IoT Gateway Data Classification for Edge Cloud Routing
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Solution Overview
Problem
In IoT environments, the limited resources and computing capabilities at the edge devices lead to inefficiencies in processing time-critical data, as transferring data to the remote cloud results in latency and security issues, making it difficult to process sensitive data effectively.
Innovation Solution
An IoT Gateway device classifies data traffic as normal or critical based on time criticalness and confidentiality levels, automatically designating edge computing resources for critical data processing and remote cloud resources for normal data, using container orchestration and machine learning algorithms to manage processing tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If data is transferred to remote cloud for processing, then computing capabilities are improved, but latency increases and security issues arise
Solution Approach 1:
The patent segments data into two categories: time-critical data and non-time-critical data. Time-critical data is processed locally at the edge device, while non-time-critical data is transferred to the remote cloud for processing. This segmentation resolves the contradiction by ensuring that data requiring fast processing remains at the edge, eliminating latency concerns, while still utilizing cloud computing capabilities for less urgent data.
2Power
If data is transferred to remote cloud for processing, then computing capabilities are improved, but security issues arise
Solution Approach 1:
The patent segments data based on security sensitivity, keeping sensitive and time-critical data locally at the edge device while transferring only non-sensitive, non-time-critical data to the cloud. This ensures that security-sensitive information never leaves the secure edge environment, maintaining data security while still benefiting from cloud computing resources for appropriate data types.
Solution Approach 2:
The edge device acts as an intermediary between the data source and the remote cloud. It filters and preprocesses data before cloud transmission, ensuring that only appropriate data leaves the secure edge environment. This intermediary function protects data security by preventing sensitive information from being exposed to potential security risks in the cloud.
3Productivity
If segmentation rules are based on network state, then resource allocation is optimized, but critical data may still be processed in cloud when edge resources are static or under high load
Solution Approach 1:
The patent implements preliminary classification of data as time-critical or non-time-critical before processing decisions are made. This preliminary action ensures that time-critical data is immediately routed to local edge processing without being subject to dynamic resource allocation decisions. By pre-identifying critical data, the system guarantees its processing at the edge regardless of current edge resource state, maintaining reliability while still optimizing overall resource allocation for non-critical data.
Data Source
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AI summary
A method, corresponding system, and computer program product for edge data processing in an Internet of Things (loT) environment is disclosed. The method includes receiving, by an loT Gateway device, a data traffic generated by one or more edge devices in the loT environment. Thereafter, the loT Gateway device classifies the received data traffic as one of a normal data and a critical data. In response to classifying the received data traffic, the loT Gateway device thereafter automatically designates a network location for processing the received data traffic. The network location corresponds to an edge computing resource arranged locally to the loT Gateway device when the received data is classified as critical data and the network location corresponds to a remote cloud computing resource when the received data is classified as normal data.