IoT for intelligent traffic system

Pyykönen, Pasi; Laitinen, Jyrki; Viitanen, Jouko; Eloranta, Pekka; Korhonen, T.

doi:10.1109/iccp.2013.6646104

Cited by 30 publications

(4 citation statements)

References 4 publications

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“…The enabling technologies of IoT include sensors, software, electronics, actuator, etc. The applications of IoT can be found in many fields such as healthcare (Catarinucci et al 2015), transportation (Pyykönen et al 2013), and smart city (Jin et al 2014). IoT can lead to countless benefits, such as significantly enhanced automation, accuracy, efficiency and productivity.…”

Section: Introductionmentioning

confidence: 99%

Digital twin-driven product design framework

Tao

Sui

Liu

et al. 2018

International Journal of Production Research

780

289

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With the advent of new generation information technologies in industry and product design, the big data-driven product design era has arrived. However, the big data-driven product design mainly places emphasis on the analysis of physical data rather than the virtual models, in other words, the convergence between product physical and virtual space is usually absent. Digital twin, a new emerging and fast growing technology which connects the physical and virtual world, has attracted much attention worldwide recently. This paper presents a new method for product design based on the digital twin approach. The development of product design is briefly introduced first. The framework of digital twin-driven product design (DTPD) is then proposed and analysed. A case is presented to illustrate the application of the proposed DTPD method.

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Section: Introductionmentioning

confidence: 99%

Digital twin-driven product design framework

Tao

Sui

Liu

et al. 2018

International Journal of Production Research

780

289

View full text Add to dashboard Cite

show abstract

“…The traffic management system plays an important part in ensuring the nation’s economic stability [ 4 , 126 ]. With the increasing number of vehicles on the road, intelligent traffic signal timing depending on vehicle density can be managed using the IoT, locally deployed RSUs, and security cameras [ 4 , 127 , 128 ].…”

Section: Industry 40 and The Industrial Internet Of Thingsmentioning

confidence: 99%

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

Salih

Rashid

Radovanović

et al. 2022

Sensors

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There is no doubt that new technology has become one of the crucial parts of most people’s lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

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“…The unsupervised learning-based clustering techniques are significantly applied in transport research areas for identifying travel patterns (Pyykonen et al, 2013;Yu et al, 2012). The k-means is a crisp clustering algorithm based on partitioning and the Gaussian Mixture Model (GMM) is a fuzzy-based clustering technique, which is widely used for grouping transport patterns during peak and off-peak hours.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Machine Learning-Based Cluster Analysis Mechanism for IoT Data

Balakrishna

2023

International Journal of Hyperconnectivity and the Internet of Things

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The prevailing developments in internet of things (IoT) and other sensor technologies such as cyber physical systems (CPS) and wireless sensor networks (WSNs), the huge amount of sensor data has been generating from various IoT devices and protocols. Making predictions and finding density patterns over such data is a challenging task. In order to find the density patterns and make analysis over real-time dynamic data, the machine learning (ML) based algorithms are widely used to deal with the IoT data. In this article, the authors proposed an efficient ML-based cluster analysis mechanism for finding density patterns in IoT dynamic data effectively. In this proposed mechanism, the k-means and GMM models are used for clustering data analysis. The proposed mechanism has been implemented on ThingSpeak Cloud platform for analysing the data efficiently on daily and weekly basis. Finally, the proposed mechanism acquired superior results than the existing benchmarked mechanisms over all the performance evaluation metrics used for analysis over IoT dynamic data.

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IoT for intelligent traffic system

Cited by 30 publications

References 4 publications

Digital twin-driven product design framework

Digital twin-driven product design framework

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

An Efficient Machine Learning-Based Cluster Analysis Mechanism for IoT Data

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