Laboratory of Information and Communication Technologies (LabTIC), Ecole Nationale des Sciences Appliquées de Tanger, Abdelmalek Essaadi University, Tangier BP 1818, Morocco.
Department of Automation, Electrical Engineering and Electronic Technology, Universidad Politécnica de Cartagena, Plaza del Hospital 1, 30202 Cartagena, Spain.
Sensors (Basel). 2022 Jul 28;22(15):5662. doi: 10.3390/s22155662.
Internet of Things (IoT) landscape to cover long-range applications. The LoRa-enabled IoT devices adopt an Adaptive Data Rate-based (ADR) mechanism to assign transmission parameters such as spreading factors, transmission energy, and coding rates. Nevertheless, the energy assessment of these combinations should be considered carefully to select an accurate combination. Accordingly, the computational and transmission energy consumption trade-off should be assessed to guarantee the effectiveness of the physical parameter tuning. This paper provides comprehensive details of LoRa transceiver functioning mechanisms and provides a mathematical model for energy consumption estimation of the end devices EDs. Indeed, in order to select the optimal transmission parameters. We have modeled the LoRa energy optimization and transmission parameter selection problem as a Markov Decision Process (MDP). The dynamic system surveys the environment stats (the residual energy and channel state) and searches for the optimal actions to minimize the long-term average cost at each time slot. The proposed method has been evaluated under different scenarios and then compared to LoRaWAN default ADR in terms of energy efficiency and reliability. The numerical results have shown that our method outperforms the LoRa standard ADR mechanism since it permits the EDs to gain more energy. Besides, it enables the EDs to stand more, consequently performing more transmissions.
物联网（IoT）的应用场景涵盖了远程应用。启用 LoRa 的物联网设备采用自适应数据速率（ADR）机制来分配传输参数，如扩频因子、传输能量和编码率。然而，这些组合的能量评估应该仔细考虑，以选择准确的组合。因此，应该评估计算和传输能量消耗之间的权衡，以保证物理参数调整的有效性。本文提供了 LoRa 收发器功能机制的全面细节，并为端设备 ED 的能量消耗估计提供了数学模型。实际上，为了选择最佳的传输参数，我们将 LoRa 能量优化和传输参数选择问题建模为一个马尔可夫决策过程（MDP）。动态系统会监测环境统计数据（剩余能量和信道状态），并搜索最佳操作，以在每个时隙内最小化长期平均成本。该方法已在不同场景下进行了评估，并在能量效率和可靠性方面与 LoRaWAN 默认 ADR 进行了比较。数值结果表明，我们的方法优于 LoRa 标准 ADR 机制，因为它允许 ED 获得更多的能量。此外，它使 ED 能够站得更稳，从而进行更多的传输。
