Current sensor fingerprint for real-time failure detection and transceiver identification in autonomous systems controlled by artificial intelligence (AI)

Abstract

Sensors are relied upon to represent the physical state of autonomous systems that are controlled by AI. Accurate data from these sensors is required to avoid collisions and catastrophic events in actual situations. Redundant sensors are often used in mission critical sensors to mitigate both unexpected failures and cyberattacks. Redundant sensors double or triple the sensor system cost without giving the user information about which sensor is faulty in real-time. We developed a method that exploits small sensor-to-sensor variations; thereby fingerprinting them. For this purpose, we designed circuitry measuring a TMCS1101A4 magnetic current sensor pair. The small differences between the two sensors and the measurement of the calibrated sensor are amplified and recorded in a database as the current sensed is varied over a range from 0 to 320 mA. Future measurements of the sensor pair may be compared to the stored table in real-time to check sensor accuracy. Sensor failure can be modeled by placing the output of one sensor in an open-circuit condition. Measurements then specify the percentage accuracy that is maintained in real-time without the sensor reporting a fault. Additionally, arrays of sensors are used to generate unique system identifiers that verify the authenticity of the source of the data. These identifiers are not fixed but can be regenerated by a challenge from the server making it more difficult for actors to falsify the sensor data. Larger arrays of sensors will be investigated to generate encryption keys and seeds for Post Quantum Cryptography (PQC).