Network Entity Misinformation Categorization for Wireless Data
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining data quality due to the presence of misinformation and disinformation from rogue actors, which can lead to error propagation and degrade the coordination among multiple actors in perceptive wireless communication systems.
Innovation Solution
A method and apparatus are provided to categorize data elements as misinformation, temporarily exclude them from propagation, and allow for reevaluation based on predefined criteria, using a network entity associated with a machine learning procedure to manage the exclusion and reevaluation of data from rogue actors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data elements are temporarily excluded from propagation when categorized as misinformation, then data quality and reliability are improved, but device complexity and operational complexity increase due to the need for categorization and reevaluation mechanisms
Solution Approach 1:
A network entity acts as an intermediary between wireless devices and the machine learning procedure. This intermediary receives data elements from devices, categorizes them as misinformation or not, and temporarily excludes them from propagation. The intermediary also manages reevaluation requests and coordinates with the machine learning procedure, thereby resolving the contradiction by centralizing complexity in a dedicated component rather than distributing it throughout the system.
Solution Approach 2:
The system segments the data processing function into distinct components: wireless devices that generate data elements, a network entity that performs categorization and exclusion, and a machine learning procedure that provides categorization guidance. This segmentation allows each component to have well-defined responsibilities, reducing overall system complexity while maintaining high reliability through specialized processing at each stage.
2Reliability
If data elements are temporarily excluded from propagation, then error propagation is reduced, but loss of information increases as accurate data may be discarded along with misinformation
Solution Approach 1:
The system implements dynamic data handling where the exclusion status of data elements is not fixed but can change over time. The network entity temporarily excludes data elements categorized as misinformation but can reinstate them upon reevaluation when new information becomes available or conditions change. This dynamic approach prevents permanent loss of potentially accurate data while maintaining error propagation control during active exclusion periods.
Solution Approach 2:
The system performs preliminary categorization of data elements before they are fully propagated through the network. By categorizing data elements as misinformation or not at the point of generation or initial transmission, the system can take preliminary exclusion action to prevent error propagation while preserving the original data for potential reevaluation, thus avoiding unnecessary information loss.
3Adaptability or versatility
If reevaluation criteria are established for misclassified data, then adaptability improves, but device complexity increases due to criteria management and reevaluation coordination
Solution Approach 1:
The system establishes a feedback mechanism where the network entity monitors data elements that were temporarily excluded and can initiate reevaluation when predefined criteria are met. The machine learning procedure provides feedback on categorization accuracy, and the network entity adjusts its exclusion decisions accordingly. This feedback loop enables adaptability to changing conditions while managing complexity through automated, criterion-based decision-making rather than manual intervention.
Data Source
AI summary
An apparatus may be a UE configured to receive, from a network entity associated with a machine learning procedure, a first indication that a first set of data elements transmitted by the wireless device at a first time is categorized as misinformation and that, based on the categorization of the first set of data elements as misinformation, the network entity will temporarily exclude data from the wireless device from propagation as input for a subsequent machine learning procedure. The apparatus may further be configured to receive a second indication of a set of criteria for requesting a reevaluation of the categorization and transmit, based on meeting one or more criteria in the set of criteria, a second set of data elements to the network entity at a second time.