Semantically Enriched Internet of Things Sensor Data in Smart Networked Environments.
Big data and machine learning offer an empirical pattern for establishing the sustainable advancement of a smart interconnection setting that is dissimilar from the approach constructed on semantics and knowledge. (Zhuge and Sun, 2019) Scalable machine learning algorithms have been improved to process the huge amount of Internet of Things data. (Manogaran et al., 2018) The Internet of Things requires both technically and logically accurate ways out to provide the indispensable security and privacy. (Khattak et al., 2019) Robots are performing from supervised lab environments to the concrete realm, where a growing volume of settings has emerged as smart sensorized Internet of Things areas. (Mahieu et al., 2019)
2. Conceptual Framework and Literature Review
A significant amount of incongruous network components interact and are supervised by Internet of Things-based data systems. (Umer et al., 2019) The materialization of omnipresent sensors, smart devices and broad-band Internet capacity has facilitated the assimilation of networks for correlated information gathering and processing (Balica, 2018; Hoffman and Friedman, 2018; Kmecova, 2018; Moghtader, 2018; Nica, 2018; Popescu et al., 2017; Radulescu, 2018), which inherently enables instant decision-making and physical operations (Grossman, 2018; Klierova and Kutik, 2017; Mirica (Dumitrescu), 2018; Nica, 2017; Popescu Ljungholm, 2018; Popescu et al., 2018) to alterations immediately. (Le et al., 2019) Big data and information mining can be employed to enhance the coherence of Internet of Things and storage challenges of a massive data quantity and the transfer, analysis, and processing of such input on the Internet of Things. (Shadroo and Rahmani, 2018) Semantic modeling approaches in Internet of Things that collect meta-data from text harnessing rule-based or machine learning processes frequently are disadvantaged by scalability and inconsistency as text supplied by sensors is compressed and disorganized. (Liu et al., 2018)
3. Methodology and Empirical Analysis
The data used for this study was obtained and replicated from previous research conducted by Business Insider, DAA, Deloitte, IoT Analytics GmbH, OSF, and PwC. I performed analyses and made estimates regarding Industry 4.0 framework and contributing digital technologies (%) and importance of big data and public sector open data for enterprises' business activity (%). Data collected from 4,400 respondents are tested against the research model by using structural equation modeling.
4. Results and Discussion
The Internet of Things is designed for controlling the concrete realm employing a worldwide network of dissimilar smart objects that are interconnected online. (Pico-Valencia and Holgado-Terriza, 2018) Cloud computing has facilitated the advancement of the Internet of Things pattern and the organization of multifarious Internet-connected devices having adequate capabilities to be perpetually sensing or acting in conformity with the users' requirements. (Bellavista et al., 2019) Semantic modeling approaches are chiefly adopted to straighten out service matchmaking issues, but because the text for service description is in many instances compressed, disorganized and high-dimensional characteristics may occur. (Liu et al., 2019) (Tables 1-6)
5. Conclusions and Implications
The Internet of Things enables smart devices to provide web-based services in unrestricted and operational networking settings on a large scale. (Zhou et al., 2018) Flow semantics is a robust engineering mainstay for analysis, advancement, expansion, operation, and regulation of Internet of Things systems in volatile environments. (Linger and Hevner, 2018) The increasing relevance of the Internet of Things necessitates devices able to supply users with accurate, well-built, and secure systems. (Teixeira et al., 2019)
The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. The precision of the online polls was measured using a Bayesian credibility interval.
This paper was supported by Grant GE-1490862 from the Center for Big Data-driven Algorithmic Decision-Making, Portland, OR.
The author confirms being the sole contributor of this work and approved it for publication.
