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<p>This paper introduces a memristor based on an Ag/ZnO/FTO structure, fabricated using the chronoamperometric electrodeposition technique, which enables precise control over layer formation and ensures uniformity for improved device performance. The morphological configuration of the switching layer (ZnO) was verified through field emission scanning electron microscopy (FESEM), while X-ray diffraction analysis confirmed the phase formation of the resistive switching layer. The crystallite size of the synthesized sample was determined to be 42.81 nm. Further morphological investigation was carried out using high-resolution transmission electron microscopy (HRTEM). Experimental results revealed that the fabricated memristor exhibits bipolar resistive switching behavior, with an endurance of 5,000 cycles and a data retention time of 10<sup>4</sup> s. Due to its stable and repeatable resistive switching performance, the memristor was integrated into a proposed Internet of Things (IoT) system, designed for electric vehicle (EV) monitoring applications. The system architecture comprises an ESP32 microcontroller, a linear displacement sensor, and an ADXL335 accelerometer. This setup effectively monitors vehicle payload, acceleration, and braking, storing the data for future analysis. A test run involving approximately 9,000 instances demonstrated the system’s capability to detect overloading conditions and sudden jerks by monitoring variations in memristance. This study establishes a promising foundation for employing memristors in future IoT-based vehicle monitoring systems.</p>

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IoT augmented metal oxide memristor-based vehicle monitoring system

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Subrat Kumar Pradhan,
  • Shubham A. Ahir,
  • Tukaram D. Dongale,
  • Jothi Ramalingam Rajabathar,
  • Subramanian Sasikumar,
  • Sujata Chakravarty,
  • Chandra Sekhar Dash,
  • Ranjith Balu

摘要

This paper introduces a memristor based on an Ag/ZnO/FTO structure, fabricated using the chronoamperometric electrodeposition technique, which enables precise control over layer formation and ensures uniformity for improved device performance. The morphological configuration of the switching layer (ZnO) was verified through field emission scanning electron microscopy (FESEM), while X-ray diffraction analysis confirmed the phase formation of the resistive switching layer. The crystallite size of the synthesized sample was determined to be 42.81 nm. Further morphological investigation was carried out using high-resolution transmission electron microscopy (HRTEM). Experimental results revealed that the fabricated memristor exhibits bipolar resistive switching behavior, with an endurance of 5,000 cycles and a data retention time of 104 s. Due to its stable and repeatable resistive switching performance, the memristor was integrated into a proposed Internet of Things (IoT) system, designed for electric vehicle (EV) monitoring applications. The system architecture comprises an ESP32 microcontroller, a linear displacement sensor, and an ADXL335 accelerometer. This setup effectively monitors vehicle payload, acceleration, and braking, storing the data for future analysis. A test run involving approximately 9,000 instances demonstrated the system’s capability to detect overloading conditions and sudden jerks by monitoring variations in memristance. This study establishes a promising foundation for employing memristors in future IoT-based vehicle monitoring systems.