Content uploaded by Mauricio Adolfo Ramírez Moreno
Author content
All content in this area was uploaded by Mauricio Adolfo Ramírez Moreno on Sep 03, 2021
Content may be subject to copyright.
applied
sciences
Review
Sensors for Sustainable Smart Cities: A Review
Mauricio A. Ramírez-Moreno 1, Sajjad Keshtkar 2, Diego A. Padilla-Reyes 1, Edrick Ramos-López 1, Moisés
García-Martínez 3, Mónica C. Hernández-Luna 4, Antonio E. Mogro 1, Jurgen Mahlknecht 1,
José Ignacio Huertas 1, Rodrigo E. Peimbert-García 1,5,* , Ricardo A. Ramírez-Mendoza 1,
Agostino M. Mangini 6, Michele Roccotelli 6, Blas L. Pérez-Henríquez 7, Subhas C. Mukhopadhyay 5
and Jorge de Jesús Lozoya-Santos 1
Citation: Ramírez-Moreno, M.A.;
Keshtkar, S; Padilla-Reyes, D.A.;
Ramos-López, E.;
García-Martínez, M.;
Hernández-Luna, M.C.; Mogro, A.E.;
Mahlknecht, J.; Huertas, J.I;
Peimbert-García, R.E.; et al. Sensors
for Sustainable Smart Cities: A
Review. Appl. Sci. 2021,11, 8198.
https://doi.org/10.3390/
app11178198
Academic Editor: Juan-Carlos Cano
Received: 29 June 2021
Accepted: 31 August 2021
Published: 3 September 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1School of Engineering and Sciences, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur,
Monterrey 64849, Mexico; mauricio.ramirezm@tec.mx (M.A.R.-M.); diego.padilla@tec.mx (D.A.P.-R.);
edramos@tec.mx (E.R.-L.); a01366355@itesm.mx (A.E.M.); jurgen@tec.mx (J.M.); jhuertas@tec.mx (J.I.H.);
ricardo.ramirez@tec.mx (R.A.R.-M.); jorge.lozoya@tec.mx (J.d.J.L.-S.)
2
ESIME Unidad Ticoman, Instituto Politecnico Nacional, Mexico City 07340, Mexico; posgradoesimet@ipn.mx
3School of Engineering and Sciences, Tecnologico de Monterrey, San Luis Potosi 78211, Mexico;
moises.garcia@tec.mx
4Faculty of Economy, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza 66451, Mexico;
monica.hernandezlu@uanl.edu.mx
5School of Engineering, Macquarie University, Sydney, NSW 2109, Australia;
subhas.mukhopadhyay@mq.edu.au
6Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy;
agostinomarcello.mangini@poliba.it (A.M.M.); michele.roccotelli@poliba.it (M.R.)
7Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA; blph@stanford.edu
*Correspondence: rodrigo.peimbert@tec.mx
Abstract:
Experts confirm that 85% of the world’s population is expected to live in cities by 2050.
Therefore, cities should be prepared to satisfy the needs of their citizens and provide the best services.
The idea of a city of the future is commonly represented by the smart city, which is a more efficient
system that optimizes its resources and services, through the use of monitoring and communication
technology. Thus, one of the steps towards sustainability for cities around the world is to make a
transition into smart cities. Here, sensors play an important role in the system, as they gather relevant
information from the city, citizens, and the corresponding communication networks that transfer
the information in real-time. Although the use of these sensors is diverse, their application can be
categorized in six different groups: energy, health, mobility, security, water, and waste management.
Based on these groups, this review presents an analysis of different sensors that are typically used in
efforts toward creating smart cities. Insights about different applications and communication systems
are provided, as well as the main opportunities and challenges faced when making a transition to a
smart city. Ultimately, this process is not only about smart urban infrastructure, but more importantly
about how these new sensing capabilities and digitization developments improve quality of life.
Smarter communities are those that socialize, adapt, and invest through transparent and inclusive
community engagement in these technologies based on local and regional societal needs and values.
Cyber security disruptions and privacy remain chief vulnerabilities.
Keywords:
smart networks; digitization; smart transit; cyber security; smart city; smarter communi-
ties; smart sensors; smart meters; internet of things (IoT); facial recognition; cyber privacy
1. Introduction
“Smart Cities” or “intelligent cities” are the cities of the future that offer innovative
solutions to improve the quality of life of urban communities in a sustainable and equitable
manner. The idea of a “Smart City” represents more efficient cities that better manage
their resources, services and technologies and, above all, put them at service of the citizens.
People-centric planning will allow a better management and efficiency of the city through
Appl. Sci. 2021,11, 8198. https://doi.org/10.3390/app11178198 https://www.mdpi.com/journal/applsci
Appl. Sci. 2021,11, 8198 2 of 29
the deployment of smart infrastructure. By 2050, 85% of the world’s population is expected
to live in cities [
1
–
3
]. This means that in the following decades, urban centers will face a
growing number of problems such as: (1) energy supply, (2) CO
2
emissions, (3) mobility
systems planning, (4) raw materials and goods provision, and (5) the provision of health
and security services to all the residents in these rapidly growing population centers [4].
To better respond to the increasing volatility driven by climate change, pandemics like
COVID-19, and connected political and economic fluctuations, cities must be redesigned to
increase their adaptive capacity and resilience [
5
]. Schemes and models of more liveable
cities need to be created, where digital technologies are the key elements for the sustainable
development and organic growth of cities [
6
]. Vulnerabilities of the shared economy in
smart cities, such as transport services and their enabling technologies are being tested by
the global pandemic and cyber security risks. However, new apps and internet businesses
flourished, such as food delivery and online shopping [7–9].
Skepticism towards pervasive digitization, sensing, monitoring, and visualization
capacities deployment, and other smart communication technologies used by both the
private sector and government, arise, among other factors, from the concerns of citizens
regarding the processing of their data, and, therefore, their privacy [
10
]. The use of new
technologies such as artificial intelligence, where personal data play a critical role, are
facing increasing challenges [
11
]. Support for the establishment of codes of conduct are
being promoted by the industry itself, and use cases will be key so that its development
does not suffer [
1
]. In the years to come, problems such as intentional disinformation,
the evolution of so-called digital rights, or the consolidation of digital identity, must be
addressed. The solutions oriented to solve these problems require a long-term perspective,
which will steadily become more and more evident as Smart City implementations have
become a norm across the globe [12].
