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
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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
(This article belongs to the Special Issue Meeting Challenges in IoT: Sensing, Energy Efficiency, and the Implementation)
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%.
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Keywords:
parking occupancy; RSSI; SNR; LoRa; Hidden Markov Model; Deep Learning; Neural Networks
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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 StyleDujić 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
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