In-line Wireless Anomaly Detection and Filtering
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Solution Overview
Problem
Debugging wireless systems is challenging due to their complexity and variability in protocol implementations, leading to inefficient and costly traditional debugging methods that consume valuable resources and require reproducing anomalies, which are time-consuming and error-prone.
Innovation Solution
Implementing in-line monitoring techniques that filter and store only relevant data packets at edge nodes, allowing for efficient anomaly detection and reduced network bandwidth usage by transmitting filtered packets and storing them for a threshold time before deletion, thereby reducing resource utilization and improving debugging efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If debug information is collected at all times in the wireless system, then complete debugging data is available, but valuable computing resources and network bandwidth are wasted collecting and processing large amounts of unused data
Solution Approach 1:
The system performs preliminary actions by enabling debug information collection only when anomalies are detected, rather than continuously. The anomaly detection mechanism triggers the data collection process, ensuring resources are used only when necessary for debugging purposes.
Solution Approach 2:
The system extracts and collects only the specific debug information relevant to detected anomalies, rather than collecting all possible debug data continuously. This selective extraction approach reduces resource consumption while maintaining debugging effectiveness.
2Use of energy by moving object
If debug information is collected manually after a bug or anomaly is identified, then resource consumption is reduced, but the underlying data relating to the anomaly is not recorded and the anomaly must occur again to record the data
Solution Approach 1:
The system implements a feedback mechanism where anomaly detection automatically triggers data collection. When an anomaly is detected, the system responds by collecting and storing the relevant debug information, ensuring the data is captured without requiring manual intervention or anomaly reproduction.
Solution Approach 2:
The system performs preliminary data collection automatically when anomalies are detected, capturing the necessary debug information before it is lost. This ensures the data is recorded in real-time without waiting for manual configuration or anomaly reproduction.
3Device complexity
If traditional manual debugging techniques are used with minimal software support, then system complexity is reduced, but debugging costs are unpredictable and lead to workflow disruptions and increased business costs
Solution Approach 1:
The system provides self-service debugging capabilities through automated anomaly detection and data collection. The system independently identifies anomalies, triggers appropriate data collection, and makes debugging information available without requiring manual technician intervention, thereby improving efficiency while maintaining manageable complexity.
Solution Approach 2:
The system changes the operational parameters of debugging from manual to automated, and from continuous to event-triggered. This transformation improves debugging efficiency and predictability while keeping the system complexity within acceptable bounds through selective automation.
Data Source
AI summary
A plurality of digital data packets may be received via a wireless data interface, wired data interface, or data path. Code may be executed for a data communications protocol in which one or more of the filtered digital data packets causes the code to transition to different states of the protocol. A protocol state machine may be executed comprising a plurality of states and a plurality of transitions between the states to simulate correct execution of a particular data communication protocol. An anomaly may be detected between a first particular state of the protocol during the execution of the code and a second particular state of the protocol state machine, and in response, an anomaly event may be generated comprising digital data indicating that the anomaly has occurred. An anomaly event log based on the anomaly event and the filtered digital data packets may be transmitted to a computing device.