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Based on an in-depth survey of the literature, the purpose of the paper is to explore environmentally sustainable urban development and Internet of Things connected sensors in cognitive smart cities. Using and replicating data from Capgemini, EIU, ESI ThoughtLab, JLL, KPMG, ICMA, SCC, and UKTI, I performed analyses and made estimates regarding how networked and integrated sustainable urban technologies optimize interconnected sensor networks through digital urban governance in smart and environmentally sustainable cities. Data-driven planning technologies can upgrade the standard of living by gathering massive volumes of data from Internet of Things sensing infrastructures. Data-driven Internet of Things systems integrate computationally networked urbanism and sustainable governance networks across Internet of Things sensing infrastructures by harnessing machine learning-based analytics. Smart and environmentally sustainable cities leverage datadriven planning technologies and machine learning-based analytics. Networked and integrated sustainable urban technologies develop on data-driven Internet of Things systems and machine learning-based analytics. Smart sustainable city governance and management can be enhanced by harnessing Internet of Things sensors, urban big data analytics, data-driven Internet of Things systems, and sensor-based big data applications. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

This paper analyzes the outcomes of an exploratory review of the current research on sustainable industrial big data, automated production processes, and smart networked factories in cyber-physical system-based manufacturing. The data used for this study was obtained and replicated from previous research conducted by Deloitte, Eclipse Foundation, McKinsey, LNS Research, MHI, Plex Systems, and PwC. We performed analyses and made estimates regarding the robust designs and performance of wireless sensor network-based smart systems. Data collected from 6,500 respondents are tested against the research model. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

Impirical evidence on smart biomedical sensors, big healthcare data analytics, and virtual care technologies in monitoring, detection, and prevention of COVID-19 has been scarcely documented in the literature.Using and replicating data from Amwell, Ericsson Consumer Lab, Deloitte, DHS, Kyruus, McKinsey, and PwC, I performed analyses and made estimates regarding how telehealth can facilitate real-time interaction between quarantined patients with COVID-19 and medical providers. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

Empirical evidence on sensor-based big data applications and environmentally sustainable urban development in Internet of Things-enabled smart cities has been scarcely documented in the literature. Using and replicating data from Capgemini, CBRE Research, Deloitte, EIU, ESI Thought Lab, and RICS, we performed analyses and made estimates regarding Internet of Things connected sensors in smart city governance. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. [ABSTRACT FROM AUTHOR]

The purpose of this study was to empirically examine cyber-physical system-based manufacturing. Building our argument by drawing on data collected from Capgemini, Catapult, CGI, Deloitte, Eclipse Foundation, PAC, PwC, SME, Software AG, and ZDNet, we performed analyses and made estimates regarding the link between smart connected sensors, industrial big data, and real-time process monitoring. The structural equation modeling technique was used to test the research model. [ABSTRACT FROM AUTHOR]

Employing recent research results covering Internet of Things-based real-time production logistics, and building our argument by drawing on data collected from Catapult, Deloitte, MHI, PwC, SME, Software AG, and ZDNet, we performed analyses and made estimates regarding the relationship between industrial artificial intelligence, smart connected sensors, and big data-driven decision-making processes. Structural equation modeling was used to analyze the collected data. [ABSTRACT FROM AUTHOR]

I develop a conceptual framework based on a systematic and comprehensive literature review on Internet of Things-based real-time production logistics. Building my argument by drawing on data collected from Capgemini, CGI, CompTIA, and PwC, I performed analyses and made estimates regarding how organizations can accelerate and secure their smart factory transformation. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 4,600 respondents. [ABSTRACT FROM AUTHOR]

Based on an in-depth survey of the literature, the purpose of the paper is to explore cloud-based cyber-physical manufacturing systems. Using and replicating data from CompTIA, Deloitte, and McAfee, I performed analyses and made estimates regarding cloud-based applications in use (%), new/improved skills needed for cloud solutions (%), sources for procurement/management of cloud-based applications (%), and changes to IT function during cloud adoption (%). Data were analyzed using structural equation modeling. [ABSTRACT FROM AUTHOR]

Despite the relevance of the relationship between healthcare body sensor networks and smart wearable devices in Internet of Medical Things, only limited research has been conducted on this topic. Using and replicating data from McKinsey & Co., Philips, PwC, and Syneos Health, I performed analyses and made estimates regarding most significant barriers healthcare organizations face in adopting artificial intelligence. The results of a study based on data collected from 4,200 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire. [ABSTRACT FROM AUTHOR]

I develop a conceptual framework based on a systematic and comprehensive literature review on wearable Internet of Things healthcare systems. Building my argument by drawing on data collected from Accenture, Ericsson ConsumerLab, GE Healthcare, McKinsey & Co., MIT Technology Review Insights, PwC, and Statista, I performed analyses and made estimates regarding patients willing to use digital devices at home and visit with doctors virtually and to have surgery performed by a robot instead of a doctor. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 4,600 respondents. [ABSTRACT FROM AUTHOR]

Despite the relevance of big data-driven smart manufacturing, only limited research has been conducted on this topic. Using and replicating data from Accenture, Deloitte, Management Events, and PwC, I performed analyses and made estimates regarding current state of customer experience management (%), Industry 4.0 investments broken down by steps of the value chain (%, priorities), degree of digitization of the value chain by industry sector (%), top challenges in changing strategy and in preparing the workforce for Industry 4.0 (%), and worker time potentially augmented/automated by intelligent technologies (%). The results of a study based on data collected from 4,400 respondents provide support for my research model. Using the structural equation modeling and employing the probability sampling technique, I gathered and analyzed data through a self-administrated questionnaire. [ABSTRACT FROM AUTHOR]