A Novel Approach Toward Enhancing the Quality of Life in Smart Cities Using Clouds and IoT-Based Technologies

Mishra, Kamta Nath; Chakraborty, Chinmay

doi:10.1007/978-3-030-18732-3_2

Cited by 44 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thresholding is the method of producing black and white image out of a grey scale image by setting exactly those pixels to white whose value is above a specified threshold, allocating other pixels to black [24,26]. It is possible to construct binary images from greyscale images by using thresholding.…”

Section: Threshold Processing Methodsmentioning

confidence: 99%

Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

View full text Add to dashboard Cite

The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R‐wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R‐wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R‐wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.

show abstract

Section: Threshold Processing Methodsmentioning

confidence: 99%

Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…So far, the majority of focus has been on government and energy initiatives, followed by transportation, building, and water, among other goals. A number of research and development efforts on smart cities and DT applications have been generated [92][93][94][95][96]. Similar to smart manufacturing, smart cities are the combination of a wide range of IoT domains to address the complex challenges in cities and must incorporate cloud/edge computing with big data collection and analysis as essential techniques to drive the realization of efficiency and optimization [11,97].…”

Section: Smart Citiesmentioning

confidence: 99%

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

et al. 2022

View full text Add to dashboard Cite

The Internet of Things (IoT) connects massive smart devices to collect big data and carry out the monitoring and control of numerous things in cyber-physical systems (CPS). By leveraging machine learning (ML) and deep learning (DL) techniques to analyze the collected data, physical systems can be monitored and controlled effectively. Along with the development of IoT and data analysis technologies, a number of CPS (smart grid, smart transportation, smart manufacturing, smart cities, etc.) adopt IoT and data analysis technologies to improve their performance and operations. Nonetheless, directly manipulating or updating the real system has inherent risks. Thus, creating a digital clone of a real physical system, denoted as a Digital Twin (DT), is a viable strategy. Generally speaking, a DT is a data-driven software and hardware emulation platform, which is a cyber replica of physical systems. Meanwhile, a DT describes a specific physical system and tends to achieve the functions and use cases of physical systems. Since DT is a complex digital system, finding a way to effectively represent a variety of things in timely and efficient manner poses numerous challenges to the networking, computing, and data analytics for IoT. Furthermore, the design of a DT for IoT systems must consider numerous exceptional requirements (e.g., latency, reliability, safety, scalability, security, and privacy). To address such challenges, the thoughtful design of DTs offers opportunities for novel and interdisciplinary research efforts. To address the aforementioned problems and issues, in this paper, we first review the architectures of DTs, data representation, and communication protocols. We then review existing efforts on applying DT into IoT data-driven smart systems, including the smart grid, smart transportation, smart manufacturing, and smart cities. Further, we summarize the existing challenges from CPS, data science, optimization, and security and privacy perspectives. Finally, we outline possible future research directions from the perspectives of performance, new DT-driven services, model and learning, and security and privacy.

show abstract

“…Die aktuellen Forschungs und Entwicklungsaktivitäten im Bereich Digitaler Zwillinge sind äußerst vielschichtig und multidisziplinär [1]. Bereits seit mehr als 20 Jahren werden sie erfolgreich im Maschinen und Fahrzeugbau sowie bei industriellen Fertigungsabläufen oder in cyber physischen Systemen eingesetzt [2,3]. Im Bauwesen wird der Mehrwert Digitaler Zwillinge zurzeit in mehreren Forschungs(groß)projekten untersucht und implementiert, die u. a. von der Europäischen Union, der Deutschen For schungsgemeinschaft und von verschiedenen Bundes ministerien gefördert werden [4][5][6][7][8].…”

Section: Sensorintegrierte Digitale Zwillinge Für Das Automatisierte ...unclassified

Sensorintegrierte Digitale Zwillinge für das automatisierte Monitoring von Infrastrukturbauwerken

2022

View full text Add to dashboard Cite

Das automatisierte Bauwerksmonitoring auf Basis Digitaler Zwillinge hat sich als eine zeit‐ und kostengünstige Ergänzung zu manuellen Inspektionen bewährt, um ein vorausschauendes, prädiktives Instandhaltungsmanagement von Infrastrukturbauwerken zu gewährleisten. Digitale Zwillinge verknüpfen Sensordaten mit digitalen Modellen und Algorithmen, sodass Zustands‐ und Bestandsinformationen generiert und den Entscheidungsträgern in Echtzeit zur Verfügung gestellt werden können. Üblicherweise ist ein Digitaler Zwilling jedoch als sogenannte Single Source of Truth in Form zentraler Modelle auf einem zentralen Server situiert, sodass sehr große Datenmengen von den Sensoren vor Ort zum zentralen Server gesendet werden und der Server bei einem Ausfall das gesamte Monitoring zum Erliegen bringen würde. Im vorliegenden Beitrag werden die Ergebnisse einer Machbarkeitsstudie vorgestellt, in der Digitale Zwillinge nicht mehr nur als Zentralmodell realisiert, sondern als digitale Modelle mit Algorithmen direkt in die Sensoren eingebettet werden. Jeder Sensor enthält damit einen partiellen Digitalen Zwilling seiner Umgebung, der mit den Digitalen Zwillingen weiterer Sensoren kommuniziert, sodass sich durch die Summe der einzelnen Modelle ein vollwertiger Digitaler Zwilling des gesamten Bauwerks ergibt. Die Machbarkeitsstudie wird beispielhaft anhand eines drahtlosen Bauwerksmonitoringsystems umgesetzt und dessen Praktikabilität am Monitoring einer Fußgängerbrücke aufgezeigt.

show abstract

A Novel Approach Toward Enhancing the Quality of Life in Smart Cities Using Clouds and IoT-Based Technologies

Cited by 44 publications

References 27 publications

Heart rate monitoring of physical fitness training load based on wavelet transform

Heart rate monitoring of physical fitness training load based on wavelet transform

Digital Twin—Cyber Replica of Physical Things: Architecture, Applications and Future Research Directions

Sensorintegrierte Digitale Zwillinge für das automatisierte Monitoring von Infrastrukturbauwerken

Contact Info

Product

Resources

About