An IoT-Driven Vehicle Detection Method Based on Multisource Data Fusion Technology for Smart Parking Management System

Lou, Liangliang; Li, Qiang; Zhang, Zengchao; Yang, Rong; He, Wei

doi:10.1109/jiot.2020.2992431

Cited by 28 publications

(5 citation statements)

References 17 publications

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“…The two-level data fusion model proposed in this study fuses the data at the cluster head node and the gateway node, respectively. Several sensor nodes are arranged in each region, and then, a cluster head node is selected in each region according to certain rules according to LEACH protocol to form a clustering structure [17]. In the topology of grassland environment monitoring network, the sensor nodes collect the environmental parameter data, and the cluster head node is responsible for receiving data from each sensor node in the region.…”

Section: Two-level Data Fusion Model For Grasslandmentioning

confidence: 99%

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

et al. 2023

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In order to control the grassland ecological environment, an application method of multisource data fusion technology in the construction of land ecological index is proposed. Due to the high requirements for grassland environmental monitoring, the use of traditional technologies to monitor grassland environmental conditions lacks certain effectiveness, has high investment costs, and consumes a lot of manpower and material resources. The use of sensors to dynamically monitor the grassland environment is conducive to monitoring the environment from a scientific and technological level. By understanding the fusion principle and process of three fusion methods, adaptive weighted average, BP neural network, and D-S evidence theory, the construction of Bashang grassland ecological energy big data platform based on multisource data fusion is proposed. A two-level data fusion model based on grassland environmental monitoring is proposed. Several environmental parameters in the experimental environment were monitored, and the validity of the two-level fusion model was verified by two evaluation indicators, the mean absolute percentage error and the corrosion error. This suggests that a combination of BP neural network and D-S proof theory improves system performance. It provides the possibility for more comprehensive monitoring of grassland ecological environment in the future.

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Section: Two-level Data Fusion Model For Grasslandmentioning

confidence: 99%

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

et al. 2023

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“…The presented work in [ 18 ] involves the employment of a canny edge detection method and a USB camera to define the available parking slots. The work in [ 19 ] is used wireless vehicle detectors and magnetic sensors to define the availability of parking spaces. The four aforementioned works lack tracking and controlling mechanisms.…”

Section: Related Workmentioning

confidence: 99%

A Smart Real-Time Parking Control and Monitoring System

Elfaki,

Messoudi,

Bushnag

et al. 2023

Sensors

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Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle’s license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

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“…In attempts to deal with this stringent energy transformation challenge, the Internet of Things (IoT) has emerged as a potentially effective solution for monitoring and optimizing vehicle performance. By enabling the connection and intercommunication between smart objects, the IoT enables real-time data collection, providing valuable insights into vehicle operation [16,17]. In the automotive field, IoT solutions can be designed around On-Board Diagnostics (OBD-II), a tool that provides access to vehicle data, including information about CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

Andrade,

Medeiros,

Medeiros

et al. 2024

Sustainability

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The air pollution caused by greenhouse gas emissions, particularly carbon dioxide (CO2), is a significant environmental concern that impacts air quality and contributes to global warming. The transportation sector plays a pivotal role in this issue, being a major contributor to CO2 emissions. In light of this situation, this article proposes a methodology that utilizes a supervised learning algorithm to estimate CO2 emissions and compare vehicles fueled with ethanol and gasoline. Additionally, the solution adopts an online, unsupervised machine learning algorithm to identify data outliers and improve the confidence in the results. Furthermore, this work incorporates the concept of digital twins, using virtual models of vehicles to carry out more extensive pollution simulations and allowing the simulation of various types of vehicles and the modeling of realistic traffic scenarios. A supervised machine learning approach was adopted to infer emission data in the model, allowing more comprehensive and meaningful comparisons between real-world and simulated measurements. The performed analyses of pollution emissions for different speeds and sections of routes demonstrate that CO2 emissions from ethanol were significantly lower than those from gasoline, favoring more sustainable fuels even in combustion engine vehicles. Adopting cleaner fuels is perceived as crucial to mitigate the negative effects of climate change, with plant-based fuels like ethanol being crucial during the transition from fossil fuels to a more sustainable vehicular landscape.

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An IoT-Driven Vehicle Detection Method Based on Multisource Data Fusion Technology for Smart Parking Management System

Cited by 28 publications

References 17 publications

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

[Retracted] Application of Multisource Data Fusion Technology in the Construction of Land Ecological Index

A Smart Real-Time Parking Control and Monitoring System

On the Use of Biofuels for Cleaner Cities: Assessing Vehicular Pollution through Digital Twins and Machine Learning Algorithms

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