Edge computing for IoT

Today, edge computing has become an essential complementary technology to IoT, helping speed data processing times, reducing latency and improving the security of a wide range of IoT devices.

Many modern applications depend on edge computing in IoT for their functionality. From connected devices that enable healthcare professionals to monitor patients remotely. Sensors optimize traffic flows in congested areas and systems control hydroelectric dams—its use cases are broad and varied.

A recent report projected that the number of IoT devices worldwide would reach 18 billion by the end of 2025, an increase of 1.6 billion over the previous two years.¹

Edge computing is critical to ensuring that the data these devices generate is processed at the edge rather than in the cloud, where it would dramatically slow networks like the internet.

According to Fortune Business Insights, the global market for edge computing was valued at a little over USD 10 billion just two years ago. It is expected to reach USD 182 billion over the next six years—a compound annual growth rate of 38.2%.²

Edge computing is a distributed computing framework that moves enterprise applications (apps) closer to the data sources they depend on for their functionality, such as edge computing devices.

By moving apps closer to the source, edge computing helps speed time to insight. It also improves response times and increases bandwidth. With the spread of 5G networks, devices with internet connectivity can generate massive volumes of data.

Edge computing enables newer technologies like cloud computing and artificial intelligence (AI) that depend on this data to flourish.

The Internet of Things (IoT) refers to a network of physical devices, often called “smart” devices, that are embedded with sensors and software. These devices are connected to a network like the internet, allowing them to collect and share large amounts of data.

Examples of IoT devices include smart home appliances like refrigerators and thermostats, as well as more advanced systems like wind turbines, hydroelectric dams and drones.

Edge computing significantly enhances the efficiency of IoT devices by processing the data that they collect closer to its source. This approach avoids transporting the data to a centralized data center first.

By processing data closer to where it's collected, edge computing significantly shortens IoT data processing times, making the technology more efficient and increasing its number of use cases and applications.

IoT edge computing uses devices and sensors to push data through a system, process, and store it—all without transporting it to a data center. By spreading its workloads across multiple devices, IoT edge computing ensures that no single device ever gets overloaded. Here’s a closer look at the process.

1. Data collection: A sensor on an IoT device collects data. Examples include wind speed and direction on a turbine or room temperature on a smart thermostat.
2. Local processing: The data is processed locally through an edge device, usually a gateway or nearby server, that analyzes it using local computing resources.
3. Data filtering: The edge device filters the raw data that it collected, discarding unimportant information and processing what’s relevant.
4. Automation: Perhaps the most critical step in the whole process, edge computing enables automated decision-making for some IoT devices. Sensors can be programmed to take certain actions based on real-time data—for example, shutting down a machine that’s overheating or turning an autonomous vehicle to prevent a crash.
5. Cloud processing: The last step in the process involves identifying data that needs to be sent to the cloud for further processing, storage and data analysis. For example, if it were to yield business intelligence insights or serve training purposes.

IoT devices and edge devices are so similar that the two terms are often used interchangeably. However, there are a few differences worth noting. Broadly speaking, IoT devices are hardware components connected to a network that generate data through one or more sensors. Edge devices are also pieces of hardware. Unlike IoT devices, they are designed to collect, process and act upon data—not merely store it.

Typically, edge devices are more complex than IoT devices and contain more parts. Some edge devices contain both processing power and computing resources.

When an edge device is highly integrated into an IoT device, it can be considered a component of the device itself, rather than a separate system. For example, when an IoT device is equipped with data storage and enough compute power to make simple, low-latency decisions.

Machine learning (ML), a type of AI that focuses on teaching computers to learn like humans, plays an important role in most edge computing and IoT applications.  

Using ML, IoT and edge devices can be trained to make predictions and initiate responses based on data they have collected, stored and processed.  

ML application programming interfaces (APIs) collect data from IoT edge devices and use ML algorithms to spot patterns, changes in environmental conditions and more. Using that information, the edge device can learn to spot certain conditions or anomalies and trigger automated processes.

For example, an ML-equipped IoT edge device attached to water flow sensors can be programmed to open or close drainage channels to allow water to flow in different directions to prevent flooding.

Through a device known as an IoT gateway, edge computing and IoT devices can connect with modern cloud computing environments to improve functions like data filtering and analytics. IoT gateways are small devices designed to connect IoT devices to the cloud by translating communication protocols and collecting and processing data locally.

IoT gateways help ensure a reliable, secure flow of data between the IoT or edge device and cloud-based systems and services, improving efficiencies and enhancing overall network security. IoT gateways use a range of encryption capabilities to make data unreadable as it moves between devices, users and the cloud, ensuring only authorized users can view it.

Through IoT gateways, IoT devices enable a wide range of cloud services like smart homes and cities, remote facility management and supply chain management.

Combining the power of edge computing with the versatility and diversity of applications that IoT devices offer has a wide range of benefits. Here are some of the most common.

Edge computing in IoT has transformed the way enterprises monitor the performance of their most valuable assets and collect, store and process data. From safely operating autonomous vehicles to making cities safer and optimizing complex manufacturing systems, here are five of its most compelling use cases.

Healthcare workers use IoT sensors and edge computing to monitor patients remotely and treat a wide range of conditions. From tracking vital signs to sending alerts about changes in chronic conditions like diabetes and heart murmurs, edge computing in IoT plays a vital role in remote patient monitoring. It has made the process safer and easier for both patients and their care providers.

IoT solutions and edge computing capabilities enable autonomous vehicles, from self-driving cars to pilotless aircraft and weapons systems to perform a wide range of tasks safely and effectively. Planes, drones and cars all need to react to changes in their environment in near real-time.

Advances in edge computing for IoT have made autonomous vehicles less reliant on cloud computing, allowing data to be processed on the edge of a network rather than in a data center.

Industrial IoT (iIot) involves adding IoT sensors to the complex and expensive machines used in many industrial manufacturing processes. These edge and IoT sensors analyze a constant stream of data, applying advanced ML learning algorithms to spot opportunities for improvement.

IoT sensors can also be added to weak or vulnerable parts of a manufacturing system to help engineers better understand what is causing a failure to occur.

Smart cities, connected urban areas that rely on technology to collect and analyze data to improve the quality of life for citizens, are highly dependent on edge computing and IoT technologies.

In smart cities, local governments rely on sensors attached to roads, vehicles, power plants and more to deliver real-time information on conditions. This information helps them optimize power grids, traffic systems, emergency response systems and other key parts of infrastructure.

IoT technology has allowed for most aspects of modern supply chains to be managed remotely. Sensors affixed to goods at the time of manufacture, for example, provide real-time status and location information. This data gives users a real-time picture of their inventory and allows them to optimize the flow of goods.

In more advanced applications, companies have used edge computing in IoT systems to automate aspects of inventory management, freeing up human resources to be deployed elsewhere.