Edge computing processes data closer to where devices actually live. This reduces latency, enhances security and improves real-time data analytics. Here is a closer look at exactly what edge computing is, how it works and why it is essential to the future of Internet of Things (IoT).
What is edge computing?
Edge computing is the movement of processing closer to the location where data is generated, rather than taking in all of that data to a centralised cloud server. By moving processing out of the centralised cloud and into a node closer to where the data is generated, you keep the data travel distance low, meaning that things act faster and you use less bandwidth.
How does edge computing work?
Edge computing comprises devices like sensors, gateways and edge servers that receive data from IoT and other devices and process it in real-time. These local devices undertake important activities such as data filtering, aggregation and analysis. By performing these activities locally, edge computing ensures that the only data captured that is sent to the cloud for further processing and storage is necessary.
Benefits of edge computing in IoT
1. Reduced latency
One of the most important advantages of edge computing is reduced latency, which is the delay between when data is generated and when it’s processed. As a result, edge computing keeps data processing closer to the source, which is vital for applications that rely on real-time responses, including autonomous vehicles, industrial automation and smart cities.
2. Enhanced security and privacy
Physically localising decision-making also improves security and privacy: keeping sensitive data close reduces the risk of data loss or exposure in transmission, and local processing can enable more fine-grained security based on the individual application and environment.
3. Improved reliability
For example, when you use edge computing in an IoT system, your applications can continue to operate with improved reliability and resilience because data gets processed locally rather than relying on a central cloud server. This will be important for all of the above healthcare monitoring, in case of a central cloud server outage or connectivity problem, and will also be a must for connectivity-dependent emergency response systems.
4. Bandwidth optimisation
Filtering and processing at the edge considerably lowers the volume of data required to be transferred to the cloud, representing a major cost saving in bandwidth and better operational efficiency, especially in low-band environments.
5. Scalability
Through edge computing, processing tasks can be distributed among several different devices, thereby facilitating scalability. IoT systems can be expanded without overloading central servers, which makes it easier to incorporate new devices and applications.
Applications of edge computing in IoT
Industrial IoT
Edge computing also helps with automation in industrial settings, optimising machines and equipment for predictive maintenance and greater efficiency. When data is analysed in close proximity to individual pieces of equipment, tasks such as preventing breakdowns and enhancing safety become easier.
Smart cities
With edge computing, smart cities are possible on a smaller scale: data can be processed at the network edge so that traffic lights can be managed in real-time, energy use can be truly optimised based on demand, and public safety systems can respond immediately. Localised processing of citizen data has the potential to create more efficient and green cities.
Healthcare
Edge computing is crucial for remote patient monitoring, telemedicine and smart medical devices to operate in healthcare. Local data analysis aids in providing critical and timely health assessments for easy decision-making on patient treatment, and helps in reducing healthcare expenditure.
Retail
For retail businesses, edge computing can be used to manage inventory, personalise marketing and improve the customer experience. Individual stores enjoy real-time stock updates, customised marketing offers and faster point-of-sale systems by processing data locally.
Autonomous vehicles
It goes without saying that edge computing is crucial for autonomous vehicles. As it stands, a vehicle’s speed relies on real-time data processing. For example, if a car in front brakes suddenly, your vehicle needs to react instantly. If you collect sensor data from the car in front and stream it back to a data centre for processing, you are likely to crash into it before you get a response. Local analysis of the data from your sensors ensures that you’ve made a decision in time to avoid an accident. In other words, edge computing improves the autonomous vehicle’s responsiveness and safety.
This is where edge computing comes into its own by vastly improving several metrics such as latency, security and reliability of the IoT ecosystem. As IoT expands, it is highly likely that edge computing will increase in importance, bringing about new ideas, innovations and applications across industries. Edge computing will be material to IoT technologies and understanding it will give businesses and developers a competitive edge in the future.
Article by Magda Dąbrowska, editor at WeKnow Media
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