With the incoming necessity of communities to become smarter, many applications
have started to arise on different countries; and a literature revision that discusses such
applications will be useful as a reference for future smart city implementations. In this
review, the literature covering several applications of sensors for smart cities is summarized
and discussed, in order to fulfill three main objectives: (1) To provide a revision of the most
important Smart Cities implementations across the world; (2) To describe the main applica-
tions of sensors for smart cities across six main topics (health, security, mobility, water and
waste management, and energy efficiency); and (3) To identify common challenges and
opportunities of smart city deployments in the proposed six topics.
2. Literature Review
A systematic search of relevant scientific literature was conducted in this study
through databases such as Scopus, Google Scholar, and IEEE Xplore. The literature search
on sensors for smart cities was divided into six main sectors: health, security, water, waste,
energy, and mobility. The criteria for selecting and revising relevant papers from each
section, followed the PRISMA methodology [13], as well as these principles:
1.
Recent (2010–2020) literature was reviewed to ensure a revision of the current state of
the art of the technologies applied to smart cities environment; prioritizing papers
published during the 2015–2020 period. Figure 1a shows the distribution of the years
of publications of the revised papers for this review;
2.
Among the selected literature for each section, the most cited papers, including journal
articles and conference proceedings were revised extensively. Figure 1b presents the
distribution of the number of references with an increasing number of citations of the
revised literature. Papers from Q1 and Q2 journals were given priority over Q3 and
Q4 journals; as well as journals with impact factors higher than 1.0. Figure 1shows (c)
the quartiles, and (d) journal impact factor of the revised papers in this this review.
Additionally, Figure 2a shows the type of references (journal, conference proceedings,
books, webpages, and theses) selected and its percentage;
Appl. Sci. 2021,11, 8198 3 of 29
3.
In the six main sectors, preference was given to articles where the main topic was
the use of sensors exclusively for the subject evaluated. A wide range of studies
from the exploration of theoretical aspects up to practical applications were included.
Figure 2b
shows the percentage of revised papers under categories “Health”, “Secu-
rity”, “Mobility”, “Water”, “Waste”, “Energy”, and “Smart Cities”;
4.
For each of the six sectors, different keywords were used to find relevant literature
across each field. A list of keywords used for each section is presented as follows:
(a)
Health: Key terms were searched in the publication title, abstract, and key-
words, and include: “smart city”, “smart cities”, “sensors”, “wearable sensors”,
“body sensors”, “smart health”, “smart healthcare”, “healthcare sensors”,
“healthcare applications”, and “internet of things”;
(b)
Water: The selection of the articles for the survey was carried out using the
keywords “water” AND “sensors” AND “smart cities”;
(c)
Waste: The selection of the articles for the survey was carried out using the
keywords “waste” AND “sensors” AND “smart cities”;
(d)
Mobility: Among the main keywords and combination of keywords used
for this search were “mobility” AND “sensors” AND “smart cities”. Other
keywords such as “traffic”, “vehicle”, “pedestrian” AND “sensors” AND
“smart cities” were also included;
(e)
Energy: The following keywords were included in the search: “energy con-
sumption”, “thermal comfort”, “energy-consuming systems”, “greenhouse gas
emissions”, “HVAC system”, “lighting systems”, “buildings energy consump-
tion”, “urban space energy consumption”, ”Key Performance Indicators”,
“Light Power Density (LPD)”, “alternative energy source”, “smart buildings”,
“smart lighting”, “smart citizens”, “ecological buildings”, “virtual sensors”,
“BIM modeling”, “energy consumption sensors”;
(f)
Security: Keywords from this topic include: “Cybersecurity”, “Sustainable
Development”, “Environment Security”, “Society Security”, “Human Security”
AND “sensors” AND “smart cities”.
Following the aforementioned principles, a total of 193 references were reviewed in
detail; of which 129 come from journals, 46 from conference proceedings, 9 from books, 8
from web pages, and 1 from a Ph.D. thesis.
Appl. Sci. 2021,11, 8198 4 of 29
d)
100101
Impact Factor
0
5
10
15
20
No. of references
b)
101102103104
No. of Citations
0
5
10
15
20
No. of references
c)
1 2 3 4
Quartile
0
50
100
No. of references
a)
2000 2010 2020
Year
0
10
20
30
40
No. of references
Figure 1.
Histograms showing the distributions of different features of the papers selected for this
review: (a) Year, (b) Number of citations, (c) Journal Quartile and (d) Journal Impact Factor.
67%
24%
5%
4% Journal
Conference
Book
Webpage
Thesis
16%
13%
22%
8%
11%
11%
18%
Health
Security
Mobility
Water
Waste
Energy
Smart Cities
a) b)
Figure 2.
Pie charts representing the distribution of the (
a
) Type of reference and (
b
) Topic of the selected references for
this review.
3. Results
3.1. Smart City
The goals of smart cities initiatives are to develop economically, socially, and envi-
ronmentally sustainable cities [
5
,
6
,
12
]. Generally, the ideal model of a smart city is based
on the incorporation of the following subsystems and technologies: distributed energy
generation (micro-generation) [
14
], smart grids (interconnected and bidirectional smart
networks) [
15
], smart metering (intelligent measurement of energy consumption data) [
16
],
smart buildings (eco-efficient buildings with integrated energy production systems) [
17
],
smart sensors (intelligent sensors to collect data and keep the city connected) [
18
], eMobility
Appl. Sci. 2021,11, 8198 5 of 29
(implementation of electric vehicles) [
19
], information and communication technologies
(ICT) [20] and smart citizens (key piece of a smart city) [21].
Cities constitute a complex socio-technical system [
22
,
23
]. In order to design the
best solutions for cities, their inhabitants, social entities and governments need to be
considered [2,3].
More pleasant spaces and places are required to live, boost competitiveness and
productivity. To achieve this, the development of communication technology, such as 5G,
is imperative. There also exists a growing need for initiatives of Industry 4.0 to permeate
cities, since small or medium-sized enterprises (SMEs) represent the largest business fabric
in developing countries [
5
]. In this sense, it is necessary to increase operators’ access to
reliable 5G infrastructure, allowing optimal deployment and economic rationality of the
networks. In addition, governments are expected to provide access to resources and tools
that facilitate the deployment speed and offer the necessary infrastructure for the latest
generation networks [24].
