The Revolutionizing Impact of Edge Computing on IoT
To understand the impact of edge computing on IoT, let us first take a moment to visualize the numerous sensors and wearables all around us and the kind of data they are capturing. Edge computing enables us to make sense of this enormous data closer to the devices at the edges of any IoT network. This triggers analytics and responses in real-time without burdening the congested networks.
A study by Gartner predicts that by 2025 at least 75% of enterprises will implement edge computing to process data outside a traditional centralized data center or cloud. Currently, 10% of the enterprises have done so. Another study by IDC predicts that worldwide spending on Edge computing will touch $250 billion by 2024. From a technology perspective, edge services will account for 21.6% of IT spends. Clearly, edge computing is poised to be an important part of digital transformation strategies.
Edge Computing in IoT - Moving Away From the Cloud
By performing basic analytics closer to the point or edges where data is captured, the need to transfer vast amounts of data to a centralized location is reduced.
Edge computing thus helps to overcome the problem of latency and network congestion.
While cloud computing was all about centralized systems, edge computing refers to a more distributed paradigm. In some cases, it eliminates the need for cloud while in others it acts as a middle layer between the edge devices and cloud so that basic, real-time analytics can be performed at the edges while more sophisticated analysis is performed on the cloud with only relevant data transmitted across.
Consider the bandwidth spared in cases of security cameras that capture hours and hours of video every day! The analytics performed with the help of edge computing not only save bandwidth but also enable the IoT devices to interact meaningfully with their users without the need to communicate with the cloud server.
A hybrid solution that involves both edge computing and cloud removes most of the inherent efficiency issues in a purely cloud-based system, specifically, costly bandwidth additions, lagged responses, and security - all of which are greatly amplified in an IoT setup.
The Expanse of IoT Devices at the Edges
IoT devices and services have grown exponentially riding on the wave of 5G services, taking over every aspect of our daily lives. The need for edge computing and the impact of edge computing on IoT becomes abundantly clear when we realize the expanse of IoT devices that sit on the edges of any network. Personal assistants like Google Home and Alexa, laptops, smart watches, smart vehicles, smart locks and doorbells, cleaning devices, smart switches, smoke alarms, smart heating systems, health monitors, pollution monitors, and fitness trackers are just some of the IoT devices seen in households today, which connect to the internet. Studies estimate that after some years every individual in the US will own at least 10 IoT devices.
Moving on to manufacturing, you have a whole new world of the Industrial Internet of Things, also called IIoT. AR applications for maintaining heavy machinery, AI-controlled drones for warehouses, robots for predictive repairs, machine sensors to reduce energy and water wastage, temperature sensors, are some of the generic applications of IoT in industries. As is evident, the IIoT is more sophisticated than home-based IoT, and the data that is collected needs to be acted upon in real-time.
Implementing Edge Computing in IoT Networks
Edge computing has made it possible to achieve Analytics of Things (AoT), a shorthand term that refers to IoT analytics. However, in the real world scenario, the IoT devices are extremely lightweight with limited storage and computing capabilities.
This is why, when we talk about edge computing in IoT, the edge devices include not only the sensors and other IoT devices, but also the routers and gateways. In fact, the routers and gateways are the actual computing devices that run on Linux or another similar OS. It is on these devices where an edge computing middleware can be installed that receives data from the IoT devices in a secure manner. As a result, the devices truly on the edge can have lightweight solutions running on them while the actual analytics is performed on the gateways and routers that are still closer to these devices.
Use Cases for Edge Computing in IoT
Edge computing is gaining prominence over cloud computing in cases where network latency is a bigger issue over computing power.
Let’s look at some specific examples that show how edge computing makes a difference.
- Smart homes- As mentioned earlier, a security camera need not stream all the video it captures to the cloud. Instead, if it can detect outlines of common threats like a gun or a masked person, only that clip can be streamed to the cloud servers for further actions. Even so, an alarm can be sounded instantly upon detection.
- Self-driving vehicles - In scenarios like autonomous vehicles, lag of a few milliseconds can risk lives. This is why computing and response time cannot depend on the cloud where loss of connectivity can prove disastrous.
- Monitoring health of patients - The data generated by medical IoT edge devices needs to be analyzed and health advice needs to be given in real-time. If it is sent to a central cloud-based server to be analyzed properly, it may be too late in some cases. Edge computing in healthcare makes it possible to react to health emergencies.
- Safety of workers in factories - Smart wearables like helmets and wristbands can be used to track the safety of workers in heavy manufacturing scenarios and prevent mishaps. They can also track health metrics like body temperature and pulse and indicate when a worker needs rest. The level of toxicity and radiation in the factory environment can be monitored and corrective measures can be taken without sending all this data to the cloud.
Digital videos, multimedia content, sensors for temperature, motion, fuel levels, pressure, etc., and machine data from servers and production line machinery and other IoT sources are generating huge volumes of data at unimaginable speed. The role of edge computing in IoT is to make use of this data while eliminating network latency and freeing up bandwidth requirements.
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