Technical Article

What is the Difference Between IoT and Cloud Computing?

August 31, 2021 by Anish Devasia

IoT infrastructure consists of many factory parts, one being cloud computing. Rather than ask how they’re different, let’s look into how they work together to form industry 4.0.

Internet of things (IoT), cloud computing, edge computing, and big data are becoming common phrases used in every industry. These concepts form the backbone of industry 4.0 and its architecture. This article covers the definition and how they work together. How all these elements work together will throw light on the differences between these technologies.


Internet of Things 

Every physical object can be embedded with software, sensors, and other technologies. Such devices are capable of collecting data and transmitting information to other devices over a network. IoT devices can also receive information and perform actions accordingly. In most consumer IoT devices, the network will be internet, hence the name.

IoT devices constantly monitor their respective processes. They are fitted with sensors and software to measure the physical characteristics of the process and environment. IoT devices also have network modules and protocols for communication. The sensors record the information and are relayed to the cloud or the central server over a network.

In a nutshell, IoT is the concept that all physical objects can collect data and communicate over a network.


Big Data

When all physical devices generate information and store it, the amount of information collected becomes too large. Today, all modern electronic devices generate data. The vast amount of data is collected by original equipment manufacturers (OEMs), network intermediaries, and other service providers.


Figure 1. Big data is the concept of collecting a large amount of data, then processing and analyzing the data to obtain meaningful, actionable insights for business operation.


In 2020 alone, every person in the world generated 1.7 megabytes of data every second. This amounts to 40 zettabytes or 40 trillion gigabytes. The vast amounts of data collected are stored, processed, and analyzed to obtain meaningful insights for business operations.

Traditionally, data is stored and collected in structured tables. But modern data generated is not just numerical data points: Audio, video, images, and other forms of data. Such data is known as unstructured data. Simple statistical techniques and query languages are not suitable to process such information. 

Artificial intelligence (AI) and machine learning algorithms are necessary to process unstructured data in large quantities. Big data analytics encompasses the tools and techniques to process large volumes of unstructured data.


Cloud Computing

Dedicated infrastructure-as-a-service providers build and maintain the infrastructure that other businesses can utilize. This way, a business does not have to spend capital and effort on building computing infrastructure. They would be able to use the computing infrastructure on a pay-as-you-go model.


Figure 2. Cloud computing uses managed computing infrastructure to store and process data.


The infrastructure, which is accessed over the internet, can be scaled up and down according to the current requirement. All data could be stored and processed on robust computing infrastructure managed by a dedicated, expert team.


Edge Computing

Edge computing is a distributed model of cloud computing. Most cloud computing implementations try to capture all data and think about what to do about it later. In an edge computing architecture, all devices have some computing power.

Edge computing distributes the computing power across the system. It sends only essential data to the cloud system to reduce the capacity requirement.


How Do IoT and Cloud Computing Work Together?

In an industrial scenario, all four concepts work in close conjunction. IoT devices collect data. Cloud computing and edge computing processes data. Cloud computing stores data. Big Data analytics processes information. An example would make it clear how all the different components work together.

A process in a manufacturing plant requires steam at a steady pressure. Water is heated in a boiler until steam at the right pressure is achieved. Let’s see how this relatively small system works in the IoT environment.

The boiler is connected with sensors that measure the temperature and pressure of the steam. These sensors connect to the network using a WiFi 6E connection. The sensor, an IoT device, has the modem and other ancillary components to connect to the network.


Figure 3. Temperature sensors in a boiler room.


The data measured by the IoT sensor is analyzed locally with the computational capability of the sensor. The temperature and pressure measurements are compared against the desired metrics. Since the computation is done at the sensor, a node in the IoT network, it is edge computing.

When the edge computing IoT device measures deviance from the desired measurement, the information is passed to the cloud computing system with the help of the WiFi 6E network. The cloud computing system assesses the deviance and computes the corrective action necessary. The required corrective measure is sent to the corresponding IoT device to execute.

If the measured pressure is less than the desired pressure, the water level in the boiler is assessed. If the boiler has sufficient water, the power to the boiler is increased. The power is decreased when the pressure is more than required. IoT devices execute all these changes after receiving the command from the cloud computing system.

All the data collected from IoT devices can be collected and stored in the cloud computing system. This data contains a lot of insights that can improve the process and safety of the plant. But the data collected would be on the scale of terabytes daily. Conventional data analysis techniques cannot be used on such a large volume of data. Instead, big data techniques are employed on these large datasets to extract insights.

Cloud computing, IoT, edge computing, and big data are smaller parts of the industry 4.0 ecosystem. Each element standalone has some uses, but the benefits compound when the different elements work in conjunction with each other.