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The Social Sustainability of Smart Cities: Urban Technological Innovation, Big Data Management, and the Cognitive Internet of Things.

1. Introduction

The key feature of current and envisioned data-based smart city advancements is not information and communications technology, statistics, or intelligent infrastructure, but the cutting-edge applications for value production for stakeholders. (Lim et al., 2018) An urban area can be transformed into a smart city by enhancing citizens' standard of living, sustainability, and work productivity through fashionable information and communication technology and Internet of Things. (Malik et al., 2018) Smart cities expand far and wide and materialize with comparable characteristics and interdependencies internationally. Smart cities represent a local experience, as each urban area is beyond compare, has distinct issues, and should tackle them with distinctive ways out. (Dameri et al., 2019)

2. Conceptual Framework and Literature Review

Big data analytics are decisive in carrying out the essential green characteristics of smart sustainable cities, i.e. the coherence of processes and services, the improvement of natural resources, and the judicious administration of infrastructures and facilities. (Bibri, 2018) Automated system supervising undertakings for a smart city need to be flexible to real-time information processing issues to carry out data analytics swiftly and precisely. (Malik et al., 2018) Fast expansion places large-scale requirements on city services and road and rail networks that require groundbreaking and sustainable ways out which gradually entail smooth monitoring, gathering, storage and interpretation of vast, disparate data. (Alabdulatif et al., 2019) Smart cities are established in standards of democratic representation, although the connection between political entities and citizens is not completely advanced because of the absence of mechanisms genuinely furthering the involvement and voicing of the general public. (Nesti and Graziano, 2019)

3. Methodology and Empirical Analysis

Building my argument by drawing on data collected from Black & Veatch, ESI ThoughtLab, Grand View Research, McKinsey, and Statista, I performed analyses and made estimates regarding the top three major challenges teams are facing with the current distribution system automation and communication capabilities (%), Internet of Things units installed base within smart cities in 2018 (by subgroup), EU smart governance market share (by sub-application, %), and potential improvement through current generation of smart city applications, from time of implementation (%). Multivariate statistics techniques have been applied for data analyses (e.g. structural equation modeling).

4. Results and Discussion

The big data applications facilitated by the Internet of Things may be adequate to an array of the spheres of smart sustainable cities in connection with their operational functioning, administration, and design in the framework of environmental sustainability. (Bibri, 2018) Significant frequency and amount of big data entailed in the smart city necessitate sustainable statistical projection while preserving its representation for generating real-time inferencing and analytical outcomes. (Malik et al., 2018) City processes can be enhanced by putting into effect ways out through adopting such applications, but building up data accessibility in association with robust analytic tools intensifies the risk of privacy breaches. (Curzon et al., 2019) Social and organizational features of smart cities security develop out of opposing concerns of distinct participants, high degrees of connection, and social and political intricacy. (Vitunskaite et al., 2019) (Tables 1-8)

5. Conclusions and Implications

The adoption of urban big data is instrumental in the production of statistics for stakeholders to carry out their processes more thoroughly and generate value. (Lim et al., 2018) The function of big data analytics is noticeable not only as regards bringing on and expanding the green development operations of smart sustainable cities, but also with reference to grasping, examining, evaluating, and planning such metropolitan areas in manners that judiciously enhance their input to the objectives of ecologically sustainable society. (Bibri, 2018) Information modeling transformed into remarkably appropriate patterns for inferencing and analytics represents a demanding and expensive undertaking (Georgiou and Rocco, 2017; Grossman, 2018; Havu, 2017; Michailidou, 2017; Nica et al., 2017; Ohanyan and Androniceanu, 2017; Popescu, 2018; Sion, 2018a, b) taking into account time limitations. Their natural grids are satisfactorily harmonized for real-time information analytics and inferencing in Internet of Things-based data online. (Malik et al., 2018)


This paper was supported by Grant GE-1795342 from the Social Analytics Laboratory, Los Angeles, CA.

