IoT and Edge Analytics: Decentralized Data Processing
IoT architecture today is designed such that the processing capability starts only at the cloud.
Due to the sheer amount of data being produced in an IoT application, network latency and connectivity issues, there are missed opportunities in terms of missing out on real-time insights and perishing data value.
- Soumika Sarkar, Consultant, RapidValue
Data processing capabilities are moving all the way down to the devices level.
The solution to this is edge computing, where the data processing capability is decentralized and brought all the way down to the root level of the data source.
By moving parts of your workload to the edge your organization can focus on business insights instead of data management.
Since a lot of data would be handled at the edge level, the data sets being sent to the cloud would reduce in volume considerably. Also, with the processing being done close to the data source, issues like latency, delayed insights, perishable data are automatically resolved.
Edge computing minimizes security risks.
The data security issue is resolved as the edge devices are mostly on your premise and only insights flow to the cloud, not the raw data.
Edge Intelligence does not replace Cloud Intelligence. They rather complement each other.
Edge intelligence can help in scenarios where near real time response is required. Hence, threshold-based rule engines and control actions can be moved to the Edge thereby reducing network load and complex decision-making modules which use machine learning and AI should be hosted in cloud.