3.2. Smart Cities in the World
In this section, some smart cities deployments across the world are presented. In
Europe: Tampere, Helsinki, Amsterdam, Vienna, Copenhagen, Stockholm, Milton Keynes,
London, Malaga, Barcelona, Santander, Paris, and Geneva [
4
,
25
–
27
]. In Asia: Singapore,
Hong Kong, Shanghai, Beijing, Songdo, Seoul, and also smart cities in Taiwan, Indonesia,
Thailand, and India [
4
,
25
,
27
–
29
]. In North America: Toronto, Vancouver, New York,
Washington, and Seattle [
4
,
25
,
27
]. In South America: Medellin and Rio de Janeiro [
27
,
30
,
31
].
In Oceania: Melbourne, Perth, Sydney, Brisbane, and Adelaide [27,32].
The most common smart city project implementations across these cities include:
development of collaborative business districts in Barcelona [
21
,
25
] and Hong Kong [
4
,
27
];
citizen security by traffic monitoring in Rio de Janeiro [
4
,
31
], and natural disaster mon-
itoring in Singapore and Indonesia [
29
,
33
]; public service, smart government, and com-
munication transformation in cities of China (Wuhan, Shanghai, Beijing, Dalian, Tianjin,
Hangzhou, Wuxi, Shenzhen, Chengdu, and Guangzhou) [
28
]; adaptation of cultural spaces
in Medellin [
30
]; citizen engagement and data enhancement in New York and Wash-
ington [
27
]; deployment of experimental testbeds and living labs in Santander [
26
] and
London [
27
]; integration of local and foreign universities in Tampere [
27
] and Songdo [
25
];
deployment of smart green projects and policies in Seoul [
27
] and Toronto [
4
]; fiber optic
and smart grids in Geneva [
27
]; energy efficiency and innovation enhancement in Vi-
enna [
27
]; improved water consumption in Copenhagen, Hong Kong, and
Barcelona [4]
;
deployment of electric charging stations in Malaga and Paris and Amsterdam [
4
]; Big data
integration and analysis in India and Thailand [
29
]; wired communities, pedestrian mobil-
ity, and mass transit solutions in Sydney, Brisbane, Adelaide, Melbourne, and Perth [
32
];
carbon emission reduction in Seattle [
25
]; smart waste collection systems in Helsinki [
25
],
Songdo [
25
], Barcelona [
25
]; smart parking in Milton Keynes [
25
]. Table 1shows the main
Smart City implementations reported in the literature for health, security, mobility, water,
waste management, and energy efficiency, for the countries reviewed in this section. A
more extensive search on smart city deployments around the world was performed us-
ing the results from the literature review, as well as information gathered from related
websites [34–36]. The results of this search are presented in Figure 3.
Appl. Sci. 2021,11, 8198 6 of 29
Table 1. Smart cities implementations around the world.
City Health Security Mobility Water Waste Energy
Tampere
[27]Smart
transportation
Helsinki
[25]Car charging
facilities Automated waste
collection Smart grids
Amsterdam
[4]ICT in health,
Health Lab Clean energy
generation
Vienna [27]Smart parking, car
sharing Energy efficiency
Copenhagen
[4]Bike lane network Water quality
monitoring Optimized waste
disposal Energy efficiency
Stockholm
[4]Water management
policies
Waste management
system
Milton
Keynes
[25]
Smart parking,
MotionMap app
Sensors in recycling
centers Smart metering
app
London
[27]App for public
transport Smart Waste
collection
Malaga [4]Electric vehicles,
charging stations
Smart grids,
clean energies,
smart lighting
Barcelona
[4]Remote healthcare Incident
detectors at
home
Traffic and public
transport
management Smart Containers Centralized
heating/cooling
Santander
[26]
Smart Parking, GPS
monitoring Smart park
irrigation Smart public
lighting
Paris [4]eHealth, smart
medical records Bike sharing,
charging stations
Geneva
[27]Smart
transportation Fiber-optic, smart
grid networks
Singapore
[33]
Siren alerts for
natural
disasters
Traffic maps, public
transport apps
Apps for water
consumption
tracking
Apps for energy
consumption
tracking
Hong
Kong [
4
,
27
]
Smart card IDs
for citizens Open, real-time
traffic data Smart waste
management
Shanghai
[28,37]
Pedestrian
movement analysis
(Big data)
Beijing
[28,38]
V2E solutions,
smart cards for
transportation
Songdo
[25,27]
Remote medical
equipment and
checkups
Self-charging
electric vehicle
technology
Underground
waste suction
system Smart buildings
Seoul [27]Bus service based
on data analytics
Taiwan
[39]
Smart defense
system for law
enforcement
Indonesia
[33]
Flood
monitoring and
report app
Thailand
[33]
Tsunami and
flood
monitoring
Water management
app
India [4]Smart transport
systems Clean energy,
green buildings
Toronto
[28]Smart urban zone
growth
Appl. Sci. 2021,11, 8198 7 of 29
Table 1. Cont.
City Health Security Mobility Water Waste Energy
New York
[27]
Sensors
deployment after
9/11 attacks
Energy
efficiency using
LEDs
Washington
DC [27]Bike sharing, smart
stations
Sensor-based
LED
streetlights
Seattle [25]
Flood monitoring,
law-enforcement
cameras, gunshots
GPS tracking
Smart trafic lights Real-time
precipitation
monitoring
Reduction of
CO2emissions
Medellin
[30]
Outdoor electric
stairs and air
wagons
Rio de
Janeiro [31]
GPS/video
monitoring
installation in
police cars
Traffic monitoring
using cameras
Melbourne
[27]
Smart parking,
open urban
planning, metro
Wi-Fi
Energy
efficiency, smart
grid,
smart lighting
Perth [40]Cyber-security and
digital forensics
Sydney
[32]ICTs in daily urban
transport
Brisbane
[32]Pedestrian spines
Adelaide
[32]
Wired communities
N°of cities per Country
0 4 8 12 16
0 4 8 12 16
United States
India
China
Spain
Germany
Australia
France
England
Canada
South Korea
Netherlands
Italy
Indonesia
Brazil
Vietnam
Thailand
| | | |
North America Europe Africa
South America Asia Oceania
Figure 3.
Map of smart cities across the world. The shade of color represents different number of
smart cities per country. Cities are represented with colored points in the map, identified in six main
regions: Africa, Asia, Europe, North America, Oceania, and South America. The bars on the right
side show the countries with highest number of smart cities.
3.3. Sensors
The general architecture of an intelligent management system consists of readings
(sensors), gateways (communication), and workstations (instructions, analytics, software,
and user interface) [41–43].
