To understand the impact of edge computing on the Internet of Things (IoT), let's take a moment to imagine the numerous sensors and wearable devices around us, and the types of data they are capturing. Edge computing enables us to interpret this massive amount of data near devices at the edge of any IoT network. This will trigger analytics and responses in real time without burdening a congested network.
A Gartner study predicts that by 2025, at least 75% of enterprises will implement edge computing to process data outside of traditional centralized data centers or the cloud. Currently, 10% of enterprises have already done so. Another IDC study predicts that global spending on edge computing will reach $250 billion by 2024. From a technology perspective, edge services will account for 21.6% of IT spending. Clearly, edge computing will become a crucial component of digital transformation strategies.
Edge computing in the Internet of Things – away from the cloud
By performing basic analysis near the data capture point or edge, the need to transfer large amounts of data to a centralized location is reduced.
Therefore, edge computing helps overcome latency and network congestion problems.
Cloud computing is all about centralized systems, while edge computing is a more distributed model. In some cases, it eliminates the need for the cloud, while in others it acts as an intermediary layer between edge devices and the cloud, allowing basic real-time analytics to be performed at the edge, while more complex analytics can be performed in the cloud simply by transferring the relevant data across the network.
Think about the bandwidth saved by security cameras that capture hours of video every day! Analytics performed with edge computing not only saves bandwidth but also enables IoT devices to interact meaningfully with users without communicating with cloud servers.
Hybrid solutions involving edge computing and the cloud eliminate most of the inherent efficiency problems of purely cloud-based systems, particularly the costly increase in bandwidth, lag in response, and security—all of which are amplified in IoT settings.
The expansion of IoT devices at the edge
The Internet of Things (IoT) devices and services are growing exponentially with the rise of 5G, taking over every aspect of our daily lives. The need for edge computing and its impact on the IoT becomes crystal clear as we realize the sheer number of IoT devices located at the edge of any network. Personal assistants like Google Home and Alexa, laptops, smartwatches, smart cars, smart locks and doorbells, cleaning equipment, smart switches, smoke detectors, smart heating systems, health monitors, pollution monitors, and fitness trackers are just a fraction of the IoT devices connected to the internet in homes today. Research estimates suggest that in a few years, each person in the United States will own at least 10 IoT devices.
Moving to manufacturing, you'll see a whole new world of Industrial Internet of Things (IIoT), also known as the Industrial Internet. AR applications for maintaining heavy machinery, AI-controlled drones in warehouses, robots for predictive maintenance, machine sensors for reducing energy and water waste, and temperature sensors are just some common applications of IoT in industry. Clearly, IIoT is far more complex than home-based IoT, requiring real-time processing of the collected data.
Implementing edge computing in IoT networks
Edge computing enables Internet of Things (AoT) analytics, an abbreviation for Internet of Things analytics. However, in the real world, IoT devices are extremely lightweight and have limited storage and computing power.
This is why when we talk about edge computing in the Internet of Things (IoT), edge devices include not only sensors and other IoT devices, but also routers and gateways. In fact, routers and gateways are actual computing devices running Linux or other similar operating systems. Edge computing middleware can be installed on these devices to securely receive data from IoT devices. Therefore, the devices that are truly at the edge can run lightweight solutions on them, while the actual analytics are performed on gateways and routers closer to these devices.
Use cases of edge computing in the Internet of Things
In situations where network latency is more important than computing power, edge computing stands out over cloud computing.
Let’s look at some specific examples.
Smart Home: As mentioned earlier, security cameras don't need to transmit all the video they capture to the cloud. Instead, if it can detect the outline of a common threat, only that segment can be streamed to the cloud server for further action. Even then, an alert should be issued immediately upon detection.
Self-driving cars: In scenarios like self-driving cars, even milliseconds of latency can be life-threatening. This is why computing and response times cannot rely on the cloud, where losing connectivity can be catastrophic.
Monitoring patient health requires analyzing data generated by edge devices in the medical IoT ecosystem and providing real-time health advice. Sending this data to a cloud-based central server for proper analysis can sometimes be too late. Edge computing in healthcare enables responses to urgent health situations.
Factory worker safety: Smart wearable devices, such as helmets and wristbands, can be used to track worker safety in heavy manufacturing settings and prevent accidents. They can also track health indicators such as body temperature and pulse, and indicate when workers need rest. Toxicity and radiation levels in the factory environment can be monitored and corrective measures taken without sending all this data to the cloud.
Digital video, multimedia content, temperature, motion, fuel levels, pressure, and other sensors, as well as data from production line machinery and other sources, are generating massive amounts of data at an unimaginable rate. The role of edge computing in the Internet of Things is to utilize this data while eliminating network latency and freeing up bandwidth.
