Internet of Things (IoT) Module 1

The fundamental building blocks of IoT networks. They are 'smart' because they use contextual information and configured goals to take action.

The 'Things' layer is responsible for collecting data from sensors and actuators.

The 'Things' layer is responsible for collecting data from sensors and actuators.

Enables communication between 'Things' and the broader network. It is divided into sublayers: Access Network, Gateways & Backhaul, IP Transport, and IoT Network Management.

Connects 'Things' to the network.

Connects local networks to the broader internet.

Provides standardized communication methods. Ensures data can travel across the internet.

Manages and monitors the entire network. Ensures consistent operation.

Making objects smarter by connecting them to a network, allowing us to sense and control the physical world.

The core aim of IoT is to connect objects that are not currently on the internet, enabling them to communicate and interact with people and other objects.

Remote sensing and control of objects and machines across a network creates a closer link between the physical world and computers.

Kevin Ashton, while working at Procter & Gamble in 1999, coined the term "Internet of Things" to explain a new concept of connecting the company's supply chain to the internet.

The Internet has evolved through four distinct phases, each impacting our lives and society significantly.

Digitization broadly refers to converting information into a digital format, which can involve connecting objects, generating data, and deriving business insights.

While IoT and digitization are often used interchangeably, IoT focuses specifically on connecting objects to a network, while digitization encompasses a broader concept of data conversion and business insights.

The transformation of the entire photography industry from film to digital illustrates the concept of digitization.

A standardized architecture for machine-to-machine (M2M) communication, designed to accelerate the adoption of M2M applications and devices, and later extended to include the Internet of Things.

A major challenge in designing IoT architectures, arising from diverse types of devices, software, and communication methods.

The top layer in the oneM2M architecture, focusing on connections between devices and their applications, including standardized northbound APIs for interaction with business intelligence (BI) systems.

The middle layer in the oneM2M architecture, providing a horizontal framework across different vertical industry applications, including the physical network, management protocols, and hardware.

The bottom layer in the oneM2M architecture, responsible for the communication domain of IoT devices and endpoints, including devices themselves and the communication network linking them.

Application-layer protocols and standardized APIs that allow applications to interact with business intelligence (BI) systems.

Specific data models used by applications, reflecting the needs of different industries.

A common services layer built on top of the physical network, management protocols, and hardware, which enables communication and management of the various aspects of the IoT.

Refers to whether a connected device has its own power supply or relies on an external source.

Determines whether a connected device is stationary or capable of movement.

Describes the frequency at which a device reports its data. Higher frequency means more frequent updates.

Defines the amount of data transmitted each time the device sends information.

Determines the maximum distance a device can be from its gateway to maintain communication.

Indicates the number of devices operating in a specified area, connected to the same gateway.

The differences in physical form factor, operating environment, and operational aspects between devices used in IT and OT.

A method of processing and analyzing data collected by smart objects to gain meaningful insights about an IoT system. It can range from simple alerts triggered by sensors to complex data processing for predictions.

This type of analytics focuses on understanding the health and performance of the network that connects IoT objects. It helps identify issues like connectivity losses and optimize network efficiency.

A type of intelligent service that leverages IoT data to enhance operations. This can be applied in various domains by providing a more knowledgeable and responsive approach to managing assets and processes.

An interactive visual tool that displays data and insights about an IoT system. It can be used for monitoring, analysis, and tracking various aspects of the network and connected objects.

Control applications in IoT manage complex aspects of a network using logic that cannot be easily implemented within a single device. They coordinate actions between multiple objects and utilize data from various sources.

A software system or feature that monitors the health and performance of a computer network, like an IoT network. It provides insights into network traffic, connectivity issues, and other relevant data.

These are software tools that provide insight into data gathered from IoT sensors. They can be used to analyze data patterns, identify trends, and draw conclusions about the system.

The use of technologies like sensors, actuators, and communication protocols to connect physical objects to the internet. It enables collecting data from real-world environments and controlling devices remotely.

The ability of smart services to monitor and analyze machine performance metrics, such as output and speed, to assess efficiency.

An IoT system can integrate smart services, such as sensors, which may trigger actions based on real-time data. Example: A light bulb turning on/off based on human presence.

As data volume, diversity of connected devices, and efficiency demands increase, analyzing data closer to the source becomes crucial for real-time decision-making.

Minimizing the delay (latency) in data analysis is critical for real-time applications, such as preventing industrial accidents or restoring services.

When dealing with a large number of connected devices generating massive amounts of data, transferring it all to the cloud is impractical and inefficient.

Analyzing data at the source (locally) can improve efficiency by enabling independent responses to local environmental conditions.

Traditional cloud computing models are not optimal for IoT systems due to limitations in bandwidth, latency, and data management.

IoT systems face challenges like limited bandwidth, high latency, and managing vast amounts of data from millions of connected devices.