Appl. Sci. 2021,11, 8198 8 of 29
3.3.1. Sensors for Health Monitoring
Healthcare has become a prolific area for research in recent years, given that new
sensor technology allows real-time monitoring of the patients’ state. Smart healthcare
provides healthcare services through smart gadgets (e.g., smartphones, smartwatches,
wireless smart glucometer, etc.) and networks (e.g., body area and wireless local area
network), offering different stakeholders (e.g., doctors, nurses, patient caretakers, family
members, and patients) timely access to patient information and the ability to deploy the
right procedures and solutions, which reduces medical errors and costs [44].
Biosensors are fundamental when monitoring health, and different applications can
be identified in medical diagnoses [
45
], and antigen detection [
46
], among others. Inor-
ganic flexible electronics have witnessed relevant results, including E-skin [
47
], epidermal
electronics (see Figure 4) [
48
], and eye cameras [
49
]. Some common materials used to
create these sensors are carbon-based or conductive organic polymers, which present poor
linearity [
50
,
51
]. However, more reliable, and flexible sensors have been created at lower
cost, better linearity, and shorter response time, such as piezoresistive sensors integrating
nano-porous polymer substrates [52].
New sensor developments are creating relevant opportunities in the health industry.
Current procedures for sensing proteins are commonly based on noisy wet-sensing meth-
ods. A more robust procedure is carried out by means of graphene sensors that avoid the
drifting of electrical signals, resulting in more stable and reliable signals. These sensors
also reduce detection times [
53
]. Nonetheless, when having these robust sensors connected
to the human body, one critical challenge is their communication through the wireless
sensor network, since the IEEE 802.15.4 standard can hardly be adapted to multi-user inter-
faces [
54
]. Still, some solutions have been proposed, such as replacing the ultra-wideband
(UWB) with a gateway, so sensor nodes stop and switch to ‘sleep mode’ until new infor-
mation transmission is needed again. This allows lowering the energy consumption and
collisions and increase the speed and number of users [55].
Figure 4.
(
a
) Epidermal electronic system (EES) made of a skin replica created from the forearm,
before and; (
b
) after application of a spray-on-bandage; (
c
) Colourized microscopy image of the EES
with conductive gold films of 100
µ
m and; (
e
) its magnified view. (
d
) Microscopy image of EES with
gold films of 10 µm and; (f) its magnified view [48].
Appl. Sci. 2021,11, 8198 9 of 29
3.3.2. Sensors for Mobility Applications
Three main systems of urban mobility: vehicles, pedestrians, and traffic, are considered
in this section. Due to the increasing number of vehicles every year in urban settlements,
traffic jams, pollution, and road accidents tend to increase as well [
56
]. These problems
suggest that there is an emergent need for intelligent mobility solutions. One such solution
is intelligent traffic control, oriented to avoid traffic jams and optimize traffic flow [18].
Due to repetitive starts and stops, fuel consumption and carbon emissions increase
during traffic jams [
18
]. Therefore, providing solutions for traffic jams represents a direct
positive impact in terms of urban mobility and air quality in cities. Moreover, heavy-duty
vehicles (HDVs) and freight traffic release into the atmosphere large quantities of carbon
emissions [
57
]. The automotive industry has put significant effort into developing more
energy efficient powertrains (for example, hybrid electric vehicles). However, most HDVs
are still fueled by diesel and providing optimal solutions to reduce the carbon emissions
produced by these types of vehicles becomes a fundamental task [58].
Due to the COVID-19 pandemic, urban mobility underwent significant changes, such
as a noticeable decrease in collective and individual transport, and with that a reduction
in air pollution and carbon emissions [
59
]. This phenomenon has made governments
and citizens consider future changes in post-lockdown mobility, to maintain cleaner en-
vironments in their cities. Applications in smart mobility include vehicle–vehicle (V2V),
vehicle–infrastructure (V21), vehicle–pedestrian (V2P) [
60
], and vehicle–everything (V2X)
connections (see Figure 5) [61].
Vehicles include several sensors needed for their proper operation, measuring several
operational parameters of the vehicle, such as speed, energy consumption, atmospheric
pressure, and ambient temperature [
62
,
63
]. Such parameters are used to optimize speed
profiles to minimize vehicle energy consumption considering traffic condition and ge-
ographical information. To achieve this aim, a cloud architecture is implemented that
retrieves information from vehicle sensors and external services. In [
64
], an eco-route
planner is proposed to determine and communicate to the drivers of heavy-duty vehicles
(HDVs) the eco-route that guarantees the minimum fuel consumption by respecting the
travel time established by the freight companies. Additionally, in this case, a cloud comput-
ing system is proposed that determines the optimal eco-route and speed and gear profiles
by integrating predictive traffic data, road topology, and weather conditions. Vehicle
weight and speed regulation are also important to ensure road and passengers safety, help-
ing in the avoidance of serious accidents [
65
]. Efforts in increasing pedestrian’s safety are
valuable contributions in improving urban mobility [
66
]. Regarding traffic, conventional
traffic light systems are defined in a non-flexible structure, such that light transitions have
defined delays and onsets [
67
]. Dynamic changes in traffic volume, congestions, accidents,
and pedestrian confluence, should have been considered to provide an optimized traffic
control [67].
Pedestrian
´
s movement and behavior in urban settings have been monitored mainly
using cell phones, by monitoring call detail records (CDRs) [
68
], social media check-
ins [
37
,
69
], MAC address reading [
70
], and smart cards detection in public transport [
71
].
Vehicle detection have been achieved by cement-based piezoelectric, induction loop sen-
sors, measuring vehicle’s weight-in-motion (WIM), and performing vehicle type classifica-
tion [
65
], and ferromagnetic sensors buried in the asphalt for smart-parking
solutions [72]
.
Vision-based sensors, such as infrared (IR) [
67
] and light detection and ranging (LiDAR) [
73
]
have been used to detect the position of vehicles, pedestrians and buildings within a given
proximity. Pedestrian–vehicle (P2V) oriented sensors also exist, such as the “smart car
seat”, a contact-free heart rate monitoring sensor oriented to ensure driver’s well-being
and safety [
74
]. Mobile phone apps have allowed P2V and V2P applications for collision
prediction [66,75].
Appl. Sci. 2021,11, 8198 10 of 29
Figure 5.
Two types of V2V connections: (
a
) from vehicles to vehicles with an intermediate transfer
connection and; (b) directly from vehicles to other vehicles [61].
