Adaptive RSSI Adjustment for V2X Misbehavior Detection
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Solution Overview
Problem
Current misbehavior detection techniques in V2X communications, particularly in vehicle-to-everything (V2X) systems, are inadequate as they can be circumvented by software algorithms, and existing methods based solely on application layer data are not effective in identifying abnormal transmissions such as location spoofing, which can lead to safety hazards and traffic congestion.
Innovation Solution
A method and apparatus for abnormal transmission identification in V2X communications that involve determining a signal propagation context, obtaining RSSI and distance values, generating an adjusted RSSI value, and comparing it to a predetermined RSSI-to-distance relationship model to identify abnormal transmissions, thereby enhancing the detection of misbehavior in V2X systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If misbehavior detection is based solely on application layer data, then the detection method is simple to implement, but it can be circumvented by software algorithms and is ineffective against location spoofing
Solution Approach 1:
The detection system is divided into multiple independent layers: application layer data analysis and physical layer RSSI-to-distance relationship verification. Each layer operates independently and cross-validates findings, preventing software algorithms from circumventing detection by manipulating only application layer data.
Solution Approach 2:
The RSSI-to-distance relationship model serves as an intermediary verification mechanism between the transmitting and receiving devices. This physical layer mediator provides objective evidence that cannot be easily spoofed, complementing the application layer detection methods.
2Measurement precision
If signal propagation context is considered for RSSI adjustment, then the accuracy of abnormal transmission identification is improved, but the computational complexity increases
Solution Approach 1:
Signal propagation context parameters (urban, suburban, rural environments) are predetermined and stored in the RSSI-to-distance relationship model before actual detection occurs. During operation, the system only needs to retrieve and apply the appropriate pre-calculated model, avoiding complex real-time calculations while maintaining high detection accuracy.
3Ease of operation
If a predetermined RSSI-to-distance relationship model is used, then the detection process is simplified, but it may not accurately reflect real-world signal propagation variations
Solution Approach 1:
The system maintains multiple predetermined RSSI-to-distance relationship models corresponding to different signal propagation contexts (urban, suburban, rural). The appropriate model is selected based on the detected environment, allowing the system to adapt to real-world variations without complex calculations. This approach balances simplicity with accuracy by changing model parameters based on environmental context.
Data Source
AI summary
Methods, apparatuses, systems, and non-transitory computer-readable medium are disclosed relating to abnormal transmission identification. One method comprises, at a receiving device, receiving a V2X message from a transmitting device. The method further comprises determining a signal propagation context for the receiving device and obtaining an RSSI value and a distance value for the V2X message. The method further comprises generating an adjusted RSSI value based on (1) the RSSI value and (2) the signal propagation context for the receiving device. The method further comprises obtaining a predetermined RSSI-to-distance relationship model and comparing an adjusted RSSI-to-distance data pair, comprising the adjusted RSSI value and the distance value, to the predetermined RSSI-to-distance relationship model. The method further comprises, in response to determining that the adjusted RSSI-to-distance data pair fails a criterion for conforming to the predetermined RSSI-to-distance relationship model, identifying the V2X message as an abnormal transmission.