Edge Device Platform for IoT Data Routing
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Solution Overview
Problem
As the number of IoT devices connecting to wireless communication networks increases, traditional centralized IoT ecosystems face challenges in efficiently managing data communications, leading to network congestion, latency, and unnecessary data transmission, which affects performance and resource utilization.
Innovation Solution
Integration of a device platform within a core network or multi-access edge computing environment that uses a communication management component trained with machine learning or AI to analyze device characteristics and data, determining which data to forward, thereby optimizing data processing and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a centralized IoT ecosystem is used to manage data communications, then device connectivity and data transmission are simplified, but network congestion and latency increase due to the large number of devices
Solution Approach 1:
The patent segments the centralized IoT ecosystem into distributed edge computing nodes deployed at network edges (base stations, gateways). Each edge node independently processes data from local devices, dividing the monolithic centralized system into multiple autonomous segments that handle regional traffic independently, thereby reducing network latency and congestion while maintaining simplified device connectivity.
2Quantity of substance
If all device data is transmitted through the centralized core network, then comprehensive data collection is achieved, but network congestion and resource utilization deteriorate
Solution Approach 1:
The patent extracts data processing functions from the centralized core network and relocates them to distributed edge computing nodes. Each edge node collects and processes data locally, extracting only essential information or aggregated results for transmission to the core network. This reduces the volume of data traversing the core network, thereby improving network efficiency while maintaining comprehensive data collection capabilities through coordinated edge nodes.
3Device complexity
If traditional centralized processing is used, then system architecture is simplified, but latency and unnecessary data transmission increase
Solution Approach 1:
The patent adds a spatial dimension to the system architecture by deploying edge computing nodes across multiple geographic locations at network perimeters. This transforms the single-point centralized architecture into a distributed multi-dimensional structure where data can be processed at the nearest edge node, reducing transmission distance and processing latency while maintaining architectural manageability through standardized edge node interfaces.
4Productivity
If machine learning/AI processing is implemented at the edge, then data processing efficiency improves, but device platform complexity increases
Solution Approach 1:
The patent implements universal edge device platforms with standardized hardware and software stacks that can perform multiple functions including machine learning inference, data processing, and protocol translation. These multi-functional platforms reduce individual device complexity by consolidating diverse processing requirements into unified architectures, while maintaining high data processing efficiency through integrated AI/ML capabilities.
Data Source
AI summary
Techniques for integrating a device platform in a core network or MEC environment, and managing data communications associated with devices are presented. The device platform, integrated with the core network or MEC environment, can comprise a communication management component (CMC) that can manage communication of data associated with devices connected to the core network. CMC can receive data and metadata from a device, analyze the data and metadata, and, based on the analyzing and data management criteria, determine whether any, all, or a portion of the data is to be communicated to a second device associated with the core network or associated communication network. CMC can be trained, using machine learning, to learn to identify device types, communication protocols, and data payload formats of devices. Based on the analyzing and the training, CMC can determine the device type, communication protocol, and data payload format associated with the device.