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.


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Armenia Androniceanu

University of Social Sciences, Lodz, Poland; The Bucharest University of Economic Studies, Romania

How to cite: Androniceanu, Armenia (2019). "The Social Sustainability of Smart Cities: Urban Technological Innovation, Big Data Management, and the Cognitive Internet of Things," Geopolitics, History, and International Relations 11(1): 110-115. doi:10.22381/GHIR11120197

Received 1 December 2018 * Received in revised form 13 May 2019

Accepted 19 May 2019 * Available online 1 June 2019

Table 1 What role is your utility playing in your municipality's smart
city initiatives? (%, Select one choice.)

Support role     31.2
Leadership role  20.4
Not involved     19.8
Don't know       28.6

Sources: Black & Veatch; my survey among 5,200 individuals conducted
November 2018.

Table 2 The top three major challenges teams are facing with the
current distribution system automation and communication capabilities
(%, Select top three choices.)

Cybersecurity                                           47.1
Old and obsolete equipment                              35.7
Lack of staff to support future needs and requirements  35.4

Sources: Black & Veatch; my survey among 5,200 individuals conducted
November 2018.

Table 3 Internet of Things units installed base within smart cities in
2018 (by subgroup)

Smart homes                 1,074.2
Smart commercial buildings  1,066.1
Transport                     518.6
Utilities                     464.8
Public services               168.1
Others                         33.3
Healthcare                     13.9

Sources: Statista; my 2018 data.

Table 4 Please tell us your city's stage of development in the use of
data and data analytics in the following areas (%):

                   Beginner  Transitioning  Leader

Collecting data     4        58             97
Extracting data     4        49             93
Integrating data    1        41             99
Analyzing data      2        47             98
Providing a mix    16        53             96
of data
Making data         2        50             84
accessible and
Monetizing data     2        33             70

Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted
November 2018.

Table 5 When adopting new technologies, what approach is your city most
likely to take? (%)

                                Beginner  Transitioning  Leader

Outsource implementation         8.4        22.4         83.6
to consultants
Partner with technology         30.1        51.8         77.1
Buy the technology               8.7        42.8         60.2
License the technology          28.2        41.9         59.4
Partner with academic           11.2        34.1         30.6
Partner with service providers  36.2        32.2         24.9
Public-private partnerships     17.4        16.4         12.6
Develop/Operate systems         14.3        23.8         11.7
Outsource development           14.1        27.2          6.2

Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted
November 2018.

Table 6 EU smart governance market share (by sub-application, %)

City surveillance             27
Command and control solution   5
E-governance                  24
Smart lighting                25
Smart infrastructure market   19

Sources: Grand View Research; my 2018 data.

Table 7 Potential improvement through current generation of smart city
applications, from time of implementation (%)

Health                    Disease burden                            13

Time and convenience      Commute time                              17
                          Time spent interacting with               60
                          healthcare and government
Safety                    Fatalities                                10
                          Crime incidents                           35
                          Emergency response time                   30
Cost of living            Citizen expenditures                       3
Jobs                      Formal employment                          2
Social connectedness and  Citizens feel connected to their local    16
civic participation       community
                          Citizens feel connected to their          20
                          local government
Environmental quality     GHG emissions                             14
                          Water consumption                         36
                          Unrecycled waste                          15

Table 8 Which of the following types of data is your city currently
using to drive smart city initiatives, and which do you plan to use
over the next three years? (%)

Technology               Now  3 years

Internet of Things       69   94
Real-time                62   91
Administrative           59   74
Local business           53   62
Social media             51   63
Geospatial               49   73
Behavioral               44   70
Channel use              43   57
Predictive data          36   64
Crowd-sourced            30   52
Psychographic            27   47
Artificial intelligence  12   66

Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted
November 2018.
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Author:Androniceanu, Armenia
Publication:Geopolitics, History, and International Relations
Article Type:Report
Date:Jun 1, 2019
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