Cloud computing architecture has been proposed as a recent advancement to address the difficulties of implementing globally distributed Internet of Things (IoT) applications. The fundamental problem with existing IoT implementations is how to transfer and handle the enormous amounts of data produced by IoT devices on top of remote cloud computing resources. This causes significant delays and traffic congestion because the current mobile network is unable to send this much data without encountering an excessive amount of latency. While all data generated at edge devices must pass over the network and may be exposed in the course of transmission, this scenario may also result in privacy concerns when it comes to cloud computing. Since sensitive data must be obtained and handled in cloud computing environments, data privacy may also be jeopardized there. As an alternative to cloud computing and in order to decrease the sending of duplicate data to cloud-based resources, edge computing is discussed in the context of IoT.
In edge computing, bringing computations towards the edges, data can be analyzed and decisions can be made faster, which enables rapid response times, lower latency, and reduced traffic load. Additionally, the microservice architecture of edge devices allows AI components to offer real-time IoT services in a distributed manner. This means that data does not need to go through the network but can be processed locally, resulting in decreased network traffic and improved privacy.
To achieve these capabilities, real-time deep learning AI algorithms can be designed at the edge using microservices. This approach enables faster analysis of data and decision-making, as well as the ability to offer real-time IoT services.
Overall, cloud computing architecture has been proposed as a solution to address the challenges of implementing globally distributed IoT applications. However, edge computing offers a more efficient and secure solution, enabling real-time data analysis and decision-making capabilities, reducing network latency, and decreasing traffic load. By designing real-time deep learning AI algorithms using microservices at the edge, organizations can achieve these capabilities and improve their IoT implementations.
- Dr. Upul Jayasinghe, email