VEHIOT: Design and Evaluation of an IoT Architecture Based on Low-Cost Devices to Be Embedded in Production Vehicles

Redondo, Jonatan Pajares; González, Lisardo Prieto; Guzmán, Javier García; Boada, Beatriz L.; Díaz, Vicente · 2018 · OpenAlex-citations

DOI: 10.3390/s18020486

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the need for affordable, reliable sensor systems to monitor vehicle dynamics for stability and control applications. While high-end systems like the Racelogic VBOX provide accurate data, their prohibitive cost prevents widespread integration into commercial vehicles. The authors propose VEHIOT, an Internet of Things (IoT) architecture utilizing low-cost single-board computers and inertial measurement units (IMUs) to estimate critical variables such as lateral acceleration and roll rate. The study aims to evaluate whether these low-cost alternatives can match the accuracy, acquisition time, and reliability of professional-grade equipment under dynamic driving conditions. The experimental design involved embedding three sensor kits into a Ford Fiesta: a high-end VBOX 3i with dual-antenna GPS serving as the ground truth, a Raspberry Pi 3 Model B paired with a BNO055 IMU, and an Intel Edison paired with an LSM9DSO IMU. All kits were mounted at the vehicle’s center of gravity and synchronized via a WiFi router and custom software architecture. The researchers conducted eight controlled driving maneuvers, including roundabouts, lane changes, and normal circulation, to generate high-dynamic data. Data were sampled at 100 Hz, and performance was evaluated by calculating root mean square (RMS) error, norm error, and maximum error against the VBOX readings. Reliability was assessed by tracking successful data acquisition rates across the tests. The results revealed significant disparities in reliability and accuracy. The Intel Edison and VBOX achieved 100% reliability, successfully completing all eight tests. In contrast, the Raspberry Pi failed in 62.5% of the tests, demonstrating poor reliability due to connectivity and processing issues. Regarding accuracy, the Intel Edison exhibited higher errors than the Raspberry Pi in the successful trials. For lateral acceleration, the Intel Edison showed a higher RMS error (0.0692 g) and significantly higher maximum error (0.3844 g) compared to the Raspberry Pi (0.0541 g RMS; 0.2063 g max). Similarly, for roll rate, the Intel Edison recorded higher RMS and maximum errors, attributed to greater sensitivity to noise and data scattering. The Raspberry Pi, when functioning, provided data closer to the ground truth but suffered from frequent system failures. The study concludes that while low-cost IoT architectures are viable for vehicle dynamics estimation, current implementations face trade-offs between cost, reliability, and precision. The Intel Edison offered superior reliability but lower accuracy due to noise, whereas the Raspberry Pi provided better accuracy but lacked the robustness required for consistent operation. These findings highlight the challenges of integrating consumer-grade hardware into safety-critical automotive systems and suggest that further improvements in hardware stability and noise filtering are necessary before such low-cost solutions can reliably replace professional equipment in production vehicles.

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• v2x connected vehicle