Edge Network Policy-Based Payload Delivery for IoT Data
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Solution Overview
Problem
Current IoT and IoE data processing methods rely on 'store first, analyze later' approaches in cloud and backend servers, which are inefficient and unable to handle the vast amounts of data generated by connected devices, lacking real-time processing and policy-based delivery capabilities.
Innovation Solution
Implementing policy-based payload delivery at edge network devices, where rules and schemas are applied to traffic streams to filter, index, and actuate actions based on conditions, allowing for real-time processing and delivery of specific data to multiple endpoints through edge network devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all IoT and IoE data is processed in cloud and backend servers using 'store first, analyze later' approach, then data processing can be performed with centralized resources, but the system cannot handle the vast amounts of data generated and lacks real-time processing capability
Solution Approach 1:
The patent segments data processing functionality by deploying edge computing devices at network edges closer to data sources. These edge devices perform local data processing, filtering, and analysis, while cloud backend handles aggregate processing. This segmentation enables real-time processing at the edge while maintaining centralized cloud capabilities, resolving the contradiction between handling vast data volumes and achieving real-time processing.
Solution Approach 2:
The patent introduces edge computing devices as intermediary components between IoT data sources and cloud backend servers. These edge devices act as mediators that pre-process, filter, and selectively forward data to the cloud, reducing the data burden on centralized systems while enabling real-time processing at the edge. This intermediary approach resolves the contradiction by distributing processing responsibilities across multiple hierarchical levels.
2Productivity
If all IoT and IoE data is stored and processed in cloud backend servers, then centralized data management is achieved, but the system becomes inefficient and unable to handle the enormous data volume
Solution Approach 1:
The patent extracts data processing functionality from the centralized cloud backend and distributes it to edge computing devices. Edge devices extract and process data locally, filtering out unnecessary information before forwarding to the cloud. This extraction approach reduces the data volume that must be transmitted and processed centrally, thereby improving overall system efficiency while maintaining centralized management capabilities.
Solution Approach 2:
The patent implements preliminary data processing, filtering, and analysis at edge computing devices before data reaches the cloud backend. By performing these actions in advance at the edge, the system reduces the volume of data requiring centralized processing, improving efficiency. The cloud backend then receives pre-processed, filtered data, making centralized management more manageable despite the original enormous data volume.
3Adaptability or versatility
If traditional cloud-based data processing is used, then centralized resource utilization is achieved, but the system lacks context-aware processing and policy-based delivery capabilities
Solution Approach 1:
The patent implements local quality by enabling edge computing devices to perform context-aware processing specific to their local environments and data sources. Each edge device can apply localized policies, filters, and analysis relevant to its specific context, while the cloud backend maintains overall system coordination. This local quality approach provides context-aware processing without requiring complete centralization, managing complexity through distributed intelligence.
Solution Approach 2:
The patent introduces dynamic policy-based data delivery where processing and delivery behaviors can be adjusted based on changing conditions, priorities, and contexts. Edge devices and cloud backend can dynamically modify processing rules, data routing, and delivery policies in response to real-time requirements. This dynamic capability enables adaptability while managing complexity through flexible, rule-based control mechanisms rather than rigid centralized management.
Data Source
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AI summary
Information describing a rule to be applied to a traffic stream, comprising e.g. traffic from Internet of Things, IoT, devices, is received at an edge network device. The traffic stream is received at the edge network device. A schema is applied to the traffic stream at the edge network device. It is determined that a rule triggering condition has been met. The rule is applied to the traffic stream, at the edge network device, in response to the rule triggering condition having been met. At least one of determining that the rule triggering event has taken place or applying the rule is performed based on the applied schema.