Internet of Things (IoT) and Big Data: Data Management, Analytics, Visualization and Decision Making

Table of Contents:

Introduction

Nowadays, numerous new digital technologies are changing the world through digital adoption. These technologies have caused digital disruption in the lifestyle of people. Internet of Things (IoT) has widened the innovation landscape in technology, elevating the digital experience to a new level across the globe. These innovations are ubiquitous, whether it is lifestyle products, business operations, or governance. Digital technologies are working together to make IoT a reality.

The rapid growth in the number of devices related to IoT coupled with the exponential increase in data utilization illustrate how advancements in big data are associated with that of IoT. The control of big data in a continuously increasing network gives a push to non-trivial concerns regarding data collection ability, analytics, data processing, and security. Despite numerous studies on big data, analytics, and IoT, the concurrence of these technologies creates several possibilities for the advancement of big data analytics and IoT systems. Technologies such as IoT, artificial intelligence, machine learning, big data, and sensor technology can be incorporated to increase the efficiency of data management and knowledge discovery of applications.

IoT can enable big data and their related technologies, which are used to improve the performance of IoT devices. IoT, big data, and related technologies can be used to improve functions and operations in various applications as well as in diverse sectors. Both have extended their capabilities to a wide range of areas. Big data analytical process uses IoT-generated data from different physical devices, which is used to help or improve decision making. The role of big data in IoT is to process a large amount of data based on real-time processing or batch processing and to store the results using storage technologies [7]. IoT-big data process can be implemented using the following steps (Figure 10.1):

  • 1. IoT device generates considerable amounts of data from various physical IoT devices. This IoT-generated big data depends on factors such as volume, velocity, and variety.
  • 2. The large amount of big data is stored in big data management system.
loT-big data processing

Figure 10.1 loT-big data processing.

  • 3- The stored IoT-big data is analyzed using big data analytic tools. 4. The reports are generated and used in decision-making processes.
  • 10.1.1 The Importance of Data Analytics in IoT Applications

IoT with big data moves at the edge for ongoing decision making, such as recognizing crop designs in agricultural plants, detecting suspicious activities at ATMs, and anticipating driver behavior for an associated car. In IoT environment, big data technologies offer data storage, and big data analytical tools implement data analysis to make better decisions. IoT applications are important sources of big data and big data analytics. Table 10.1 shows the importance of big data analytics in IoT applications.

IoT with big data helps realize the future of a smart technological world. The convergence of IoT and big data can provide new opportunities in all applications [6].

Big Data Framework for IoT

Big data framework in IoT acts as the foundation for data analytics and visualization. The framework is divided into different layers with each layer performing a specific function, which is used for big data analytics. The big data framework can be used for the following tasks:

■ Information extraction

■ Massive datasets processing

■ Environment optimization

Table 10.1 Importance of Big Data Analytics in loT Applications

IoT Applications

Uses of Big Data Analytics

Smart transportation
  • • Minimize accidents by history of mishaps
  • • Minimize traffic congestion
  • • Optimize shipment movements
  • • Ensure road safety

Smart healthcare
  • • Predict epidemics, cures and disease
  • • Help insurance companies make better policies
  • • Pick up the warning signs of any serious illnesses in their early stages

Smart grid
  • • Help design an optimal pricing plan according to the current power consumption
  • • Predict future supply needs
  • • Ensure an appropriate level of electricity supply

Smart inventory system
  • • Detect fraudulent cases
  • • Strategically place an advertisement
  • • Understand customer needs
  • • Identify potential risks

■ Storage of large amounts of unstructured data

■ Unstructured data to structured data format

■ Data analysis

IoT-big data management performs a number of managerial activities, such as data collection, integration, storage, processing, analysis, and visualization, which are implemented using various models, architectures, and frameworks [2]. Various types of frameworks are used in IoT-big data management, data analysis, and visualization. In IoT-big data research, different big data frameworks, such as general framework, Cognitive-Oriented IoT Big-data (COIB) framework and machine- to-machine (M2M) framework, are implemented for performing various data management activities for data analytics and knowledge discovery. The big data management framework is used to securely manage the large amounts of data generated by IoT-enabled devices. In this section, we describe two specific frameworks used in IoT-big data activities [1].
 
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