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Pregled bibliografske jedinice broj: 1099963

Sensing Occupancy through Software: Smart Parking Proof of Concept


Dujić Rodić, Lea; Perković, Toni; Županović, Tomislav; Šolić, Petar
Sensing Occupancy through Software: Smart Parking Proof of Concept // Electronics, 9 (2020), 12; 2207, 28 doi:10.3390/electronics9122207 (međunarodna recenzija, članak, znanstveni)


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Naslov
Sensing Occupancy through Software: Smart Parking Proof of Concept

Autori
Dujić Rodić, Lea ; Perković, Toni ; Županović, Tomislav ; Šolić, Petar

Izvornik
Electronics (2079-9292) 9 (2020), 12; 2207, 28

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
parking occupancy ; RSSI ; SNR ; LoRa ; Hidden Markov Model ; Deep Learning ; Neural Networks

Sažetak
In order to detect the vehicle presence in parking slots, different approaches have been utilized, which range from image recognition to sensing via detection nodes. The last one is usually based on getting the presence data from one or more sensors (commonly magnetic or IR-based), controlled and processed by a micro-controller that sends the data through radio interface. Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver. This paper presents an alternative, cost-effective beacon-based mechanism for sensing the vehicle presence. It is based on the well-known effect that, once the metallic obstacle (i.e., vehicle) is on top of the sensing node, the signal strength will be attenuated, while the same shall be recognized at the receiver side. Therefore, the signal strength change conveys the information regarding the presence. Algorithms processing signal strength change at the receiver side to estimate the presence are required due to the stochastic nature of signal strength parameters. In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data. In order to extract the information of presence, the Hidden Markov Model (HMM) was employed with accuracy of up to 96%, while the Neural Network (NN) approach reaches an accuracy of up to 97%. The given approach reduces the costs of the sensor production by at least 50%.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
UIP-2017-05-4206 - Internet stvari: istraživanja i primjene (IoTRA) (Šolić, Petar, HRZZ - 2017-05) ( CroRIS)

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split

Profili:

Avatar Url Toni Perković (autor)

Avatar Url Lea Dujić Rodić (autor)

Avatar Url Petar Šolić (autor)

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Dujić Rodić, Lea; Perković, Toni; Županović, Tomislav; Šolić, Petar
Sensing Occupancy through Software: Smart Parking Proof of Concept // Electronics, 9 (2020), 12; 2207, 28 doi:10.3390/electronics9122207 (međunarodna recenzija, članak, znanstveni)
Dujić Rodić, L., Perković, T., Županović, T. & Šolić, P. (2020) Sensing Occupancy through Software: Smart Parking Proof of Concept. Electronics, 9 (12), 2207, 28 doi:10.3390/electronics9122207.
@article{article, author = {Duji\'{c} Rodi\'{c}, Lea and Perkovi\'{c}, Toni and \v{Z}upanovi\'{c}, Tomislav and \v{S}oli\'{c}, Petar}, year = {2020}, pages = {28}, DOI = {10.3390/electronics9122207}, chapter = {2207}, keywords = {parking occupancy, RSSI, SNR, LoRa, Hidden Markov Model, Deep Learning, Neural Networks}, journal = {Electronics}, doi = {10.3390/electronics9122207}, volume = {9}, number = {12}, issn = {2079-9292}, title = {Sensing Occupancy through Software: Smart Parking Proof of Concept}, keyword = {parking occupancy, RSSI, SNR, LoRa, Hidden Markov Model, Deep Learning, Neural Networks}, chapternumber = {2207} }
@article{article, author = {Duji\'{c} Rodi\'{c}, Lea and Perkovi\'{c}, Toni and \v{Z}upanovi\'{c}, Tomislav and \v{S}oli\'{c}, Petar}, year = {2020}, pages = {28}, DOI = {10.3390/electronics9122207}, chapter = {2207}, keywords = {parking occupancy, RSSI, SNR, LoRa, Hidden Markov Model, Deep Learning, Neural Networks}, journal = {Electronics}, doi = {10.3390/electronics9122207}, volume = {9}, number = {12}, issn = {2079-9292}, title = {Sensing Occupancy through Software: Smart Parking Proof of Concept}, keyword = {parking occupancy, RSSI, SNR, LoRa, Hidden Markov Model, Deep Learning, Neural Networks}, chapternumber = {2207} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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