Adaptive IoT Edge Processing Models for Bandwidth Optimization
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Solution Overview
Problem
IoT devices face challenges in efficiently processing and transmitting large volumes of data across networks, leading to performance issues and increased power consumption, especially when implementing data processing models that require significant network bandwidth and computational resources.
Innovation Solution
Implementing data filters at IoT devices to selectively transmit only necessary data to a hub device, while processing functions and model portions are dynamically configured and updated based on network topology and performance changes, with secure and unsecure model portions managed separately to optimize network performance and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data processing models are implemented at the hub device to analyze data from multiple IoT devices, then data analysis capability is improved, but network bandwidth consumption increases
Solution Approach 1:
The patent segments the data processing model into multiple components: edge devices perform local data filtering and preprocessing, the hub device performs intermediate processing, and the provider network performs comprehensive analysis. This segmentation distributes computational load across the network hierarchy, reducing bandwidth consumption while maintaining analysis capability.
Solution Approach 2:
The patent implements preliminary data filtering and preprocessing at edge devices before data is transmitted to the hub. By performing initial data processing at the source, unnecessary data is filtered out beforehand, reducing the volume of data that needs to be transmitted and processed at the hub, thus conserving network bandwidth.
2Loss of information
If a large volume of data is transmitted from IoT devices to the hub device, then data completeness is improved, but network performance deteriorates
Solution Approach 1:
The patent applies different processing qualities at different network levels: edge devices perform basic filtering to remove obviously irrelevant data, the hub performs intermediate processing on partially filtered data, and the provider network performs comprehensive analysis. This local quality approach ensures data completeness is maintained where needed while optimizing network performance by not transmitting all raw data.
Solution Approach 2:
The patent implements partial data transmission where only selected portions of data that meet certain criteria are transmitted to the hub. This partial action approach maintains sufficient data completeness for effective analysis while avoiding the excessive transmission of all raw data, thus preserving network performance.
3Loss of energy
If data filters are implemented at IoT devices to reduce data transmission, then network bandwidth consumption is reduced, but data processing complexity at edge devices increases
Solution Approach 1:
The patent implements dynamic filtering where the filtering criteria and processing functions at edge devices are not fixed but can be adapted based on network conditions, device capabilities, and data characteristics. This dynamic approach allows the system to optimize the balance between filtering complexity and bandwidth savings in different operational contexts.
Solution Approach 2:
The patent incorporates feedback mechanisms where the hub device and provider network can send updates and adjustments to edge device filters based on observed performance. This feedback loop allows the system to learn from actual data patterns and network conditions, automatically optimizing filter complexity to achieve the best bandwidth savings without excessive processing overhead.
4Productivity
If processing functions are dynamically updated based on network conditions, then network performance is optimized, but system complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically monitors network conditions and dynamically adjusts processing functions without requiring manual intervention. The edge devices, hub, and provider network work autonomously to detect performance issues and update processing functions accordingly, optimizing network performance while managing complexity through automation.
Data Source
AI summary
A hub device of a network receives topology data for the network that indicates changes in a topology or performance of the network. The hub device determines modifications to processing functions for edge devices of the network based on the topology data. The hub device deploys the modifications to respective edge devices of the network. An edge device may collect data and use a processing function to perform operations on the data, generate processed data, and send the processed data to the hub device for further processing. In some cases, a remote provider network receives topology data for the network and generates modifications to processing functions for edge devices of the network based on the topology data. The remote provider network then transmits the modifications to the network.