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The Embedding of Smart Digital Technologies within Urban Infrastructures: Governance Networks, Real-Time Data Sustainability, and the Cognitive Internet of Things.

1. Introduction

Climate risk and restructurings in practice indicate a distancing from greening endeavors in preparation for sustainable advancement. (Gazzola et al., 2019) The notion of smart cities has matured significantly with the expansion and buildout of the Internet of Things as groundbreaking kind of sustainable advancement. (Sharma and Park, 2018) Smart cities attempt to employ technology and data to enhance the coherence of city services, to tackle societal issues, and to optimize partnership between citizens and government. Handling such social transformation necessitates a massive socio-ecological cutover with both organizations and citizens' ways of life expecting to change. (Hudson et al., 2019)

2. Conceptual Framework and Literature Review

The mingling of greening and smart notions for sustainable advancement may be more thoroughly attained if smart-centric strategies to policy and administration are comprised in the all-encompassing conceptualization of green quality and flexibility, with environmental directions to urban advancement, establishing the route and activating decisions in the direction of a sustainable future. (Gazzola et al., 2019) The Internet of Things represents an essential element of the information and communication technology infrastructure of smart sustainable cities, being a developing urban advancement line of action because of its significant capacity to further environmental sustainability. (Bibri, 2018) The rise of smart cities focuses on moderating the difficult tasks increased as a result of the incessant urbanization expansion (Balica, 2017; Hardingham et al., 2018; Hoffman and Friedman, 2018; Meila, 2018; Popescu et al., 2018; Popescu Ljungholm, 2018a, b; Radulescu, 2018) and growing population density in urban areas. Thus, governments and decision makers should become involved in smart city projects pursuing sustainable economic growth and superior standard of living for citizens. (Osman, 2019)

3. Methodology and Empirical Analysis

Using and replicating data from BI Intelligence, Black & Veatch, Bloomberg Intelligence, ESI ThoughtLab, Frost & Sullivan, and UBS, we performed analyses and made estimates regarding smart city addressable market by segment in 2025 (%), the primary driver for smart city projects in the U.S. (%), the top three most important systems a smart city program should invest in first (%), and digital technologies cities currently actively use to support operations (%). Structural equation modeling was used to analyze the collected data.

4. Results and Discussion

As an established information and communication technology innovation or computing pattern, the Internet of Things is related to big data analytics, which is incontestably on a pervasive route throughout numerous urban spheres for shaping up energy efficiency and moderating environmental consequences, therefore concerning chiefly the adequate use of natural resources, the judicious administration of infrastructures and facilities, and the improved provision of services in the interest of the environment. (Bibri, 2018) The smart city supplies higher quality ways out for urban zones which are booming at an amazing tempo. (Li et al., 2019) (Tables 1-6)

5. Conclusions and Implications

The Internet of Things and associated big data applications are instrumental in bringing on and enhancing the operation of environmentally sustainable advancement. (Bibri, 2018) Smart cities are a fashionable notion as they may catalyze a sustainable and harmonious urban future. (Yigitcanlar and Kamruzzaman, 2019) Sensor-based devices are put into service to an increasing extent for diverse applications to gather a massive quantity of data for grasping the world, thus integrating wide-ranging sensing Internet of Things into smart cities. (Teng et al., 2019) Noticeably escalated societal requirements on the urban services that call in question environmental protection and data processing capability concerned with resource use efficiency are disadvantaged by incompatible simultaneous demands. (Li et al., 2019)

Funding

This paper was supported by Grant GE-1785651 from the San Francisco Center for IoT Economy, CA.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

REFERENCES

Balica, R. (2017). "The Alienated Language of the Affective Commodity in Houellebecq's Novels," Review of Contemporary Philosophy 16: 143-149.

Bibri, S. E. (2018). "The IoT for Smart Sustainable Cities of the Future: An Analytical Framework for Sensor-based Big Data Applications for Environmental Sustainability," Sustainable Cities and Society 38: 230-253.

Gazzola, P., A. Gonzalez Del Campo, and V. Onyango (2019). "Going Green vs Going mart for Sustainable Development: Quo Vadis?," Journal of Cleaner Production 214: 881-892.

Hardingham, E., J. Vrbka, T. Kliestik, and J. Kliestikova (2018). "Will Cognitive Technology-Driven Automation Lead to Economic Growth?," Journal of Self-Governance and Management Economics 6(4): 13-18.

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.

Hudson, L., A. Wolff, D. Gooch, J. van der Linden, G. Kortuem, M. Petre, et al. (2019). "Supporting Urban Change: Using a MOOC to Facilitate Attitudinal Learning and Participation in Smart Cities," Computers & Education 129: 37-47.

Li, X., P. S. W. Fong, S. Dai, and Y. Li (2019). "Towards Sustainable Smart Cities: An Empirical Comparative Assessment and Development Pattern Optimization in China," Journal of Cleaner Production 215: 730-743.

Meila, A. D. (2018). "Regulating the Sharing Economy at the Local Level: How the Technology of Online Labor Platforms Can Shape the Dynamics of Urban Environments," Geopolitics, History, and International Relations 10(1): 181-187.

Osman, A. M. S. (2019). "A Novel Big Data Analytics Framework for Smart Cities," Future Generation Computer Systems 91: 620-633.

Popescu, G. H., I. E. Petrescu, and O. M. Sabie (2018). "Algorithmic Labor in the Platform Economy: Digital Infrastructures, Job Quality, and Workplace Surveillance," Economics, Management, and Financial Markets 13(3): 74-79.

