Wearable IoT power consumption optimization algorithm

Sabin, Banuleasa; Munteanu, Radu; Rusu, Alexandru

doi:10.1109/epe50722.2020.9305609

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Different researchers have proposed energy reduction techniques to lower energy consumption either in IoT-based systems [ 20 , 21 ], or in Wireless Sensor Networks (WSN) [ 22 , 23 ]. In [ 22 ], the authors proposed a task-based model for minimizing energy consumption in WSNs.…”

Section: Background Scenic Reviewmentioning

confidence: 99%

Energy-Aware IoT-Based Method for a Hybrid On-Wrist Fall Detection System Using a Supervised Dictionary Learning Technique

Othmen

Baklouti

Lazzaretti

et al. 2023

Sensors

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In recent decades, falls have posed multiple critical health issues, especially for the older population, with their emerging growth. Recent research has shown that a wrist-based fall detection system offers an accessory-like comfortable solution for Internet of Things (IoT)-based monitoring. Nevertheless, an autonomous device for anywhere-anytime may present an energy consumption concern. Hence, this paper proposes a novel energy-aware IoT-based architecture for Message Queuing Telemetry Transport (MQTT)-based gateway-less monitoring for wearable fall detection. Accordingly, a hybrid double prediction technique based on Supervised Dictionary Learning was implemented to reinforce the detection efficiency of our previous works. A controlled dataset was collected for training (offline), while a real set of measurements of the proposed system was used for validation (online). It achieved a noteworthy offline and online detection performance of 99.8% and 91%, respectively, overpassing most of the related works using only an accelerometer. In the worst case, the system showed a battery consumption optimization by a minimum of 27.32 working hours, significantly higher than other research prototypes. The approach presented here proves to be promising for real applications, which require a reliable and long-term anywhere-anytime solution.

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