By enabling easy access and interaction with a wide variety of physical devices and their environments, the Internet of Things (IoT) will foster the development of various applications in various domains, such as health and medical care, intelligent energy management and smart grids, transportation, traffic management, and more.
These applications will generate big and real-time/streaming data, which will require big data analysis tools, including advanced machine learning, that is, deep learning (DL), to extract useful information and make informed decisions. We need to understand the architecture of the IoT and its different components in order to apply advanced machine learning techniques on the generated data of IoT applications.
In this post, I will discuss a typical architecture of the IoT and its related concepts and components.
The IoT Network
In the above diagram:
(a) presents a three-layer IoT life cycle or architecture, and
(b) presents a five-layer IoT life cycle or architecture.
- The perception layer: This is the physical layer or sensing layer, which includes things or devices that have sensors to gather information about their environments. As shown in the following diagram, the perception layer of an E2E life cycle of the IoT solution in healthcare consists of patients, hospital beds, and wheelchairs that are deployed with sensors.
- The network layer: A network is responsible for connecting to other smart things, network devices, and servers. It is also responsible for transmitting and processing sensor data.
- The application layer: This layer is responsible for delivering application-specific services to users, based on the data from the sensor. It defines various applications in which IoT can be deployed, for example, smart homes, smart cities, and connected health.
Picture credit : Jigsaw Academy
The three-layer E2E IoT life cycle or architecture defines the key ideas of IoT, but it may not be enough for research and development, as these often deal with the finer aspects of IoT. This is why other life cycles or architectures, such as the five-layer life cycle, have been proposed.
- The transport layer: This is similar to the network layer of the three-layer life cycle. It transfers the data gathered in the perception layer to the processing layer and vice versa through networks such as wireless, 3G, LAN, Bluetooth, RFID, and NFC.
- The processing layer: This is also known as the middleware layer. It stores, analyzes, and processes huge amounts of data that comes from the transport layer. It can manage and provide a diverse set of services to the lower layers. It employs many technologies, such as databases, cloud computing, and big data processing modules.
- The business layer: This layer manages the whole IoT system, including applications, business and profit models, and user privacy.
The following diagram presents the protocol layer-wise architecture of fog computing:
Picture credit: Smart Things
As shown in the above diagram, the architecture of fog computing or fog computing with IoT consists of six layers: physical and virtualization, monitoring, preprocessing, temporary storage, security, and transport. Notably, the preprocessing layer performs data management tasks by essentially analyzing, filtering, and trimming collected data from physical or virtual sensors.
Based on professional knowledge, learning and research