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Sharma, P. K., and J. H. Park (2018). "Blockchain Based Hybrid Network Architecture for the Smart City," Future Generation Computer Systems 86: 650-655.

Teng, H., Y. Liu, A. Liu, N. N. Xiong, Z. Cai, T. Wang, et al. (2019). "A Novel Code Data Dissemination Scheme for Internet of Things through Mobile Vehicle of Smart Cities," Future Generation Computer Systems 94: 351-367.

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Harvey Kearney

h.kearney@aa-er.org

The Digital Dynamics Laboratory

at CSA, Adelaide, Australia

Tomas Kliestik

tomas.kliestik@fpedas.uniza. sk

Faculty of Operation and Economics

of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Maria Kovacova

maria.kovacova@fpedas.uniza.sk

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Marek Vochozka

vochozka@mail.vstecb.cz

The Institute of Technology and Business in Ceske Budejovice, The School of Expertness and Valuation, Czech Republic

How to cite: Kearney, Harvey, Tomas Kliestik, Maria Kovacova, and Marek Vochozka (2019). "The Embedding of Smart Digital Technologies within Urban Infrastructures: Governance Networks, Real-Time Data Sustainability, and the Cognitive Internet of Things," Geopolitics, History, and International Relations 11(1): 98-103. doi:10.22381/GHIR11120195

Received 14 December 2018 * Received in revised form 22 May 2019

Accepted 24 May 2019 * Available online 1 June 2019

doi:10.22381/GHIR11120195
Table 1 The top three most important systems a smart city program
should invest in first (%, Select three choices.)

                                Government/    Smart Services
                                Municipality   Provider

High-speed data network         34.7           26.4
Smart transportation            26.4           38.2
Smart water systems             33.8           26.7
Smart electric grid, including  36.2           40.1
smart metering
Renewable/Distributed           14.1           32.1
generation/Microgrids
Smart street lighting           19.4           17.4
Smart buildings                 34.7           53.2
Smart sensors                   17.2            8.1

Sources: Black & Veatch; our survey among 4,700 individuals conducted
November 2018.

Table 2 What are the top three hurdles that your city/utility has had
to address to enable utility, city/community or campus systems to be
managed in a smarter, more integrated way? (Select three choices.)

                             Government/        Smart Services
                             Municipality, %    Provider, %

Policy hurdles                    29.4           30.4
Lack of resources                 36.3           54.8
or expertise
Technology availability           21.1           42.1
Budget constraints                64.5           56.7
Gaining stakeholder support       24.2           32.8
Ownership across                  24.3           25.7
departments
Time constraints/                 30.7           32.6
Other priorities
Short-term mindset                28.6           28.1
Other                              2.7            2.4
Don't know                        12.1            0.2

Sources: Black & Veatch; our survey among 4,700 individuals conducted
November 2018.

Table 3 What level of attention should your city put on the following
areas of city governance? (%)

The areas of governance                     Beginner  Transitioning

requiring attention
Involving local business executives         54        48
in planning decisions
Minimizing bribery and corruption           49        41
Reducing the burden of complying with       50        42
local government regulations
Offering open data platforms across         44        41
government, business, and citizens
Updating regulations in response to new,    44        42
innovative business models
Providing channels for real time            46        41
information and resource sharing
Making it easy to find, access, and         47        43
bid on procurement opportunities
Quickly resolving city issues               42        43
that affect my business
Ensuring a stable fiscal environment        46        42
Using advanced data and technologies to     41        37
improve urban decision making
Making it easy to access information and    50        36
data to manage our business
Developing a long-term vision for the city  48        32
Ensuring a stable policy environment        40        29
for doing business
Decreasing city inefficiencies              25        20

The areas of governance                     Leader

requiring attention
Involving local business executives         47
in planning decisions
Minimizing bribery and corruption           42
Reducing the burden of complying with       45
local government regulations
Offering open data platforms across         50
government, business, and citizens
Updating regulations in response to new,    45
innovative business models
Providing channels for real time            47
information and resource sharing
Making it easy to find, access, and         43
bid on procurement opportunities
Quickly resolving city issues               44
that affect my business
Ensuring a stable fiscal environment        43
Using advanced data and technologies to     48
improve urban decision making
Making it easy to access information and    41
data to manage our business
Developing a long-term vision for the city  35
Ensuring a stable policy environment        36
for doing business
Decreasing city inefficiencies              28

Sources: Frost & Sullivan; Bloomberg Intelligence; UBS; our estimates.

Table 5 The primary driver for smart city projects in the U.S. (%)

Operational efficiency/Cost reduction  28
Environmental sustainability           26
Improved city management               22
Infrastructure resilience              13
Attract businesses                     11

Sources: BI Intelligence; Black & Veatch; our survey among 4,700
individuals conducted November 2018.

Table 6 Digital technologies cities currently actively use to support
operations (%)

                         Beginner  Transitioning  Leader

IoT/Sensors/Wearables    18        77             97
Biometrics/facial        52        57             87
recognition
Geospatial technology    36        59             90
Chatbots/Natural          4        27             56
language processing
Smart beacons/Near-       3        19             38
field communication
Artificial intelligence   5        13             30
and virtual reality
Augmented and             2        11             22
virtual reality
Drones and robots         1         9             16
Blockchain                2         6             13

Sources: ESI ThoughtLab; our survey among 4,700 individuals conducted
November 2018.
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Author:Kearney, Harvey; Kliestik, Tomas; Kovacova, Maria; Vochozka, Marek
Publication:Geopolitics, History, and International Relations
Date:Jun 1, 2019
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