The Internet of Things (IoT) has become increasingly relevant in the context of large-scale asset monitoring, revolutionizing the way data is collected and analyzed. In addition to low power consumption and long-range coverage, these applications require precise time synchronization to generate accurate data to assess the structural health of critical assets. Meeting these synchronization requirements is challenging because the devices are battery-powered, deployed in extensive environments, and limited in computational capabilities. LoRaWAN, a widely adopted standard for long-range wireless networks, addresses some of these challenges. In fact, its Class-B operational mode introduces a time synchronization mechanism based on a beacon broadcasting system. However, the energy-intensive nature of this configuration can be inefficient when devices rely on limited power sources. To address this, we introduce a predictive mechanism to dynamically adjust the beacon period in LoRaWAN Class-B networks. Extensive experimentation proves that our approach reduces the average interbeacon synchronization error by three orders of magnitude compared to the LoRaWAN standard. This improvement in timing accuracy enables more efficient energy usage, extending device battery life by more than three times relative to standard configurations. Notably, these gains are achieved with a lightweight implementation requiring only 2.5 KB of memory. Overall, our solution enhances both synchronization performance and energy efficiency in an established standard, making it a practical and cost-effective solution for IoT-based monitoring systems.
A Lightweight Algorithm for Efficient Synchronization in LoRaWAN Class-B Networks
Zanella A.;
2025
Abstract
The Internet of Things (IoT) has become increasingly relevant in the context of large-scale asset monitoring, revolutionizing the way data is collected and analyzed. In addition to low power consumption and long-range coverage, these applications require precise time synchronization to generate accurate data to assess the structural health of critical assets. Meeting these synchronization requirements is challenging because the devices are battery-powered, deployed in extensive environments, and limited in computational capabilities. LoRaWAN, a widely adopted standard for long-range wireless networks, addresses some of these challenges. In fact, its Class-B operational mode introduces a time synchronization mechanism based on a beacon broadcasting system. However, the energy-intensive nature of this configuration can be inefficient when devices rely on limited power sources. To address this, we introduce a predictive mechanism to dynamically adjust the beacon period in LoRaWAN Class-B networks. Extensive experimentation proves that our approach reduces the average interbeacon synchronization error by three orders of magnitude compared to the LoRaWAN standard. This improvement in timing accuracy enables more efficient energy usage, extending device battery life by more than three times relative to standard configurations. Notably, these gains are achieved with a lightweight implementation requiring only 2.5 KB of memory. Overall, our solution enhances both synchronization performance and energy efficiency in an established standard, making it a practical and cost-effective solution for IoT-based monitoring systems.
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