, and mobile support. Crucially, it clarifies that fog computing doesn't replace cloud computing but instead complements it, creating a synergistic relationship where fog processes data locally while relying on the cloud for larger-scale tasks and storage. The chapter effectively positions fog computing as a crucial element within the broader IoT ecosystem.
Schlüsselwörter (Keywords)
Internet of Things (IoT), Data Analytics, Big Data, Cloud Computing, Fog Computing, Edge Computing, Sensors, Data Management, IoT Architecture, Big Data Architecture, Data Mining, Machine-to-Machine (M2M) communication, Smart Cities, Healthcare.
Frequently Asked Questions: A Comprehensive Language Preview of IoT, Big Data, and Fog Computing
What is the overall focus of this document?
This document provides a comprehensive overview of the intersection of the Internet of Things (IoT), big data, and fog computing. It explores the challenges and opportunities presented by the rapidly growing volume of data generated by IoT devices and examines how big data analytics can be applied to extract valuable insights.
What topics are covered in the Table of Contents?
The document covers an introduction, a detailed explanation of the Internet of Things (IoT) including its architecture, a discussion of Big Data and its architecture, and finally, a section on Fog Computing.
What are the key objectives and themes explored?
The key objectives include exploring the characteristics and challenges of IoT data analytics, understanding the role of big data in processing and analyzing IoT data, examining the architecture and functionalities of IoT systems, investigating the application of fog computing to address latency issues in IoT, and analyzing the potential applications of IoT data analytics across various sectors.
What are the main points discussed in the chapter summaries?
The introduction sets the stage by highlighting the rapid growth of big data and IoT, emphasizing their interconnectedness. The IoT chapter provides a comprehensive overview of IoT, defining it and explaining its architecture. The Big Data chapter delves into the concept of big data, defining it and exploring the challenges in managing and analyzing vast datasets. Finally, the Fog Computing chapter focuses on fog computing as a solution to address latency challenges in IoT.
What are the key characteristics of IoT data highlighted in the document?
The document highlights the heterogeneity and noise present in IoT data, differentiating it from traditional big data. The sheer volume and velocity are also emphasized, aligning with the "three Vs" of big data (Variety, Volume, Velocity).
What is the role of big data in processing and analyzing IoT data?
Big data plays a crucial role in processing and analyzing the massive volume of data generated by IoT devices. Its techniques are essential for extracting valuable insights and making informed decisions.
How does fog computing address latency issues in IoT?
Fog computing addresses latency issues by extending cloud computing to the edge of the network, processing data locally to reduce the reliance on distant data centers and thus reducing delays.
What are the potential applications of IoT data analytics across various sectors?
The document mentions potential applications in smart cities and healthcare, but implies broader applicability across numerous sectors where data from connected devices can provide valuable insights.
What are the key architectural components of IoT systems?
Key components include sensors, gateways, edge IT, and data centers/clouds, showcasing the flow of data and control across different stages.
What are the key keywords associated with this document?
Key words include Internet of Things (IoT), Data Analytics, Big Data, Cloud Computing, Fog Computing, Edge Computing, Sensors, Data Management, IoT Architecture, Big Data Architecture, Data Mining, Machine-to-Machine (M2M) communication, Smart Cities, and Healthcare.
- Citation du texte
- Ajit Singh (Auteur), 2018, Data Analytics and The Internet of Things, Munich, GRIN Verlag, https://www.grin.com/document/488804
