A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City

Chi, Tsun-Kuang; Chen, Hsiao‐Chi; Chen, Shih-Lun; Abu, Patricia Angela R.

doi:10.3390/s21061936

Cited by 7 publications

(3 citation statements)

References 27 publications

(39 reference statements)

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“…In [18], the energy consumption of a solid waste management system is addressed in a simulated scenario that also allows the estimation of battery life. Similarly, the authors of [19] present a self-optimizing wireless water level monitoring system that can improve battery life based on operating conditions. Energy consumption is also a major constraint for the authors of [20], where a LoRa-based localization system is addressed.…”

Section: Related Work and Contributionmentioning

confidence: 99%

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

et al. 2023

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

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Section: Related Work and Contributionmentioning

confidence: 99%

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

et al. 2023

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“…As well as providing an overview of such a network, they implemented a simulation tool to assess criticisms of the protocol, particularly the collision rate behavior. In [ 11 ], a water level monitoring system using multiple LoRa devices was described. Notably, the authors introduced a multi-step strategy to guarantee high levels of accuracy and efficiency in data transmission.…”

Section: Related Work and Contributionmentioning

confidence: 99%

An IoT Measurement System Based on LoRaWAN for Additive Manufacturing

Fedullo

Morato

Peserico

et al. 2022

Sensors

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The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible.

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“…With the continuous advancement of technology, artificial intelligence has shined in many industries, such as vehicle image recognition [ 11 ], smart city [ 12 ], product recommendation systems [ 13 ], and emotion recognition [ 14 ]. The development of medical imaging is also changing with each passing day.…”

Section: Introductionmentioning

confidence: 99%

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

et al. 2022

Self Cite

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Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold preprocessing technique for image segmentation, which can achieve an accuracy rate of more than 96%; (2) a better and more intuitive apical lesions symptom enhancement technique; and (3) a model for apical lesions detection with an accuracy as high as 96.21%. Compared with existing state-of-the-art technology, the proposed model has improved the accuracy by more than 5%. The proposed model has successfully improved the automatic diagnosis of apical lesions. With the help of automation, dentists can focus more on technical and medical diagnoses, such as treatment, tooth cleaning, or medical communication. This proposal has been certified by the Institutional Review Board (IRB) with the certification number 202002030B0.

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A High-Accuracy and Power-Efficient Self-Optimizing Wireless Water Level Monitoring IoT Device for Smart City

Cited by 7 publications

References 27 publications

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

An IoT Measurement System Based on LoRaWAN for Additive Manufacturing

A High-Accuracy Detection System: Based on Transfer Learning for Apical Lesions on Periapical Radiograph

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