Cognitive Computing Architecture for IoT Latency Reduction
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Solution Overview
Problem
The modern Internet of Things (IoT) environment requires an efficient technology platform to accommodate cognitive computing applications with billions of sensors, necessitating advanced data processing and analytics while ensuring data validity, preventing overload, and maintaining performance across various events.
Innovation Solution
A cognitive computing architecture that includes edge analytics devices for preliminary data validation, fog computing for decentralized infrastructure, and cloud computing for scalable analytics, utilizing machine learning and statistical modeling to generate decision systems and regulate IoT sensors, ensuring data integrity and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all IoT sensor data is transmitted to cloud computing devices for processing, then comprehensive analytics can be performed, but network bandwidth is consumed and latency increases
Solution Approach 1:
The patent segments the data processing function across multiple levels: edge analytics devices perform preliminary filtering and validation, fog computing devices handle intermediate processing, and cloud computing devices perform comprehensive analytics. This segmentation allows critical functions to be performed locally (reducing latency) while maintaining comprehensive cloud-based analytics capability.
Solution Approach 2:
The patent introduces fog computing devices as intermediary components between edge devices and cloud computing devices. These intermediaries perform distributed processing, reducing the burden on cloud infrastructure and minimizing data transmission requirements while maintaining comprehensive analytics capability.
2Productivity
If edge analytics devices perform preliminary analysis to filter data, then network bandwidth is reduced and latency is lowered, but data validity must be ensured
Solution Approach 1:
The patent implements preliminary data validation and filtering at the edge analytics device before data is transmitted to cloud computing devices. This preliminary action ensures that only valid, relevant data enters the network, maintaining data quality while improving processing efficiency and reducing network bandwidth consumption.
3Speed
If a decentralized fog computing infrastructure is implemented, then system responsiveness is improved and cloud bandwidth is reduced, but infrastructure complexity increases
Solution Approach 1:
The patent adds a spatial dimension to the computing architecture by introducing fog computing devices at intermediate network locations between edge devices and cloud computing devices. This dimensional expansion creates a multi-layered architecture that improves responsiveness through local processing while distributing complexity across multiple tiers rather than concentrating it at a single level.
Data Source
AI summary
A system and method provide cognitive computing services, comprising: one or more Internet of Things (IoT) sensors; an edge analytics device that performs a preliminary analysis of the IoT sensor data; a cloud computing device of the cloud environment that stores a set of application programming interfaces (APIs) for interfacing between the cloud environment and the IoT sensors and an underlying infrastructure; an analytics device that performs analytics on the IoT sensor data; and a cognitive computing device that regulates one or more of the IoT sensors or the edge analytics device by modifying one or more rules performed by the one or more of the IoT sensors or the edge analytics device.