Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks
Duty-cycled wireless sensor networks rely on tight clock synchronisation to fuse data and schedule radio activity. Yet the frequency of low-cost quartz oscillators drifts non-linearly with temperature, ageing, and supply voltage, so a network that forgoes active time-sync quickly accumulates errors...
| Autor: | |
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/446737 |
| Acceso en línea: | https://hdl.handle.net/2117/446737 |
| Access Level: | acceso abierto |
| Palabra clave: | Wireless sensor networks Clocks and watches--Repairing Sincronització de rellotges Xarxes de sensors sense fils Compensació de temperatura Predicció del desfasament de rellotge TinyML LoRaWAN Clock Synchronization Wireless Sensor Networks Temperature Compensation Clock Skew Prediction Rellotges--Reparació |
| Sumario: | Duty-cycled wireless sensor networks rely on tight clock synchronisation to fuse data and schedule radio activity. Yet the frequency of low-cost quartz oscillators drifts non-linearly with temperature, ageing, and supply voltage, so a network that forgoes active time-sync quickly accumulates errors far beyond the typical ±10 ppm budget. Frequent two-way time-stamp exchanges or temperature-compensated crystals can correct this drift, but they inflate radio airtime, energy consumption, and bill-of-materials cost. The challenge is to extend synchronisation intervals without new hardware or loss of accuracy. This thesis proposes a firmware-only clock-parameter predictor that combines physics-guided curve-fitting with a TinyML neural network to estimate skew in real time, thereby reducing the need for explicit sync messages. Five temperature-indexed skew traces from a LoRa SX1276 mote were recorded between 18 °C and 27 °C. A four-parameter stretched-exponential captures the monotonic decay of each trace, while a cubic term models a transient HVAC-induced overshoot. Synthetic extrapolation balances all traces to 1 500 samples each without extra lab time, and hold-out tests validate the augmentation. A lightweight multilayer perceptron, (in = 2) ¿ 64 ¿ 64 ¿ (out = 1), trained on the augmented dataset, it predicts skew with a mean absolute error of 1.0 × 10¿7 (0.10 ppm), including temperatures withheld during training. The resulting drop in radio airtime yields substantial energy savings without compromising synchronisation accuracy or network reliability. The work enables self-calibrating, sub-ppm clock recovery on battery-powered IoT devices without additional silicon, paving the way for longerlived networks, improved duty cycling, and potential side applications such as node identification via temperature-dependent skew fingerprints. |
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