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.
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
4.1 ARCHITECTURE FOR FOG Computing
5. Data Mining
5.1 ARCHITECTURE FOR Data Mining
6. Conclusion
Research Objectives and Focus Areas
This paper examines the integration of Big Data analytics within the Internet of Things (IoT) ecosystem, focusing on how massive data volumes generated by sensors can be effectively processed and utilized. The research aims to explore the interplay between IoT architectures, cloud and fog computing models, and data mining techniques to derive actionable insights from complex, heterogeneous sensor data.
- Architectural requirements for large-scale IoT data processing.
- The transition from Cloud to Fog computing for low-latency applications.
- Advanced data mining methodologies for extracting knowledge from IoT sensor networks.
- Challenges associated with big data characteristics (Variety, Volume, Velocity) in IoT environments.
Excerpt from the Book
4. Fog Computing
The number of devices being connected to the Internet is tremendously increasing. The device being connected to Internet is only due to advancement in the field of electronics and telecommunication field. The devices that are being connected are powerful in the sense that these device are able to communicate with each other .This type of communication is called M2M communication or Machine to Machine communication. In other words this paradigm is known as Internet of Things.
The devices being referred to as ―Things include sensors, physical devices for performing various tasks. We can define IoT as network of physical objects embedded with software, electronics, sensors to achieve value and service by exchanging data with operators, connected devices through various protocols without any human interaction and involvement.
With the increased number of devices being connected to each other the data produced by the devices is also huge which is transferred through the network to the Internet. In IoT cloud plays an important role. The word Cloud Computing has been in the market for so many years now and various researches and advancements have been done in the field of cloud computing. In IoT one of the benefits we get from the cloud is the flexibility the user gets in accessing the services offered by the cloud providers through user interfaces.
The cloud can pose a problem for latency sensitive applications as IoT requires mobility support and geo distribution in addition to low latency and location awareness. So a new platform is needed called Fog. Fog extends cloud to the edge of the network. The term fog computing was introduced by Cisco in 2014. It was introduced so that it addresses applications which do not fit the paradigm of the cloud.
Summary of Chapters
1. INTRODUCTION: Provides an overview of the growth of IoT and Big Data, highlighting the challenges of processing large-scale, heterogeneous sensor data.
2. The IoT: Defines the Internet of Things and its role in connecting physical objects, including its architecture and the projected expansion of interconnected devices.
3. BIG DATA: Discusses the transition from traditional database systems to modern big data architectures capable of handling massive, high-velocity data streams.
4. Fog Computing: Explains the necessity of Fog computing to address latency-sensitive applications that the traditional cloud model cannot support efficiently.
5. Data Mining: Analyzes the relationship between IoT and data mining, outlining the key considerations for building high-performance mining modules.
6. Conclusion: Summarizes the current state of IoT data analytics and suggests that future developments must focus on real-time insights.
Keywords
IoT, Data Analytics, Big Data, Cloud Computing, Edge Computing, Fog Computing, Internet of Things, Sensor Networks, Data Mining, Architecture, Latency, M2M Communication, Real-time Analytics, Heterogeneity, Information Extraction.
Frequently Asked Questions
What is the primary focus of this research?
The work investigates the integration of big data analytics into IoT systems, focusing on the processing challenges and opportunities presented by massive sensor-driven data.
What are the central thematic areas?
The paper covers IoT architecture, big data characteristics, cloud versus fog computing, and data mining techniques applied to intelligent sensor networks.
What is the main objective of the paper?
The goal is to explore how to effectively process and analyze huge volumes of IoT-generated data to improve decision-making and business value.
Which scientific methods are employed?
The research is based on a survey and analysis of current IoT big data analytics frameworks, comparing different computing architectures and mining methodologies.
What is addressed in the main sections of the document?
The text details IoT and Big Data definitions, their respective architectures, the emergence of Fog computing as a solution for latency, and the integration of data mining for knowledge discovery.
Which keywords characterize the work?
Key terms include IoT, Big Data, Fog Computing, Cloud, Data Mining, Latency, and Sensor Networks.
Why is Fog computing highlighted as a necessary development?
Fog computing is presented as a solution for applications that require low latency and location awareness, which are often not well-served by centralized cloud infrastructures.
What challenges do sensor networks pose for traditional Big Data analysis?
Unlike standard big data, IoT data is characterized by extreme heterogeneity, high noise, variety, and rapid growth, necessitating specific analysis tools.
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- Ajit Singh (Autor:in), 2018, Data Analytics and The Internet of Things, München, GRIN Verlag, https://www.grin.com/document/488804