Conflict of Interest Statement
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The University of Bucharest, Romania
Received 2 April 2019 * Received in revised form 22 August 2019
Accepted 23 August 2019 * Available online 8 December 2019
Table 1 Industry 4.0 framework and contributing digital technologies (%) Mobile devices 12 IoT platforms 12 Location detection technologies 4 Advanced human-machine interfaces 7 Authentication and fraud detection 4 3D printing 10 Smart sensors 14 Big data analytics and advanced algorithms 16 Multilevel customer interaction and customer profiling 10 Augmented reality/Wearables 5 Cloud computing 6 Sources: PwC; my survey among 4,400 individuals conducted March 2019. Table 2 How important are the following industrial data analytics applications for your company in the next 1-3 years? (%) Extremely Very Moderately important important important Predictive/Prescriptive 49 37 5 maintenance of machines Customer-/Marketing- 50 30 7 related analytics Analysis of product 39 42 12 usage in the field Visual analytics 32 48 7 Analytics supporting 28 47 8 remote service/ product updates R&D-related analytics 27 44 7 Data-driven quality 41 29 7 control of manufactured products Analysis of connected 38 34 13 stationary equipment/ assets Decision-support 26 38 18 systems Analytics that support 29 30 16 process automation Cybersecurity 28 30 17 analytics Smart grid 34 17 19 Analysis of connected 36 14 21 moving equipment/ assets Slightly Not at all important important Predictive/Prescriptive 6 3 maintenance of machines Customer-/Marketing- 11 2 related analytics Analysis of product 5 2 usage in the field Visual analytics 10 3 Analytics supporting 13 4 remote service/ product updates R&D-related analytics 10 2 Data-driven quality 13 10 control of manufactured products Analysis of connected 6 9 stationary equipment/ assets Decision-support 16 2 systems Analytics that support 15 10 process automation Cybersecurity 22 3 analytics Smart grid 22 8 Analysis of connected 18 11 moving equipment/ assets Sources: DAA; IoT Analytics GmbH; my survey among 4,400 individuals conducted March 2019. Table 3 In which areas are you using big data analytics today? In which additional areas will your company use data analytics in five years? (%) Status Growth potential quo in 5 years Optimization of overall business 61 22 planning and controlling Better manufacturing/operations planning 62 19 Improvement of customer relationship 59 17 and customer intelligence along the product life cycle More efficient asset utilization 56 23 of operational efficiency Development of new or optimization 52 21 of existing products/services Increase of sales revenue 50 17 Optimization of transport and 53 17 logistics costs/efficiency Improved product or process quality 52 23 Efficient maintenance/service of 44 23 own assets or customer products Better cooperation and decision-making 39 21 with partner companies Sources: PwC; my survey among 4,400 individuals conducted March 2019. Table 4 Importance of big data and public sector open data for enterprises' business activity (%) High Medium importance importance Use of data in marketing 29 42 Use of data in research and development 31 40 Use of data in developing 28 39 marketing innovations Use of data in managing 29 38 production process Use of data in developing 27 35 process innovations Use of data in developing 28 35 organizational innovations Use of open data in 27 33 developing new products Use of big data in improving products 28 34 Use of big data in developing new products 29 33 Buying big data from other enterprises 26 24 Selling big data to other enterprises 27 21 Low importance Use of data in marketing 29 Use of data in research and development 29 Use of data in developing 33 marketing innovations Use of data in managing 33 production process Use of data in developing 38 process innovations Use of data in developing 37 organizational innovations Use of open data in 40 developing new products Use of big data in improving products 38 Use of big data in developing new products 38 Buying big data from other enterprises 50 Selling big data to other enterprises 52 Sources: OSF; my survey among 4,400 individuals conducted March 2019. Table 5 Physical-to-digital-to-physical loop and related technologies (%) Establish a Capture information from the physical world to create digital record a digital record of the physical operation and supply Analyze and network Machines talk to each other to share visualize information, allowing for advanced analytics and visualizations of real-time data from multiple sources Generate Apply algorithms and automation to translate decisions movement and actions from the digital world into movements in the physical world Establish a 94 digital record Analyze and 91 visualize Generate 88 movement Sources: Deloitte; my survey among 4,400 individuals conducted March 2019. Table 6 About how much does your company plan to invest in the next five years for Internet of Things solutions? (%) <$100,000 36 $100,000-$1 million 26 $1 million-$10 million 18 $10 million-$100 million 16 $100 million or more 4 Sources: Business Insider; my survey among 4,400 individuals conducted March 2019.