Printer Friendly

Semantically Enriched Internet of Things Sensor Data in Smart Networked Environments.

1. Introduction

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.

Author Contributions

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.


Balica, R. (2018). "Big Data Learning Analytics and Algorithmic Decision-Making in Digital Education Governance," Analysis and Metaphysics 17: 128-133.

Bellavista, P., J. Berrocal, A. Corradi, S. K. Das, L. Foschini, and A. Zanni (2019). "A Survey on Fog Computing for the Internet of Things," Pervasive and Mobile Computing 52: 71-99.

Grossman, T. (2018). "The Rise of an Automated Jobless Society: Do Cutting-Edge Technologies Expel Workers Swifter than the Economy Can Identify New Jobs for Them?," Psychosociological Issues in Human Resource Management 6(2): 62-67.

Hoffman, S. F., and H. H. Friedman (2018). "Machine Learning and Meaningful Careers: Increasing the Number of Women in STEM," Journal of Research in Gender Studies 8(2): 11-27.

Khattak, H. A., M. A. Shah, S. Khan, I. Ali, and M. Imran (2019). "Perception Layer Security in Internet of Things," Future Generation Computer Systems 100: 144-164.

Klierova, M., and J. Kutik (2017). "One Stop Government--Strategy of Public Services for Citizens and Businesses in Slovakia," Administratie si Management Public 28: 66-80.

Kmecova, I. (2018). "The Processes of Managing Human Resources and Using Management Methods and Techniques in Management Practice," Ekonomicko-manazerske spektrum 12(1): 44-54.

Le, D. N., L. L. Tuan, and M. N. D. Tuan (2019). "Smart-Building Management System: An Internet-of-Things (IoT) Application Business Model in Vietnam," Technological Forecasting and Social Change 141: 22-35.

Linger, R. C., and A. R. Hevner (2018). "Flow Semantics for Intellectual Control in IoT Systems," Journal of Decision Systems 27(2): 63-77.

Liu, Y., F. Du, J. Sun, Y. Jiang, J. He, and T. Zhu (2018). "A Crowdsourcing-based Topic Model for Service Matchmaking in Internet of Things," Future Generation Computer Systems 87: 186-197.

Liu, Y., T. Zhu, Y. Jiang, and X. Liu (2019). "Service Matchmaking for Internet of Things based on Probabilistic Topic Model," Future Generation Computer Systems 94: 272-281.

Mahieu, C., F. Ongenae, F. De Backere, P. Bonte, F. De Turck, and P. Simoens (2019). "Semantics-based Platform for Context-Aware and Personalized Robot Interaction in the Internet of Robotic Things," Journal of Systems and Software 149: 138-157.

Manogaran, G., N. Chilamkurti, and C.-H. Hsu (2018). "Machine Learning Algorithms for Internet of Things, Fog Computing and Cloud Computing," Computing 100: 757-758.

Mirica (Dumitrescu), C.-O. (2018). "Online Contingent Labor in a Precarious Capitalism: Platform Business Models, Nonstandard Forms of Employment, and Digitally Driven Sharing-Economy Services," Journal of Self-Governance and Management Economics 6(3): 129-135.

Moghtader, B. (2018). "Pastorate Power, Market Liberalism and a Knowing without Knowing," Knowledge Cultures 6(1): 18-35.

Nica, E. (2017). "Political Mendacity and Social Trust," Educational Philosophy and Theory 49(6): 571-572.

Nica, E. (2018). "Gig-based Working Arrangements: Business Patterns, Labor-Management Practices, and Regulations," Economics, Management, and Financial Markets 13(1): 100-105.

Pico-Valencia, P., and J. A. Holgado-Terriza (2018). "Agentification of the Internet of Things: A Systematic Literature Review," International Journal of Distributed Sensor Networks. doi: 10.1177/1550147718805945

Popescu Ljungholm, D. (2018). "Sharing Economy, Regulatory Arbitrage, and Urban Governance: How City Space Shapes Economic Growth and Innovation" Geopolitics, History, and International Relations 10(1): 174-180.

Popescu, G. H., E. Nica, F. C. Ciurlau, M. Comanescu, and T. Bitoiu (2017). "Stabilizing Valences of an Optimum Monetary Zone in a Resilient Economy--Approaches and Limitations," Sustainability 9(6): 1051.

Popescu, G. H., M. Mieila, E. Nica, and J.-V. Andrei (2018). "The Emergence of the Effects and Determinants of the Energy Paradigm Changes on European Union Economy," Renewable and Sustainable Energy Reviews 81(1): 768-774.

Radulescu, A. (2018). "Users' Social Trust of Sharing Data with Companies: Online Privacy Protection Behavior, Customer Perceived Value, and Continuous Usage Intention," Contemporary Readings in Law and Social Justice 10(1): 137-143.

Shadroo, S., and A. M. Rahmani (2018). "Systematic Survey of Big Data and Data Mining in Internet of Things," Computer Networks 139: 19-47.

Teixeira, F. A., F. M. Q. Pereira, H.-C. Wong, J. M. S. Nogueira, and L. B. Oliveira (2019). "SIoT: Securing Internet of Things through Distributed Systems Analysis," Future Generation Computer Systems 92: 1172-1186.

Umer, T., M. H. Rehmani, A. E. Kamal, and L. Mihaylova (2019). "Information and Resource Management Systems for Internet of Things: Energy Management, Communication Protocols and Future Applications," Future Generation Computer Systems 92: 1021-1027.

Zhou, B., Q. Zhang, Q. Shi, Q. Yang, P. Yang, and Y. Yu (2018). "Measuring Web Service Security in the Era of Internet of Things," Computers & Electrical Engineering 66: 305-315.

Zhuge, H., and X. Sun (2019). "Semantics, Knowledge, and Grids at the Age of Big Data and AI," Concurrency and Computation: Practice and Experience 31(3): e5066.

Daniela Ionescu

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
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
Analysis of connected    38         34         13
stationary equipment/
Decision-support         26         38         18
Analytics that support   29         30         16
process automation
Cybersecurity            28         30         17
Smart grid               34         17         19
Analysis of connected    36         14         21
moving equipment/

                         Slightly   Not at all
                         important  important

Predictive/Prescriptive   6          3
maintenance of
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
Analysis of connected     6          9
stationary equipment/
Decision-support         16          2
Analytics that support   15         10
process automation
Cybersecurity            22          3
Smart grid               22          8
Analysis of connected    18         11
moving equipment/

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


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

Generate        88

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.
COPYRIGHT 2019 Addleton Academic Publishers
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2019 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Ionescu, Daniela
Publication:Analysis and Metaphysics
Date:Jan 1, 2019
Previous Article:Industrial Big Data Analytics for Cognitive Internet of Things: Wireless Sensor Networks, Smart Computing Algorithms, and Machine Learning Techniques.
Next Article:Cyber-Physical Systems for Cognitive Industrial Internet of Things: Sensory Big Data, Smart Mobile Devices, and Automated Manufacturing Processes.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters