Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1083808

IoT Wallet: Machine Learning-based Sensor Portfolio Application


Šolić, Petar; Lojić Kapetanović, Ante; Županović, Tomislav; Kovačević, Ivo; Perković, Toni; Popovski, Petar
IoT Wallet: Machine Learning-based Sensor Portfolio Application // 5th International Conference on Smart and Sustainable Technologies (SpliTech)
Bol, Croatia (virtual), 2020. str. 1-5 doi:10.23919/SpliTech49282.2020.9243699 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1083808 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
IoT Wallet: Machine Learning-based Sensor Portfolio Application

Autori
Šolić, Petar ; Lojić Kapetanović, Ante ; Županović, Tomislav ; Kovačević, Ivo ; Perković, Toni ; Popovski, Petar

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
5th International Conference on Smart and Sustainable Technologies (SpliTech) / - , 2020, 1-5

Skup
5th International Conference on Smart and Sustainable Technologies 2020

Mjesto i datum
Bol, Croatia (virtual), 23-26.09.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Internet of Things ; LoRa ; deep learning ; time series modeling ; long short-term memory neural networks

Sažetak
In this paper an application for building sensor wallet is presented. Currently, given system collects sensor data from The Things Network (TTN) cloud system, stores the data into the Influx database and presents the processed data to the user dashboard. Based on the type of the user, data can be viewed-only, controlled or the top user can register the sensor to the system. Moreover, the system can notify users based on the rules that can be adjusted through the user interface. The special feature of the system is the machine learning service that can be used in various scenarios and is presented throughout the case study that gives a novel approach to estimate soil moisture from the signal strength of a given underground LoRa beacon node.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike, strojarstva i brodogradnje, Split

Citiraj ovu publikaciju

Šolić, Petar; Lojić Kapetanović, Ante; Županović, Tomislav; Kovačević, Ivo; Perković, Toni; Popovski, Petar
IoT Wallet: Machine Learning-based Sensor Portfolio Application // 5th International Conference on Smart and Sustainable Technologies (SpliTech)
Bol, Croatia (virtual), 2020. str. 1-5 doi:10.23919/SpliTech49282.2020.9243699 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Šolić, P., Lojić Kapetanović, A., Županović, T., Kovačević, I., Perković, T. & Popovski, P. (2020) IoT Wallet: Machine Learning-based Sensor Portfolio Application. U: 5th International Conference on Smart and Sustainable Technologies (SpliTech) doi:10.23919/SpliTech49282.2020.9243699.
@article{article, year = {2020}, pages = {1-5}, DOI = {10.23919/SpliTech49282.2020.9243699}, keywords = {Internet of Things, LoRa, deep learning, time series modeling, long short-term memory neural networks}, doi = {10.23919/SpliTech49282.2020.9243699}, title = {IoT Wallet: Machine Learning-based Sensor Portfolio Application}, keyword = {Internet of Things, LoRa, deep learning, time series modeling, long short-term memory neural networks}, publisherplace = {Bol, Croatia (virtual)} }

Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font