Recently, virtual sensors have been used to enhance innovative solutions especially
in the electro-mobility sector. VSs have been introduced for operating in the sensor-cloud
platform as an abstraction of the physical devices. In particular, a VS can logically reproduce
one or more physical sensors, facilitating and increasing their functionalities, performing
complex tasks that cannot be accomplished by physical sensors [
76
]. Differently from a
real sensor, the VS is equipped with an intelligent component based on data processing
algorithm to derive the required information elaborating the available input data from
heterogeneous sources. Indeed, VS is typically used in services in which it is necessary to
derive data and information that are not available or directly measurable from physical
sensing instrumentation [
77
,
78
]. Although the use of such sensors has been explored in
different domains or verticals of the smart city, the mobility sector is the one where they
find large application. In the electric mobility domain, for instance, they are used to predict
the personal mobility needs of the driver to estimate the duration and cost of the battery
charging, to predict the energy demand of medium or long-range trips, etc. All these
predictions are performed through ad-hoc algorithms able to process available input data
from the electric vehicles, the users, and the charging stations [76–78].
3.3.3. Sensors for Security
The human and environmental security approaches are a very crucial ingredient to
achieve sustainable development in smart cities. Security is referred to as a state of being
free from danger or threat and for maintaining the stability of a system. Safety is a dynamic
equilibrium, which consists in maintaining the parameters important for the existence of
the system within the permissible limits of the norm. According to the United Nation’s
Human Security Handbook and Agenda 2030 Sustainable Development Goals (SDGs) [
79
]
the types of insecurities endangering the sustainable development of humans and, hence,
future cities are: food, cybernetic, health, environmental, personal, community, economic,
and political, as the main core of a smart city.
Food security: The importance and technological challenges of the integration of urban
food systems in smart city planning are discussed in [
80
]. High quality and sustainable
production include smart hydroponics and gardening systems that gather information
by sensors that measure pH, humidity, water and soil temperature, light intensity, and
moisture [
81
]. Several methods are proposed to monitor the quality and safety of the food
during production and distribution, including gas sensor array [
82
] for the analysis of
chemical reaction occurred in spoiled food. Hybrid nanocomposites and biosensors have
also been reported in food security context [83].
Cyber security: The main security challenges, including privacy preservation, securing
a network, trustworthy data sharing practices, properly utilizing AI, and mitigating failures,
as well as the new ways of digital investigation, are discussed in [
11
,
40
]. Design plane
solutions are usually software-based and use diverse types of encryption techniques,
including advanced encryption standard (AES) and elliptic curve cryptography (ECC)
Appl. Sci. 2021,11, 8198 11 of 29
for crypto or level security and encryption, authentication, key management, and pattern
analysis for the system-level security [84,85] (see Figure 6).
Figure 6.
Smart city architecture defined in five planes: application (connects city and citizens),
sensing (sensors measurements), communication (cloud services), data (processing and analysis) and
security and privacy planes (assurance of security and privacy) [85].
Health security: In-body inserted devices are designed to communicate with health-
centers and hospitals [
55
]. The privacy, security, and integrity of these sensors and the
information on the health record concerning legal and moral issues are of great interest
which is widely discussed in works such as [86,87].
Environmental security: In recent decades, the use of satellite remote sensing and
in-orbit weather observation, disaster prediction systems have risen drastically. These
tools are an integral sensing part of the future smart cities [
88
,
89
]. There is a wide range
of sensors, including earthquake early detection systems which use vibration detection
and monitoring soil moisture and density of the earth [
90
], radiation level detectors [
91
],
tsunami inundation forecast methods assimilating ocean bottom pressure data [
92
]. These
sensors are connected in a wireless network, offering a global prognosis of environmental
threats. Continuous emission monitoring systems (CEMS) have helped develop market-
based environmental policies to address air pollution [
93
]. CEMS are allowing better
tracking of powerplant emissions in real time to inform decarbonization strategies for
the grid [
94
]. Efforts to deploy cost-effective sensing capabilities so far have produced
fragmented data, but new optimization and AI tools are being proposed to resolve this
issue [
95
]. New smart sensing and visualization (e.g., satellite, LiDAR, etc.) capabilities
are focusing on greenhouse gas emissions (GHG), for instance around the carbon capture
potential of agriculture, forestry, and other land uses (e.g., natural climate solutions). Ad-
vanced monitoring, reporting, and verification (MRV) features will continue to play a major
role in enhancing the transparency, environmental integrity, and credibility of subnational,
national, and regional emissions trading systems (ETS) for the future integration of a global
carbon market [
96
,
97
]. Regarding infrastructure and buildings, continuous monitoring
to detect corrosion and minor damages to prevent a possible failure takes advantage of
the integration of surveillance cameras, humidity, atmospheric, and stress sensors, among
others [
98
]. A simple combination of vibration and tilt sensing devices provides one of the
low-cost and high-efficiency techniques proposed for a wide range of structures [99].
Personal and community security: To detect anomalies, violence, and unauthorized
actions, biometrics and surveillance cameras are widely used. Smart lighting systems are
Appl. Sci. 2021,11, 8198 12 of 29
a useful and cost-effective tool that uses common sensors like light and motion detectors
and can improve the security tasks [
100
]. Surveillance cameras, face-recognition systems,
and global positioning systems (GPS), in combination with data handling systems, are
increasingly common tools in the hand of law-enforcement agencies as smart public security
strategy reported in [
39
]. Ref. [
101
] reviews the possible combination of different devices
categorized in sensors, actuators, and network systems. The challenges presented by the
growing use of such technologies and concerns for individual privacy is the topic of an
emerging research area [102].
3.3.4. Sensors for Water Quality Monitoring
Water is an invaluable commodity and is necessary for any living being. Smart
water management focuses mainly on making water distribution systems more efficient
by applying sensors and telemetry for metering and communication [
103
,
104
]. It applies
in three broad areas: fresh water, wastewater, and agriculture. Moreover, more holistic
perspectives around shared resource systems, such as the water–energy–food nexus are
also benefiting from new sensing capacities and smart management systems enabled
by digital technologies to provide more sustainable, resource efficient use solutions [
97
].
The principal usefulness of smart water systems lies in controlling valves and pumps
remotely [104] measuring quality [103], pressure, flow, and consumption [105].
