A Survey of LoRaWAN-Integrated Wearable Sensor Networks for Human Activity Recognition: Applications, Challenges and Possible Solutions

Abstract

Long-Range Wide Area Networks (LoRaWAN), a prominent technology within Low-Power Wide Area Networks (LPWANs), have gained traction in remote monitoring due to their long-range communication, scalability, and low energy consumption. Compared to other LPWANs like Sigfox, Ingenu Random Phase Multiple Access (Ingenu-RPMA), Long-Term Evolution for Machines (LTE-M), and Narrowband Internet of Things (NB-IoT), LoRaWAN offers superior adaptability in diverse environments. This adaptability makes it particularly effective for Human Activity Recognition (HAR) systems. These systems utilize wearable sensors to collect data for applications in healthcare, elderly care, sports, and environmental monitoring. Integrating LoRaWAN with edge computing and Internet of Things (IoT) frameworks enhances data processing and transmission efficiency. However, challenges such as sensor wearability, data payload constraints, energy efficiency, and security must be addressed to deploy LoRaWAN-based HAR systems in real-world applications effectively. This survey explores the integration of LoRaWAN technology with wearable sensors for HAR, highlighting its suitability for remote monitoring applications such as Activities of Daily Living (ADL), tracking and localization, healthcare, and safety. We categorize state-of-the-art LoRaWAN-integrated wearable systems into body-worn, hybrid, objectmounted, and ambient sensors. We then discuss their applications and challenges, including energy efficiency, sensor scalability, data constraints, and security. Potential solutions such as advanced edge processing algorithms and secure communication protocols are proposed to enhance system performance and user comfort. The survey also outlines specific future research directions to advance this evolving field.