Cloud computing has had an enormous impact on the way organizations use and deploy the network for objective purposes. From the widely adopted public cloud to the more expensive private cloud and the more complex and multifaceted hybrid cloud or multi-cloud environments, cloud computing has helped bring flexibility, versatility and scalability to businesses. For the first time, cloud solutions made it possible to access computing resources any time, anywhere, irrespective of device interface and location.
The cloud brought a literal revolution to the world of computing. But it’s a stepping stone to something better.
Despite of their revolutionary role in the history of scientific progress, steam engines were a milestone that prepared us for the more advanced electric engines. Likewise, we now have edge computing as a more advanced successor that deals with the shortcomings of the cloud environment.
Cloud computing requires collecting data and sending it to the cloud and receiving the results after the computing task is carried out. The whole process causes latency challenges because data must travel between data centers across different regions. Edge computing addresses this latency challenge by facilitating storage and computing close to where the data is generated.
Until now, internet of things (IoT) devices have mostly relied on the cloud computing environment for all their data storage, data management and computing tasks. But as edge computing has emerged as a more reliable, performance-driven and efficient solution, IoT devices can further boost their output and efficiency by taking part in edge networks. By taking part in edge networks, IoT devices can also further increase the speed, performance and responsiveness of edge computing networks.
Edge computing and IoT
Edge computing can address the connectivity problem IoT devices face in an efficient manner. By relocating key data processing functions to the edge of a network or close to where data is originated, edge computing helps connected devices maintain the same level of efficiency even while the network connection is poor.
While most IoT devices have limited processing capability, edge computing comes to their rescue by taking care of intensive processing needs locally at the very edge of the network. This helps IoT devices respond to various pressing requirements with near-zero latency. The evolving requirements of the IoT devices to rely on local processing power is actually evolving edge computing networks to incorporate edge data centers. Edge data centers and local data processing will also provide an extra layer of security between IoT devices and central cloud servers.
Key features of core IoT edge architecture
While IoT devices and sensors are becoming increasingly present in edge networks, it is important for future IoT app developers and strategists to know the key constituents and features of the IoT edge architecture. Let’s have a look at these key components or features.
- Complex event management: Complex event processing (CEP) software solutions are built in the cloud and are pushed to the edge of a network.
- Machine learning and artificial intelligence models: Machine Learning models train the devices locally to adapt to user preferences and draw relevant insights from user data that are further processed in the network edge and in a cloud server.
- IoT applications: By using CEP software solutions and ML models, many IoT devices are now running applications at the edge of networks.
- Offline support: Offline data storage at the edge of networks helps address issues concerning the availability of data in times of necessity.
- Data management:Â IoT devices use edge computing to take a call about which data they must store and process at the edge data center and which they must transfer to the cloud for further computing purposes.
5 ways IoT can add value to edge networks
By taking part in the edge computing network, IoT devices can boost network efficiency and performance to a great extent. Here are the key ways IoT devices can make high-value additions to the edge network:
- Data collection: When IoT devices take part in the edge network, it allows storing and processing the data locally on the outer edges of a network. This reduces the latency of data processing to a great extent.
- Improved processing: As sophisticated low-footprint chipsets are now packed to equip any device with great processing power, carrying out intensive computing tasks locally at the edge of a network is no longer difficult.
- Extensive reach: Thanks to the onboard data collection and processing capabilities, having a boost through edge computing, the network can quickly scale up and expand its services.
- Enhanced visibility: Because of the localized data processing and computing, edge data centers allow more relaxed scrutiny. As data management and computing take place locally at the very edge of the network, what’s going on inside can easily be viewed and evaluated.
- Improved communication and collaboration: Edge computing through extending connectivity power to several networks actually helps better collaboration and communication for real-time value additions. Since the roll-out of the 5G network is already on the horizon, a lot of new IoT devices and sensors across different niches and environments are likely to exchange data and collaborate with data for specific user needs.
Conclusion
The role of edge computing is closely intertwined with the emergence and proliferation of connected IoT devices. Presently, both IoT devices and edge computing actually play a complementary role with significant implications on end-user benefits. With the coordination of connected gadgets, edge computing will bring us a new era of instant gratification.