Consumption monitoring includes metering and model applications to describe con-
sumption patterns. Water loss management encompasses leakage detection and local-
ization [
105
]. For water quality the focus is on measuring, analyzing, and maintaining
a set of pre-established parameters. It is an integral real-time management involving
stakeholders [
106
–
108
]. In the agriculture, the use of IoT devices is a common way to make
irrigation more efficient and effortless [
109
–
111
]. Noise sensors and accelerometers are
popular methods to detect leaks in water distribution infrastructure [105,106].
The use of electromagnetic and ultrasonic flow meters and sensors for measuring
pressure are IoT technologies for water consumption rate analysis [103,108].
Sensors used to analyze the quality of the water are mainly applied for physical–
chemical parameters such pH, temperature, electrical conductivity and dissolved oxy-
gen [
108
,
109
], also oxidation-reduction potential and turbidity [
112
–
114
], and presence of
toxic substances [
115
]. In some cases, novel probes, such as for residual chlorine [
103
] or
nitrate and nitrite, were implemented (see Figure 7) [
112
]. Humidity sensors are applied
to measure soil moisture and assist in managing the schedule programs of irrigation in
agricultural lands [110,116,117].
Appl. Sci. 2021,11, 8198 13 of 29
Figure 7.
Outer and inner view of an integrated IoT sensor for water quality monitoring applications.
The sensor consists of a nitrite and nitrate analyzer based on a novel ion chromatography method,
used for detection of toxic substances [112].
3.3.5. Sensors for Waste Monitoring
Smart waste management consists of resolving the inherent problems of collection and
transportation, storage, segregation, and recycling of the waste produced. Use of smartbins,
solutions for the Vehicle Routing Problem (VRP) and waste management practices have
been reported [
41
,
118
]. The use of smartbins refers to the implementation of different kind
of sensors in the bins used to collect waste, which provide quantitative and qualitative
information about the bin content [119–121].
For the VRP, the proposals are algorithms for optimization of the routes, considering
social, environmental, economic factors, peak hours, infrastructure, type and capacity of
the collection vehicles and others, in an effort to save resources like money, time, fuel, and
labor [121–123].
With this, researchers aim for an integrated, real-time management that involves the
communities and all the stakeholders [124,125]. The principal use of sensors in smartbins
is monitoring the volume, weight, and content of the bins. For monitoring the filling level
of the container, the main approaches that have been used are ultra-sound (US) [
43
,
119
],
and in some cases IR sensors [
121
,
126
]. A load cell is also used to detect the weight of
the bin [
124
,
127
]. In the literature, various sensors are used to detect harmful gases [
126
],
movements near the container [
120
], and metal sensors to separate metallic waste [
128
],
and to measure humidity [
126
,
128
], as well as temperature [
43
,
123
]. The My Waste Bin IoT
container presented in [43] is shown in Figure 8.
Appl. Sci. 2021,11, 8198 14 of 29
Figure 8.
Front and back view of the My Waste Bin, an IoT smart waste container, enabling real-time
GPS tracking and weight monitoring [43].
3.3.6. Sensors for Energy Efficiency
Energy is an essential resource for the operation of the many activities occurring in
cities [
14
]; therefore, the efficient use of this resource is paramount to reduce costs and
promote environmental and economic sustainability [129].
The main sinks of energy consumption in urban communities are those associated
with industrial and transport activities, buildings operations, and public lighting. In this
section, we focus on the sensors used to monitor the usage of energy ground transport
in buildings and public lighting. Since sensors for industrial activities were covered in
previous sections.
Ground transportation represents the main sink of energy consumption (
∼
45%) and the
major source of air pollutants in urban centers [
130
]. Car manufacturers report the specific
fuel consumption (SFC measured as L/km) of their vehicles using laboratory test protocols.
However, they do not report these data for heavy-duty vehicles. Furthermore, the real
vehicles’ energy consumption is affected by human (driving), external (traffic, road, and
weather conditions), and technological factors. For gasoline and diesel-fueled vehicles, the
common strategies to measure real-world fuel consumption on a representative sample
of vehicles are: (i) measuring the fuel’s weight before and after a specific distance being
driven (gravimetric method), and (ii) measuring instantaneous fuel consumption through
the on-board diagnostic system (OBD method). This second alternative uses optical sensors
to measure the engine RPM, pressure sensors to measure the inlet air flow. The engine
computer unit (ECU) uses these measured data to determine the engine fuel injection
time. In addition, a global position system (GPS) determines the vehicle’s speed. Using
all this information, the ECU reports via OBD the vehicle’s instant fuel consumption.
Currently, there are commercially available readers that read the OBD data and send
the collected information to the cloud. Using these technologies, telematics companies
monitor thousands of vehicles in operation [
131
–
133
]. Similar systems are available for
electric vehicles. We recommend this OBD-based alternative to measure the real energy
consumption in ground vehicles.
Buildings represent 40% of total energy consumption [
134
] and 30% of greenhouse
gas (GHG) emissions [
129
,
134
]. The main physical and non-physical factors involved
in indoor environment quality are shown in Figure 9[
134
]. These factors are measured
using wireless sensors [
135
,
136
], virtual sensors [
137
], and artificial neural networks [
16
].
Energy consumption in buildings is associated mainly with (i) thermal comfort (operation
of heating, ventilation, and air conditioning-HVAC systems); (ii) indoor lighting; (iii)
Appl. Sci. 2021,11, 8198 15 of 29
various electrical loads (operation of electric equipment); (iv) thermal loads (use of fuels
for heating and cooking), and (v) indoor air quality (pollutant concentration, odor, and
noise) [
138
,
139
]. Table 2shows the variables used to grade these five aspects and the sensors
frequently used to measure these variables. However, additional variables influence energy
consumption, such as the occupation level [
140
], and the building’s structural design [
141
],
and outdoor conditions (temperature, humidity, pressure, and solar radiation). Therefore,
additional sensors are used to measure them. Some research works have focused on
designing intelligent building management systems (BMS) that use, in real-time, data
from the sensors listed above and take actions oriented toward the reduction in energy
consumption, such as turn lights off, closing doors and windows, etc. [142].
