Next Article in Journal
Photovoltaic Self-Consumption in Industrial Cooling and Refrigeration
Previous Article in Journal
A Fast and Accurate Maximum Power Point Tracking Approach Based on Neural Network Assisted Fractional Open-Circuit Voltage
Open AccessArticle

Sensing Occupancy through Software: Smart Parking Proof of Concept

Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture in Split (FESB), University of Split, 21000 Split, Croatia
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(12), 2207; https://doi.org/10.3390/electronics9122207
Received: 29 November 2020 / Revised: 14 December 2020 / Accepted: 17 December 2020 / Published: 21 December 2020
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%. View Full-Text
Keywords: parking occupancy; RSSI; SNR; LoRa; Hidden Markov Model; Deep Learning; Neural Networks parking occupancy; RSSI; SNR; LoRa; Hidden Markov Model; Deep Learning; Neural Networks
Show Figures

Figure 1

MDPI and ACS Style

Dujić Rodić, L.; Perković, T.; Županović, T.; Šolić, P. Sensing Occupancy through Software: Smart Parking Proof of Concept. Electronics 2020, 9, 2207. https://doi.org/10.3390/electronics9122207

AMA Style

Dujić Rodić L, Perković T, Županović T, Šolić P. Sensing Occupancy through Software: Smart Parking Proof of Concept. Electronics. 2020; 9(12):2207. https://doi.org/10.3390/electronics9122207

Chicago/Turabian Style

Dujić Rodić, Lea; Perković, Toni; Županović, Tomislav; Šolić, Petar. 2020. "Sensing Occupancy through Software: Smart Parking Proof of Concept" Electronics 9, no. 12: 2207. https://doi.org/10.3390/electronics9122207

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop