Energy-Efficient Adaptive Sensing in Low Power Wide Area Networks
Abstract
Low Power Wide Area Networks (LPWAN) is attracting attention from both research communities and the industry due to its low energy, long-range radio communication. Expectations of the capabilities of the LPWAN technologies, with LoRaWAN and NB-IoT being the two most prominent, are high. Standards organizations, such as the 3GPP, promise end-device battery life of more than ten years. However, little research has focused on investigating how this is achievable.
This thesis provides a study on the energy characteristics of LPWAN end-devices, focusing on the prominent factors leading to increased energy consumption. The research further investigates the potential implications of applying Adaptive Sensing techniques to the system.
From experiments with a Pycom Fipy LoRaWAN device, we show that energy consumption when transmitting uplink packets consists of two parts: an increasing linear factor related to increasing the packet payload size, plus a fixed cost independent of payload. Introducing accumulation of packets, or adaptive sensing, the fixed cost yields a potential reduction in end-device energy consumption of up to 9000 %, compared to using a fixed-rate transmission scheme. The results of the study also show that when adaptive sensing manages to reduce half of the transmitted packets, a seven-month increase in battery life is possible when using a 2400 mAh battery. This increase implies a 65 % extended battery life, even when sending only maximum payload packets.