###### Abstract
(2013) [21] used embedded piezoceramic transducers and smart aggregates to track hydration and strength evolution, while [PERSON] et al. (2021) [22] reviewed their widespread use in structural health monitoring due to their high sensitivity and adaptability. Despite their precision, these systems remain limited in construction applications due to cost, fragility, and complex integration. Innovative applications in this field include cement-based carbon nanotube sensors for natural frequency identification [23] and smart piezoelectric sensors for mechanical monitoring [24]. Additionally, comprehensive reviews on the piezoresistive properties of cement-based sensors have highlighted the critical role of conductor content and fabrication methods in sensor performance [25]. These multifunctional sensors have demonstrated the ability to detect stress, strain, cracks, temperature, and humidity, which are essential for evaluating the durability and performance of concrete structures [26; 27]. Beyond hardware advancements, the integration of cutting-edge data analysis methods, such as the Internet of Things (IoT), machine learning, and artificial intelligence, has significantly improved the ability to predict and control concrete behavior. These technologies enable real-time data processing and pattern recognition, enhancing decision-making in construction [28; 29; 30]. [PERSON] et al. (2024) have demonstrated the effectiveness of low-cost IoT-based systems for real-time estimation of concrete strength during curing [31], as also explored in recent EMI-based studies using deep learning approaches for early-age strength prediction [32]. Despite these advancements, challenges remain regarding the high cost and limited durability of high-precision sensors in construction environments [33]. However, efforts to develop low-cost, reliable alternatives have shown promising results. For example, cost-effective sensors with Bluetooth communication protocols have been successfully used for real-time monitoring of concrete strength, reducing both operational costs and energy consumption [34; 35]. Furthermore, microcontroller-based sensor systems have been validated for applications such as concrete curing monitoring [36] or wireless sensors to monitor temperature and humidity in structures [37; 38]. This work aims to develop and validate a low-cost, wireless Arduino-based sensor system to monitor temperature and humidity in early-age concrete elements, particularly in precast production environments, and assess its impact on improving quality control. The system is designed to track key parameters such as temperature and humidity in early-age self-compacting concrete, aiming to improve efficiency and quality in precast concrete production, particularly in demanding industrial environments. Operationally, the goal of this study is to validate a low-cost, wireless sensing workflow that can support precast quality control through a defined set of metrics derived from temperature and humidity histories. Specifically, the system targets (i) thermal metrics (peak temperature \(\mathrm{T}_{\mathrm{P}}\), time to peak \(\mathrm{t}_{\mathrm{P}}\), early age heating rate \(\mathrm{dT}/\mathrm{dt}\), and depth gradient \(\Delta\mathrm{T}\) (surface-core)), (ii) moisture metrics (early age surface humidity loss rate \(\mathrm{dRH}/\mathrm{dt}\) and stabilized \(\mathrm{RH}\) range), and (iii) a maturity-based indicator \(\mathrm{M}(\mathrm{t})\) to estimate strength development under varying curing conditions. The proposed system integrates \(\mathrm{DS18B20}\) temperature sensors and Soil Moisture Sensor v1.2 units, connected to an Arduino board. The collected data are wirelessly transmitted to a computer via Bluetooth for storage and further analysis. A custom-developed Arduino IDE script is used to manage data acquisition and transmission.