A pizza delivered from a remote location would become cold on its journey to its consumer. Therefore, the restaurant expands into other areas with smaller outlets. Hence, if you can't bring the customer closer to the restaurant, you might as well move the pizzeria closer to the customer.
Similarly, edge computing is a network design in which client data is processed as close to the source as possible at the network's edge. The ability to analyze data fast and effectively is critical in time-sensitive operations.
The growth of the Internet of Things (IoT)
Data is the heartbeat of modern business, providing crucial business insight and allowing for real-time control of operations. The Internet of Things (IoT) has increased the volume of data collected at the network edge from corporations, enterprises, factories, hospitals, banks, and other established entities.
It addresses the issues like bandwidth, latency, resiliency, and data sovereignty. It caters to the needs of workloads that require real-time processing. Decisions can be made at the collecting site or at a location that is physically nearby. This reduces the amount of time it takes to make a data-driven decision.
For example, we don't need to transfer ‘heartbeat’ data captured by IoT sensors to the cloud or a faraway cloud data center. Edge computing devices can considerably minimize the amount of data transmitted by filtering out this stable heartbeat data right at the network edge. This saves bandwidth and dramatically lowers latency.
Key Drivers for the Growth of Edge Computing.
Autonomous Vehicles and Electric Vehicles
Autonomous vehicles works alongside other connected vehicles, traffic control systems, roadside units, and pedestrians. For obvious safety considerations, autonomous cars require immediate decision-making. Autonomous vehicles are practical, because edge computers can receive data and make choices in milliseconds. We can avoid relying on the cloud to evaluate vehicle data by processing data locally at the source of data generation. The requirement for data to travel thousands of kilometers to a data center for processing and decision making is eliminated with edge computing.
The durability of electric vehicle batteries is dependent on the specific behaviors of drivers, the traffic in the locations they travel, and how frequently they are charged. Edge computing can process the relevant data for performance and maintenance.
Edge computing enables businesses to better manage their energy usage. In factories, plants, and offices, sensors and IoT devices connected to an edge platform monitor and analyze energy consumption in real time. This helps to establish new solutions with real-time visibility, such as running high-powered machines during off-peak times to reduce the electricity demand.
Factory operators can perform good maintenance if they can predict a component's failure well in advance. Manufacturers seek to analyze and detect changes in their manufacturing lines before a problem arises. For example, edge data can help the oil and gas industry manage their pipelines, detecting flaws early on and preventing failures. Results and reports that formerly took weeks to produce can now be sent in seconds.
Healthcare and Medical Applications
Monitoring equipment such as oxygen monitors and other sensors are either not connected or require vast amounts of unprocessed data to be kept on a third-party cloud. For healthcare providers, this poses a security risk.
Instead of relying on cloud services, healthcare institutions can store and analyse data locally using edge computing. As a result, professionals will have faster access to critical medical data such as MRI or CT scans, as well as information from an ambulance or emergency room, allowing for speedier diagnosis and treatments.
Edge computing sensors, monitors, wearables, and other equipment can be used by the healthcare industry to collect patient data and give experts accurate and timely insights about patient state. In robot-assisted surgery, where near-zero latency is critical, edge computing can also be used.
We can reduce latency by caching stuff at the edge, such as music, video streams, and web pages. Content providers are trying to expand the distribution of CDNs to the edge, ensuring network flexibility and customization based on user traffic demands.
Smart homes Smart Cities, Clean Energy and Green Technology
Edge computing devices can be used by cities and smart grid systems to monitor public buildings and facilities for increased efficiency in lighting, heating, clean energy, etc. Intelligent lighting controls can be used to manage individual lights or groups of lights. Edge computing devices are used in solar fields to detect weather changes, adjust location, and report metrics such as battery usage.
Edge computing sensors collect data to monitor weather conditions, soil quality, sunlight, and other essential information for crop growth. We can forecast crop output, helping farmers to better plan how their crops will be distributed once they're farmed. In greenhouses, we can collect real-time data on growing conditions such as lighting, temperature, soil condition, and humidity, for optimal crop growth.
With edge computing, only specific clips where triggers have been set off are sent for additional analysis and inspection. This reduces the amount of required internet traffic. This is in contrast to the previous model, in which all video footage was sent to the cloud for remote monitoring and analysis.
Edge AI occurs when artificial intelligence (AI) techniques are implemented in IoT endpoints, gateways, and other devices at the point of usage. Millions of smart gadgets, from smartphones and smart speakers to car sensors and security cameras, are powered by it.
Conclusion: Edge computing is revolutionizing how data is received, handled, processed, and used by billions of devices all over the world. Thanks to artificial intelligence and machine learning, edge computing will leverage data to translate information into actions that will benefit organizations and their customers.