Public lighting systems represent almost 20% of world electricity consumption, and it
is responsible for 6% of GHG [
143
]. Therefore, it is essential to centralize street lighting
control and smart management to reduce energy consumption, maintain maximum visual
comfort and occupant requirements [
139
]. The variable most used in lighting systems is
the lighting power density (LPD) [
138
]. Neural networks, wireless sensors, algorithms, and
statistical methods are used to estimate the energy consumption and the corresponding
costs [
129
,
135
,
136
]. Environmental factors, pedestrians’ flow, weather conditions, and
brightness levels influence light intensity [
129
,
144
]. The urban space adopts the most
advanced Information and ICTs to support value-added services to manage public affairs,
connecting the city and its citizens while respecting their privacy [20,145].
Figure 9. Physical and non-physical factors in IEQ studies [134].
Most of the time, the monitoring of the variables influencing energy consumption
in buildings and public lighting is carried out wirelessly [
18
,
129
]. Various intelligent
sensors [
135
], such as artificial vision, are used to measure temperature, relative humid-
ity [
17
], electric current, gas flow, air quality [
139
], lighting, luminosity [
129
], solar radi-
ation [
139
], and acoustic emission [
146
]. Measurement devices include light-dependent
resistor (LDR) [
139
], IR radiation [
140
,
147
], semiconductors, magnets, and optic fiber. Intel-
ligent sensors based on IoT are key to real-time monitoring of the many variables involved
in energy management; these sensors can be adapted to microcontrollers and virtual sen-
Appl. Sci. 2021,11, 8198 16 of 29
sors [
129
,
137
]. The main problem with wireless sensors is their battery life; therefore,
alternative energy sources (thermal, solar, wind, mechanical, etc.) is vital, although these
energies are usually available in minimal quantities [18].
Table 2. Sources of energy consumption in cities.
Sources of Energy Consumption Variable Sensor Application
Air
Conditioning Heat/Cooling
Temperature/Humidity
Thermohydrometer Temperature and relative humidity
CO2/CO CO2/CO Concentrations
Indoor Air
Quality Air
pollution/Concentration NO2/NO NDIR (Non-Dispersive
Infrared) NO2/NO Concentrations
Air renovations VQT airflow HVAC system/equipment, building
automation, vents
Presence Passive Infrared (PIR) Count/Occupation/Movement of
people and
vehicles/Temperature/Security
Lighting Indoor/Outdoor Light
Light Depending Resistor (LDR)
Color/Resistance/Security alarms,
Lighting On/Off
Brightness Phototransistor
Illumination Photodiode
Home networks, Wi-Fi LED
luminaires/Indoor lighting
apps/Proximity Light level
3.4. Communication Technologies
Some of the most common communication technologies involve 3G, 4G LTE, and
5G Wi-Fi network connections [
109
,
114
]. In general, communication infrastructures can
be classified as local area networks (LAN), wide area networks (WAN), and global area
networks (GAN) [
116
]. LAN sensors can communicate with gateways through different
protocols, such as: long range (LoRa) [
107
], Bluetooth [
126
], and low power radio [
41
].
Furthermore, new technologies have arisen in recent years, such as ZigBee [
110
] and
narrowband internet of things (NB-IoT) [
72
]. On the other hand, gateways in WAN
connections send the information to the cloud through SigFox, long range wide area
network (LoRaWAN) [
112
], while GAN encompasses more complex technologies and
all the cellular networks GSM-GPRS [
121
,
148
]. Other protocols include IP and API, TCP,
Dash7 and MQTT [
17
], along with standards such as IEEE802.15.4, IEEE802.11.x, and
IEEE1451 [135].
However, as the number of network nodes increase, interference problems can take
place, particularly in very dynamic environments, such as health and mobility [
149
]. For
instance, inter-body network problems depend on the number of sensors per network, as
well as the body networks in a location, traffic, physical mobility of each body, and the
location of nodes on the body [
150
]. In order to attenuate this problem, active and passive
schemes have been proposed, which allow a high throughput and packet delivery ratio, as
along with both a low average end-to-end delay and low average energy consumption for
a single and multi-WBSNs [
151
,
152
]. Similarly, sharing data through connected vehicles
can occur through incorporated or third-party systems [
62
], but accuracy and availability
widely depend on the interface used [
153
]. As the number of users increases, the safety,
access, and efficiency can be affected. Here, the vehicle itself is considered an integrated
sensor platform [
154
], since it can work as a data sender, receiver, and router within the
V2V or V2I communications used in ITS [19].
3.5. Applications
In the health section, sensors can be used in different multidisciplinary areas, for
example: smart toilets, applications that monitor and direct physical exercise with the
final objective of rehabilitation or prevention of injuries, and applications in health insti-
tutions [
155
,
156
]. For cycling sports, sensors are used most frequently in long-distance
running and swimming [
157
]. There are different methods used to monitor the condition of
patients, for example: monitoring the electrical activity of the heart using an array of elec-
Appl. Sci. 2021,11, 8198 17 of 29
trodes placed in a sterile way on the body [
158
], monitoring of vital signs via wireless [
159
]
for the tracking of the patient’s location by issuing alerts if necessary, implementation of
user-friendly interfaces to share information, based on wireless ECG sensors and pulse
oximeters [
159
], implementation of a network-based multi-channel frequency EM for reha-
bilitation patients with exercises [
160
], design of a system with three sensors to monitor the
ECG, body weight, and pulse of the patient [
161
], portable home health monitoring system
using ECG, fall detection, and GPS to monitor people outside [
162
], e-health monitoring
system based on the fusion of multisensory data to predict activities and promote the
decision-making process about the health of the person [
163
]; all of these home health
systems allow to monitor patient’s activity in their homes.
In the mobility section, the most common ITS applications in reducing road traffic
are related to accident detection [
164
,
165
] and prevention [
40
], identifying road traffic
events [
166
] and studying the driving behavior and applying real-time feedback [
167
,
168
].
Interesting applications in urban mobility have been proposed as well, such as smart
traffic lights [
67
], smart parking [
72
], collision prediction and avoidance [
66
], vehicle
WIM detection [
65
], and mobility visualization through heatmap representations [
169
].
Heatmaps can also be obtained from analyzing mobility data from vehicles and pedestrians
and reflect the behavior of different phenomena, such as average speed in the city [
170
],
traffic jams location, frequency, and duration [
171
], and combined spatio-temporal traffic
clustering and analysis [169].
