Leveraging Edge Computing for Advanced Analytics in IoT Systems

Category : Edge Computing for IoT Systems | Sub Category : Edge Analytics in IoT Posted on 2023-09-07 21:24:53

Leveraging Edge Computing for Advanced Analytics in IoT Systems
Introduction:
Traditional cloud-based analytic solutions are facing increasing challenges due to the rapid growth of the Internet of Things. Edge computing is a solution to the limitations of edge computing and can be used to perform advanced analytic at the edge of the network. In this post, we will discuss edge analytics and how it can change the way we process and leverage data in the internet of things.
Understanding edge analytic in the internet of things
Edge analytic refers to the analysis of data at the edge of the network, close to the source of data generation. Edge analytics is a different type of cloud-based analytic that uses local data to make decisions and respond immediately.
Edge Analytics has advantages in the internet of things.
1 There is no need to send huge volumes of data to a distant cloud server for processing, as edge analytics reduces the amount of time it takes. This is essential for applications that are time-sensitive.
2 Edge analytics reduces the risk of data breeches by processing sensitive data locally, rather than sending it to a remote cloud environment. This ensures that critical data is within the boundaries of the edge network.
3 By preprocessing and filtering data at the edge, only relevant data or valuable insights are transmitted to the cloud for further analysis. This reduces the need for data to be transmitted over the network.
4 Real-Time Decision- Making: Edge analytics is a tool that allows for instant decision-making. This is important for applications that need immediate response, such as real-time monitoring in smart cities.
Edge analytic in the internet of things
A combination of edge devices with local processing capabilities and specialized edge analytics platforms is required to implement edge analytics in the internet of things. These platforms integrate machine learning and visualization tools to enable real-time data analysis. Key components include:
1 The Edge Gateway is a bridge between the edge analytics platform and the internet of things.
2 Edge Analytics Engine is a tool that uses machine learning to extract valuable insights and perform real-time decision-making.
3 Local storage stores data that is relevant to reduce dependence on the cloud.
4 Cloud Integration is when Edge analytics platforms allow the transfer of data to the cloud for further analysis or data warehousing.
Edge Analytics can be used in the internet of things.
1 Industrial Automation: Edge analytics allows manufacturers to monitor equipment health, predict failures, andtrigger maintenance activities in real-time.
2 Edge analytics can be used to analyze patient data in real-time, providing healthcare professionals with timely insights for rapid decision-making, remote patient monitoring, and early disease detection.
3 Smart Cities: Edge analytics is a tool that helps with efficient urban planning and resource allocation.
Conclusion
Edge computing combined with analytic tools has become a game-changer in the internet of things landscape. Edge analytics has the ability to process data closer to the source, which opens the door to a wide range of applications. Edge analytic integration into architectures is needed to drive innovation and unlock the full potential of their data.