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: | |
|---|---|
| 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 |
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Clock parameter tracking under temperature variations for time synchronization in wireless sensor networksMonfort Grau, MarcWireless sensor networksClocks and watches--RepairingSincronització de rellotgesXarxes de sensors sense filsCompensació de temperaturaPredicció del desfasament de rellotgeTinyMLLoRaWANClock SynchronizationWireless Sensor NetworksTemperature CompensationClock Skew PredictionTinyMLLoRaWANXarxes de sensors sense filsRellotges--ReparacióDuty-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.Universitat Politècnica de CatalunyaHuan, Xintao20252025-06-1620252025-11-21master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/446737reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4467372026-05-27T15:37:01Z |
| dc.title.none.fl_str_mv |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| title |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| spellingShingle |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks Monfort Grau, Marc 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 TinyML LoRaWAN Xarxes de sensors sense fils Rellotges--Reparació |
| title_short |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| title_full |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| title_fullStr |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| title_full_unstemmed |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| title_sort |
Clock parameter tracking under temperature variations for time synchronization in wireless sensor networks |
| dc.creator.none.fl_str_mv |
Monfort Grau, Marc |
| author |
Monfort Grau, Marc |
| author_facet |
Monfort Grau, Marc |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Huan, Xintao |
| dc.subject.none.fl_str_mv |
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 TinyML LoRaWAN Xarxes de sensors sense fils Rellotges--Reparació |
| topic |
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 TinyML LoRaWAN Xarxes de sensors sense fils Rellotges--Reparació |
| description |
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. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025-06-16 2025 2025-11-21 |
| dc.type.none.fl_str_mv |
master thesis http://purl.org/coar/resource_type/c_bdcc NA http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/masterThesis |
| format |
masterThesis |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/2117/446737 |
| url |
https://hdl.handle.net/2117/446737 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Universitat Politècnica de Catalunya |
| publisher.none.fl_str_mv |
Universitat Politècnica de Catalunya |
| dc.source.none.fl_str_mv |
reponame:UPCommons. Portal del coneixement obert de la UPC instname:Universitat Politècnica de Catalunya (UPC) |
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Universitat Politècnica de Catalunya (UPC) |
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UPCommons. Portal del coneixement obert de la UPC |
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UPCommons. Portal del coneixement obert de la UPC |
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1869413889149501440 |
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15,228081 |