IoT Data Reception Probability Filtering for Abnormal Influx
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Solution Overview
Problem
In IoT environments, the diffusion of abnormal data among multiple devices is a challenge, particularly due to resource constraints in devices with limited computing power and battery life, and existing methods require frequent updates of reliability information as the number of devices increases.
Innovation Solution
A data receiving method that includes a data receiving unit, a data determination unit, a data prediction unit, and a data receiving determination unit to assess the probability of receiving normal data from peripheral devices, allowing selective data reception based on calculated probabilities, thereby preventing abnormal data influx and maintaining constant resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reliability information is updated and exchanged for each unit hour to prevent abnormal data diffusion, then data reliability is improved, but time and resources increase as the number of devices increases
Solution Approach 1:
The system performs preliminary actions by calculating a probability that next data will be normal data before actually receiving data from peripheral devices. This probability-based pre-assessment allows the reference device to proactively identify and block potentially abnormal data streams, preventing the need for continuous post-reception verification and reliability updates across all devices.
Solution Approach 2:
The invention changes the parameter for data reception decision from binary reliability information to a continuous probability value representing the likelihood of normal data. This probability parameter is calculated based on historical data patterns and used to dynamically adjust data reception decisions, enabling more nuanced and efficient data filtering without requiring extensive inter-device communication.
2Quantity of substance
If data is received from multiple peripheral devices to ensure comprehensive data collection, then data completeness is improved, but the risk of abnormal data influx increases
Solution Approach 1:
The system implements feedback by continuously monitoring the normality of received data and using this information to update the probability calculation for future data reception decisions. When abnormal data is detected, the system adjusts the probability assessment for that peripheral device, creating a closed-loop feedback mechanism that dynamically adapts to data quality patterns while maintaining comprehensive data collection from multiple sources.
Solution Approach 2:
The invention applies local quality by treating each peripheral device's data stream individually with device-specific probability assessments rather than applying a uniform filtering rule to all devices. This allows the system to maintain comprehensive data collection from multiple peripheral devices while applying targeted abnormal data prevention measures to specific devices based on their individual data quality patterns.
3Ease of operation
If all data from peripheral devices is received unconditionally to maintain simple reception logic, then ease of operation is improved, but abnormal data cannot be prevented from entering and spreading
Solution Approach 1:
The system implements self-service by enabling the reference device to autonomously assess the normality probability of incoming data and make independent reception decisions without requiring complex coordination or approval from other devices. The probability calculation and data reception determination are performed automatically based on historical patterns, maintaining operational simplicity while preventing abnormal data entry through intelligent, self-directed filtering.
Data Source
AI summary
Provided are a data receiving method and device to increase a probability of receiving normal data from a plurality of peripheral devices. A data receiving method of a device includes receiving current data from any one peripheral device of a plurality of peripheral devices, determining whether the received current data is normal data, calculating a probability that next data is normal data, on the basis of a result of the determination, and determining whether to receive the next data according to the calculated probability, in which the next data is data transmitted by the any one peripheral device or another peripheral device.