Implementation of the Internet of Things (IoT) in a Vehicle Maintenance Routine Information System

Abstract

Vehicle maintenance is critical for safety and longevity, yet conventional tracking methods predominantly rely on error-prone manual logging, frequently leading to delayed servicing and severe mechanical failures. To address these operational vulnerabilities, this study adopts the Design Science Research (DSR) framework to develop a proactive, offline-first IoT maintenance monitoring mobile application. The system utilizes an ELM327 On-Board Diagnostics (OBD-II) scanner to extract real-time Engine Control Unit (ECU) telemetry, specifically engine RPM, coolant temperature, and mechanical odometer readings. This data is transmitted directly to a smartphone via Bluetooth, functioning as an edge processing layer. By employing an embedded SQLite database, the application securely manages vehicle profiles, maintenance logs, and user-defined service thresholds locally. An autonomous background algorithm evaluates live telemetry against these parameters to trigger predictive maintenance alerts. Real-world offline testing validated the system’s reliability, confirming its ability to enforce maintenance discipline through timely notifications. Furthermore, the application features robust local data administration, enabling statistical expenditure reporting, CSV/PDF exports, and cascading profile deletion. The results demonstrate that the proposed architecture successfully eliminates internet dependency, guarantees maximum user data privacy, and significantly improves overall vehicle management efficiency.