IoT Device Grouping for Edge Computing Bandwidth Reduction
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Solution Overview
Problem
As the number of IoT devices on a network increases, transmitting large amounts of collected data to a cloud data center for processing becomes intensive and may lead to delays, especially in situations requiring near real-time processing.
Innovation Solution
The system dynamically groups heterogeneous IoT devices to form logically singular edged devices, identifies a primary device within each group, deploys a pre-trained tiny machine learning model on these primary devices, and utilizes their compute power to selectively transmit data to a cloud resource, thereby reducing network bandwidth consumption and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all IoT devices transmit data to the cloud data center, then complete data collection is achieved, but network bandwidth consumption increases and processing delays occur
Solution Approach 1:
The patent segments the monolithic data transmission architecture by introducing edge devices that group multiple IoT devices. Each edge device aggregates data from its group and selectively transmits only necessary data to the cloud, dividing the transmission burden and reducing overall bandwidth consumption while maintaining data collection completeness.
Solution Approach 2:
The patent introduces edge devices as intermediary components between IoT devices and the cloud data center. These edge devices perform local data processing, filtering, and aggregation, acting as mediators that reduce the volume of data transmitted to the cloud while ensuring complete and accurate data collection through intelligent selection.
2Reliability
If all IoT devices transmit data to the cloud data center, then complete data collection is achieved, but processing latency increases
Solution Approach 1:
The patent applies preliminary action by having edge devices perform data aggregation, filtering, and preprocessing before transmission to the cloud. This advance processing reduces the amount of data requiring cloud computation and enables faster overall processing while maintaining complete data collection through systematic data preparation.
Solution Approach 2:
The patent segments processing responsibilities by dividing functions between edge devices (local aggregation and filtering) and cloud data centers (comprehensive analysis). This segmentation enables parallel processing where time-sensitive operations occur at the edge, reducing overall latency while maintaining data collection completeness.
3Adaptability or versatility
If heterogeneous IoT devices are managed individually, then device diversity is supported, but system complexity increases
Solution Approach 1:
The patent merges multiple heterogeneous IoT devices into groups managed by single edge devices. Each edge device handles diverse device types through standardized interfaces and protocols, combining multiple devices logically while maintaining their individual characteristics, thus supporting device diversity while reducing management complexity.
Solution Approach 2:
The patent implements universality by designing edge devices with multi-functional capabilities to manage various types of IoT devices through common communication protocols and data formats. This universal approach allows a single edge device to handle diverse device types without increasing overall system complexity.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for transmitting data from a set of edge devices is provided. The embodiment may include identifying one or more Internet-of-Things (IoT) devices within a structure. The embodiment may include combining the identified one or more IoT devices into one or more groups. The embodiment may include identifying a respective primary device for each group of the one or more groups. The embodiment may include deploying a tiny machine learning (ML) model on each identified respective primary device. In response to detection of an event within the structure by a group of the one or more groups, the embodiment may include utilizing the tiny ML model of a primary device of the group to select one or more other groups for activation.