# A Low-Cost IoT-Based Bidirectional Torque Measurement System with Strain Gauge Technology
## 2 Related Work  
Recent advances in IoT-enabled torque sensing and machine learning-based calibration have opened new possibilities for cost-effective measurement systems [9; 10]. Recent advances in IoT-enabled torque sensing highlight the role of modern modulation schemes in boosting throughput and link robustness. Building on this trend, the present design adopts standard 2.4 GHz Wi-Fi, while future iterations will incorporate adaptive modulation similar to the OFDM-based strategies reported for industrial IoT gateways and the fine-time-measurement (FTM) techniques validated in vehicular telemetry at rotational speeds above 1000 rotations per minute [9; 10]. These studies demonstrate that dynamic selection of sub-carriers and precise timing protocols can lower the packet error rate below 1% and cut latency to sub-10 ms, metrics that are essential for distributed, multi-sensor torque monitoring in harsh electromagnetic environments [9; 10]. Neural network-based calibration methods have shown significant improvements in accuracy for force-torque sensors [11; 12; 13], while wireless sensor networks have enabled distributed monitoring in industrial applications. However, most existing DIY solutions [14; 15] focus on simple servo torque measurement or lack comprehensive bidirectional calibration and error analysis. Compared to existing low-cost solutions, the presented system uniquely combines bidirectional polynomial calibration, IoT connectivity, and comprehensive error analysis within a single platform, addressing the specific needs of automotive drivetrain applications. However, the evolution of torque measurement techniques has been driven by the need to address dynamic errors and environmental influences, which can significantly affect measurement accuracy [3; 5; 6; 16]. Strain-gauge-based systems, while widely adopted, are particularly susceptible to such errors, as highlighted in recent studies [6; 17]. The present sensor was chosen to address the lack of access to expensive dynamometric stands and high-end torque transducers in many academic and industrial laboratories. A low-cost, field-installable alternative therefore enables routine drivetrain testing under strict budgets, and once validated, it could permanently remain on vehicles to assist traction, braking, and stability control. The integration of wireless sensor networks and industrial IoT has enabled the development of distributed, real-time monitoring systems for mechanical parameters, including torque [7]. These systems leverage wireless communication protocols and modular architectures to facilitate remote data acquisition and analytics, supporting the digital transformation of manufacturing environments envisioned by Industry 4.0. Smart, connected sensors are now fundamental components in enabling adaptive control, digital twins, and data-driven decision-making in modern factories [7]. Despite these advances, ensuring measurement accuracy and reliability in such systems remains a major challenge, particularly when these systems are subjected to varying operational and environmental conditions. Recent research has made significant strides in addressing these challenges. Unlike earlier low-cost or unidirectional solutions, this work demonstrates a bidirectional, IoT-enabled torque transducer that achieves \(\pm\)2.5 N\(\cdot\)m accuracy for under EUR 30 through open-source hardware and polynomial calibration. For example, [PERSON] et al. [3] developed and calibrated a high-precision torque measurement system for industrial robot reducters. Their work systematically addressed the effects of additional torque introduced by the transmission chain, such as friction and elastic deformation, which can introduce systematic errors into the measurement process. To compensate for these errors, the authors implemented an improved particle swarm optimization and Levenberg-Marquardt algorithm-based radial basis function neural network, allowing for effective error compensation beyond traditional linear calibration.