Citation :

Subrat Kumar Pradhan, Saswat Panda, Sujata Chakravarty, Chandra Sekhar Dash, "IoT-Augmented Framework for Driver Behavior and Terrain Identification using ZnO-Based Nanoionic Memristor," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 323-334, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P123

Abstract

This study presents an IoT-enabled memristor-based nano-ionic system with its ZnO device to detect the vehicle terrain variations. An ADXL335 accelerometer is utilized to show driving dynamics, capture real-time motion data, and senses three perpendicular movements along X, Y, and Z axes. Under changing conditions, the electrical behaviour of the proposed memristor model can be analyzed by applying the measured signals. With precise timing information, the sensor data can be acquired and even stored by employing an ESP32 microcontroller, and so accurate analyses can be observed at a later stage. During the observations, the acceleration and deceleration patterns are reflected by the X-axis, while the Y-axis indicates side-to-side motion, and vertical displacements are recorded by the Z-axis owing to surface unevenness. The collected data is stored on a microSD card and subsequently used to evaluate the memristor model performance. The bipolar resistive switching behaviour is demonstrated by the ZnO-based Memristor, which is caused by the migration of oxygen vacancies, enabling the transition between high resistance and low resistance states through SET and RESET operations. On this basis, for each axis, approximately 6000 data samples were recorded, and for a detailed study, 500 samples per axis were chosen as representative points. Consistent hysteresis in conduction behaviour is produced by distinct variation in resistance led by changes in terrain conditions, and the characteristic here provides an effective difference between uneven, smooth, and rough road surfaces. Moreover, the Memristor, with its nonvolatile Nature, can retain electrical values associated with specific terrains, and the findings suggest that the IoT-based vehicle monitoring systems can be effectively integrated with this approach, offering improved durability, data retention, and performance for advanced intelligent transport applications.

Keywords

Accelerometer Sensor, EV Monitoring, Filamentary Current, Nano Ionic Memristor, Resistive Switching, ZnO Conduction.

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