Data from IoT devices must be efficiently managed, stored, and evaluated in large quantities. Two complimentary strategies that can assist you in managing and improving the performance of your Internet of Things infrastructure are cloud and edge computing.
IoT cloud computing
The provision of computer services through the internet, including servers, storage, databases, networking, software, and analytics, is known as cloud computing. With cloud computing, you can pay for just the IoT resources you need, expand up or down based on business needs, and access cutting-edge functionality and security. In addition to managing your IoT devices, cloud platforms enable you to perform data analysis and machine learning on the cloud.
IoT edge computing
Processing data closer to the source, such as on IoT devices or adjacent servers or gateways, is known as edge computing. Edge computing improves the privacy and dependability of your Internet of Things applications while lowering the latency, bandwidth, and cost of data transfers to the cloud. When necessary, you may interface with the cloud using edge devices like the Raspberry Pi, Arduino, or Jetson Nano to perform local logic, filtering, or interpretation of the data produced by your IoT sensors or cameras.
How to combine edge computing and the cloud with IoT
The data produced by IoT devices, the processing and analysis required, network connectivity, power supply availability, security and privacy, and other factors should all be taken into account when integrating IoT with cloud and edge computing. Depending on your needs and use case, pick from cloud-only, edge-only, or cloud-edge hybrid models. Apps with low delayed responses or bandwidth restrictions should use cloud-only services, whereas those with high latency or bandwidth limits and a need for simple or decentralised data processing should use edge-only services. Cloud-edge hybrids work well with applications that have different latency or bandwidth requirements.
Advantages of combining edge computing and the cloud with IoT
Plenty of advantages can result from integrating IoT with cloud and edge computing, including better performance due to lower latency and throughput, increased security due to edge encryption and authentication, lower costs due to less cloud data transfer and storage, and increased innovation due to testing and launching new features on the edge. Applying security updates and rules from the cloud, along with using the cloud’s scalability and flexibility for additional analysis and storage, can help you minimize the cost of your IoT infrastructure while safeguarding your devices and data.
IoT integration problems with cloud and edge computing
IoT integration with cloud and edge computing can be challenging since it calls for the design, development, deployment, and management of a dispersed, heterogeneous system. The requirement for data processing and analysis reliability and interoperability between devices and nodes is also crucial. The availability and dependability of IoT devices and edge nodes must also be preserved, and any possible breakdowns or interruptions in the network’s power supply or connectivity must be handled.
The best approaches to combine edge and cloud computing with IoT
The optimum integration model should be selected, common protocols like MQTT, CoAP, or EdgeX Foundry should be used, and a modular, flexible architecture employing microservices, containers, or serverless operations should be implemented to seamlessly connect IoT with cloud and edge computing. Use strategies for audits, authentication, authorization, and encryption to implement a secure data management plan. Leveraging metrics, logs, alarms, and analytics tools, you can track and debug devices, edge nodes, and cloud services while keeping an eye on the effectiveness and performance of your IoT system.
Summary
In summary, while edge and cloud computing alone have advantages and problems of their own, when combined, they may significantly increase the IoT ecosystem’s efficiency.
