Predictive Network Decoding for Low-Power IoT Data Reception
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Solution Overview
Problem
Terminal devices in IoT networks face issues with power consumption and communication delays due to insufficient signal strength or poor signal quality, particularly when failing to receive data, leading to increased power usage and longer wait times for retransmissions.
Innovation Solution
A network data processing method and device that employs an Open Systems Interconnection (OSI) model, generating and processing data blocks with error detection and encoding, allowing for predictive decoding to improve decoding performance and reduce power consumption by enabling early termination of the decoding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the terminal device continues to turn on circuits to receive repeatedly transmitted data, then the data reception capability is maintained, but the power consumption increases
Solution Approach 1:
The patent applies preliminary action by predicting data before it is actually received. The terminal device generates predicted data in advance based on previously received data patterns, so when the actual data arrives, the device can quickly compare and verify against the pre-generated prediction, avoiding the need to keep circuits continuously active for repeated reception attempts
2Reliability
If the terminal device waits for retransmission after failing to receive data, then the data integrity is ensured, but the communication delay increases
Solution Approach 1:
The terminal device performs preliminary data prediction and generates expected data patterns before the actual transmission completes. When data is received, the device can immediately compare against the predicted data to verify integrity, eliminating the need to wait for retransmission and significantly reducing communication delay while maintaining data integrity
Solution Approach 2:
The patent implements feedback by using previously received data to generate predictions about upcoming data. This feedback mechanism allows the terminal device to continuously update its predictions based on historical patterns, enabling early verification of received data without waiting for retransmission, thus reducing delay while ensuring integrity
3Measurement precision
If the terminal device performs complete decoding process, then the decoding accuracy is ensured, but the power consumption and processing time increase
Solution Approach 1:
The terminal device performs preliminary decoding by generating predicted data before complete reception. This allows the device to start verification early and terminate the complete decoding process as soon as the predicted data matches the received data, ensuring decoding accuracy while significantly reducing power consumption by avoiding unnecessary complete decoding cycles
Solution Approach 2:
The patent applies partial action by performing only the necessary portion of the decoding process. Instead of always completing the full decoding sequence, the device performs decoding partially and terminates early when the predicted data matches the received data, maintaining accuracy while reducing energy consumption by avoiding excessive processing
4Measurement precision
If the terminal device performs complete decoding process, then the decoding accuracy is ensured, but the processing time increases
Solution Approach 1:
The terminal device performs preliminary decoding by generating predicted data before complete reception. This enables early termination of the decoding process when the prediction matches the received data, ensuring decoding accuracy is maintained while significantly reducing the time required for complete decoding
Solution Approach 2:
The patent applies skipping by allowing the terminal device to rush through the decoding process selectively. When the predicted data matches the received data, the device skips the remaining unnecessary decoding steps and proceeds directly to data processing, maintaining accuracy while minimizing processing time
Data Source
AI summary
A network data prediction method applied to a device that implements an OSI model is provided. The device communicates with a target network device that implements the OSI model. The method includes the following steps: generating a transmission data according to a communication protocol of a first abstraction layer, the transmission data being able to be processed by a first peer abstraction layer of the target network device, and the first peer abstraction layer corresponding to the first abstraction layer and obeying the communication protocol; generating a predicted data according to the communication protocol and the transmission data; and transmitting the transmission data and the predicted data to a second abstraction layer.


