learning model for battery lifetime prediction of LoRa sensors in additive manufacturing
نویسندگان
چکیده
Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by requirements arising from Industry 4.0 Industrial Internet Things (IIoT) paradigms. In fact, unprecedented challenges capabilities are being faced, with ever-increasing need collect reliable yet accurate data mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption predicting battery life key issues that be accurately addressed in such IoT-based systems. This case additive manufacturing application considered this work, where smart sensors embedded manufactured artifacts reliably transmit their measured better control production final use, despite physically inaccessible. A Low Power Wide Area Network (LPWAN), particular LoRaWAN (Long Range WAN), represents a promising solution ensure connectivity aforementioned scenario, optimized minimize while guaranteeing long-range operation low- cost deployment. presented application, LoRa equipped monitor set meaningful parameters throughout lifetime. context, once embedded, they inaccessible, only power source originally installed battery. paper, lifetime prediction estimation problems thoroughly investigated. For purpose, model based on Artificial Neural (ANN) proposed, developed starting discharge curve lithium-thionyl chloride batteries used application. The results experimental campaigns carried out real were compared those tune it appropriately. obtained encouraging pave way interesting future developments.
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ژورنال
عنوان ژورنال: Acta IMEKO
سال: 2023
ISSN: ['0237-028X', '2221-870X']
DOI: https://doi.org/10.21014/actaimeko.v12i1.1400