Cloud computing has had a profound impact on how organizations use and deploy networks. From widely adopted public clouds to more expensive private clouds, and more complex and multifaceted hybrid or multi-cloud environments, cloud computing has helped bring flexibility, versatility, and scalability to businesses. For the first time, cloud solutions have enabled people to access computing resources anytime, anywhere, without being limited by device interfaces and location.
Cloud computing has brought about a true revolution in the computing world. But it is merely a stepping stone to a better future.
While the steam engine played a revolutionary role in the history of scientific progress, it was a milestone that paved the way for more advanced electric motors. Similarly, we now have edge computing as a more advanced successor to address the shortcomings of cloud environments.
Cloud computing requires collecting data and sending it to the cloud after performing computational tasks, and then receiving the results. This entire process introduces latency issues because data must be transferred between data centers in different regions. Edge computing solves this latency problem by facilitating storage and computation near where the data is generated.
To date, all data storage, data management, and computing tasks for IoT devices have primarily relied on cloud computing environments. However, as edge computing emerges as a more reliable, performance-driven, and efficient solution, IoT devices can further enhance their output and efficiency by joining edge networks. By participating in edge networks, IoT devices can also further improve the speed, performance, and responsiveness of edge computing networks.
Edge computing and Internet of Things
Edge computing can effectively solve the connectivity problems faced by IoT devices. By moving critical data processing functions to the network edge or near the data source, edge computing can help connected devices maintain the same level of efficiency even in situations with poor network connectivity.
While most IoT devices have limited processing power, edge computing helps them by handling locally intensive processing needs at the very edge of the network. This enables IoT devices to respond to various urgent demands with near-zero latency. The ever-evolving need for local processing power by IoT devices is actually driving the development of edge computing networks to consolidate edge data centers. Edge data centers and local data processing also provide an additional layer of security between IoT devices and central cloud servers.
Key Functions of Core IoT Edge Architecture
While IoT devices and sensors are increasingly appearing in edge networks, it's important for future IoT application developers and strategists to understand the key components and characteristics of IoT edge architecture. Let's take a look at these key components or features.
Complex Event Management: Complex Event Processing (CEP) software solutions are built in the cloud and pushed to the network edge.
Machine learning and artificial intelligence models: Machine learning models are trained locally on devices to adapt to user preferences and extract relevant insights from user data that is further processed at the network edge and cloud servers.
IoT Applications: By using CEP software solutions and ML models, many IoT devices can now run applications at the network edge.
Offline support: Offline data storage at the network edge helps address issues related to data availability when necessary.
Data Management: IoT devices use edge computing to answer calls, asking what data they must store and process in edge data centers, and what data they must transfer to the cloud for further computation.
Five ways the Internet of Things adds value to edge networks
IoT devices can significantly improve network efficiency and performance by participating in edge computing networks. Here are the main ways IoT devices bring high value-added capabilities to edge networks:
IoT devices can significantly improve network efficiency and performance by participating in edge computing networks. Here are some key ways IoT devices bring high added value to edge networks:
Data Acquisition: When IoT devices join an edge network, it allows data to be stored and processed locally at the network's external edge. This significantly reduces data processing latency.
Improved processing power: With advanced low-power chipsets being packaged to equip any device with powerful processing capabilities, performing intensive computing tasks locally at the network edge is no longer difficult.
Extensive coverage: Thanks to onboard data collection and processing capabilities, enhanced by edge computing, the network can rapidly expand and extend its services.
Enhanced visibility: Edge data centers allow for easier auditing due to localized data processing and computation. Because data management and computation take place at the very edge of the network, the internal situation can be easily viewed and assessed.
Improved communication and collaboration: Edge computing actually facilitates better collaboration and communication by extending connectivity to multiple networks, enabling real-time value-added services. With the rollout of 5G networks imminent, many new IoT devices and sensors across different niches and environments are likely to exchange and collaborate on data based on specific user needs.
in conclusion
The role of edge computing is closely related to the emergence and widespread adoption of connected IoT devices. Currently, IoT devices and edge computing actually play complementary roles, significantly impacting the interests of end users. With the coordination of connected devices, edge computing will usher in a new era of instant gratification.
