Intelligent Transportation Related Complex Systems and Sensors

Kyamakya, Kyandoghere; Chedjou, Jean Chamberlain; Machot, Fadi Al; Mosa, Ahmad Haj; Bagula, Antoine

doi:10.3390/s21062235

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…CPS-W, like most CPS [32] combines an Internet of Things based sensing and actuation subsystem with Fog/Cloud computing [17]. This combination has been used in numerous applications, such as in health [33], transport [34] [35] and environmental monitoring [36]. The nodes in WaterNet would be wirelessly interconnected by a two-layer LoRa network.…”

Section: The Water Monitoring Networkmentioning

confidence: 99%

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

et al. 2022

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Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting. To this end this study proposes a network architecture to collect data on water parameters in real-time and use machine learning (ML) tools to automatically determine suitability of water samples for drinking and irrigation purposes. The developed monitoring network is based on LoRa and takes the land topology into consideration. Results of simulations done in Radio Mobile revealed a partial mesh network topology as the most adequate. Due to the absence of large and open datasets on drinking and irrigation water, datasets usable for training ML models were developed. Three ML models -Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) were considered for the water classification and results obtained showed that LR performed best for drinking water, while SVM was better suited for irrigation water. Recursive feature elimination was then combined with the three ML models to reveal which of the water parameters had the greatest influence on the classification accuracies of the respective model.

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Section: The Water Monitoring Networkmentioning

confidence: 99%

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

et al. 2022

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“…There are different algorithms and solutions for traffic management systems architecture [59][60][61]. Most of them use video cameras attached to masts or traffic light poles to analyze the traffic [36,62].…”

Section: Traffic Management Issuesmentioning

confidence: 99%

Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication

et al. 2021

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This article deals with automated urban traffic management, and proposes a new comprehensive infrastructure solution for dynamic traffic direction switching at intersection lines. It was assumed that the currently used solutions based on video monitoring are unreliable. Therefore, the Radio Frequency IDentification (RFID) technique was introduced, in which vehicles are counted and, if necessary, identified in order to estimate the flows on individual lanes. The data is acquired in real time using fifth-generation wireless communications (5G). The Pots and Ising models derived from the theory of statistical physics were used in a novel way to determine the state of direction traffic lights. The models were verified by simulations using data collected from real traffic observations. The results were presented for two exemplary intersections.

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Automation Control System for Rail Transit Equipment in Forest Areas

Hou,

Wang

2023

2023 China Automation Congress (CAC)

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Intelligent Transportation Related Complex Systems and Sensors

Abstract: Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITSs) are being widely adopted worldwide to improve the efficiency and safety of the transportation system [...]

Cited by 3 publications

References 24 publications

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes

Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication

Automation Control System for Rail Transit Equipment in Forest Areas

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