Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

• Published in: International journal of network management Vol. 34; no. 1
• Main Authors: Rajyalakshmi, Vankadhara, Lakshmanna, Kuruva
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01.01.2024
• Subjects: Algorithms; Availability; Deep learning; DenseNet; detection; Internet of Things; Machine learning; mobile edge computing; Model accuracy; Optimization; Parking facilities; parking spots; smart cities
• ISSN: 1055-7148; 1099-1190
• DOI: 10.1002/nem.2228

Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT‐based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO‐based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities. This paper presents an efficient technique called Hybrid Deep DenseNet Optimization for predicting parking slot availability in smart cities, with high accuracy of 99.19% and a low error rate of 0.0306%. The experimental results show that the Adam optimizer outperforms SGD, AdaDelta, AdaGrad, and RMSprop. Due to the migration of people, the shortage of natural resources is managed more innovatively with the help of new technologies like deep learning, which protects the environment from pollution in smart cities.