For the water section, by analyzing parameters like soil humidity and nutrients, the
design of efficient irrigation programs, including remote control or automatic irrigation
systems, can be done, reducing water and energy consumption [
42
,
127
,
172
]. For example,
a system that allows real-time monitoring of surface water quality in different aquatic
environments [
43
], systems for monitoring the quality of the river that crosses a city [
173
],
and the implementation of a system that could be used in pipelines networks to monitor
the quality of water [
122
]. Distribution systems need to be regulated to ensure the required
quantity and quality. The use of sensors to collect data in real-time can detect and locate
leaks, which can affect the correct supply of water to a city or deteriorate the infrastructure
around the leak [
14
]. The water distribution systems need to be monitored to ensure that
correct water quality is distributed and for detecting pollutants [174].
In the waste section, the IoT system permits an onboard surveillance system, which
raises the process of problem reporting and evidence good waste collection practices in
real-time [
135
]. The use of mobile apps and software permit that truck drivers receive
alerts from the smart bins that need attention, and also to get the optimal route to collect
the garbage, reducing the effort and cost of waste collection [
136
–
138
]. With the collection
of information about the filling level of the containers, it is possible to determine the best
types and sizes of containers, areas that require greater or special collection capacity, and
the timing of the collection [
134
–
136
,
138
]. The sensors can improve the automation in
identifying and separating waste, allowing an increase in the processing speed for reuse
and recycling to convert a smart city to a city with zero-waste [
20
,
137
]. All data collected
from the bins and the analytics, in conjunction with the use of a GPS to know the coordinate
position of bins, dumps, and fleet, can be used to manage in novel ways the collection and
segregation of waste [17,129,136,138,141].
Finally, countries are looking to implement innovative technologies focused on mini-
mizing energy consumption and improving their citizens’ environment and welfare. For
this reason, there are various applications in the areas of buildings, public lighting, and
urban space such as counting, movement, and location of people and vehicles, security
actions for citizens, fire detection in building enclosures [
140
], smart homes [
135
,
147
,
175
]
and home networks [
135
,
136
], Light Emitted Diode-wireless (LED) indoor and outdoor
lighting fixtures [18], geothermal technology [137], hygrothermal comfort [17], cybernetic
cities, ubiquitous connectivity [
176
], HPCense (seismic activity), smart thermostat [
18
],
indoor lighting apps [
129
], microgrids [
139
], structural health monitoring of buildings [
18
],
ecological buildings [134], among others.
Appl. Sci. 2021,11, 8198 18 of 29
3.6. Study Cases
This section describes experimental results of study cases implemented by the research
groups of the co-authors of this review, developed in Tecnologico de Monterrey, under
the Campus City initiative. Figure 10 shows a visual representation of the study cases
discussed in this section.
Health. In this study, by using EEG electrodes and Bluetooth, brain signals from
students were recorded to assess learning outcomes under different modalities [177]. The
aim of the work was to propose EEG sensing as a support in education by inferring the state
of the brain. The results showed that machine learning models based on the EEG recordings
were able to predict with 85% accuracy, the cognitive performance of the students, and it
could also be used to identify unwanted conditions, such as mental fatigue, anxiety, and
stress under different contexts in the healthcare sector.
Security. PiBOT is a multifunctional robot developed to monitor spaces and imple-
ment regulations in the context of the COVID-19 pandemic [
178
]. Such robot integrates
video and thermal cameras, LiDAR, ultrasonic, and IR sensors to allow object and people
detection, facemask recognition, temperature maps, and distance between persons estima-
tion. It also integrates automated navigation algorithms, teleoperation control modes and
cloud connection (IoT) protocols for data transmission. The robot is also able to generate
and send real-time data to a web server about people count, facemask misuse, and safe
distance violations.
Mobility. The following study presents the results from the analysis of accessibility to
different services (health and education) in the urban environment of the city of Monterrey,
Mexico [
179
]. The software tools used in this study enabled to obtain quantitative repre-
sentations of the accessibility of the city when using different transport modes (walking
and cycling). The results from this work showed low accessibility to medical services, but
acceptable accessibility to educational services in the city. The study found that the use
of bicycles and other micro mobility vehicles can enhance the accessibility to services in
the city.
Water. A recent study from one of the co-authors studies the presence of SARS-CoV-2
RNA in different freshwater environments (groundwater and surface water from rivers
and dams) in urban settings [
180
] from the city of Monterrey, Mexico. The detection and
quantification of the viral loads in such environments were determined by RT-qPCR in
samples acquired from October 2020 to January 2021. The results of the study demonstrated
the feasibility of the presence of SARS-CoV-2 in freshwater environments. It also found that
viral loads variations in groundwater and surface water over time at the submetropolitan
level reflected the reported trends in COVID-19 cases in the city of Monterrey.
Waste. A recent collaboration between Tecnologico de Monterrey and industrial part-
ners from Smart City Colombia [
181
] and SmarTech [
182
] has started for the development
of smart solutions for cities. This collaboration proposes the use of a smart recycling
implementations using an urban mechanism for intelligent disposal (MUDI). The MUDI is
a GPS monitored vehicle that establishes optimal routes for collection of recycled material,
while informing both the recycler and the citizen through an app about the final disposal
of said materials.
Energy. Using engine sensors, information about the fuel consumption associated to
the operation of the A/C in light-duty vehicles was monitored via OBD for a five-month
period [
131
]. Results showed that specific fuel consumption due to A/C usage is higher at
lower speeds of the vehicle and it is lower at higher driving speeds; and shows the potential
of proposing solutions towards vehicle energy efficiency by analyzing information coming
from engine sensors through the OBD port.
Appl. Sci. 2021,11, 8198 19 of 29
Figure 10.
Study cases of smart cities implementations in Tecnologico de Monterrey, (
a
) Health: EEG monitoring for
educational services; (
b
) Security: Space monitoring in the context of COVID-19 pandemic; (
c
) Mobility: Urban accessibility
analysis in Monterrey City; (
d
) Water: Detection of SARS-CoV-2 RNA in freshwater environments from Monterrey City;
(
e
) Waste: GPS monitoring and app for recycling vehicles; (
f
) Energy: Impact of A/C usage on light-duty vehicle’s
fuel consumption.
Appl. Sci. 2021,11, 8198 20 of 29
4. Challenges and Opportunities
Advances in the use and implementation of sensors and their application for the
development of smart cities will allow residents to access a better quality of life. Even
though the use of wireless sensor networks provides valuable data that are used for a better
management of resources, there are still areas of opportunity for improvement. Although
different areas within the smart city face specific challenges, a common opportunity is the
development of new sensors and new approaches to the problem of detection, prevention,
or anticipation of the dangers which future smart cities can face.
A major challenge in health applications is privacy and the secure transmission of
data, a concern for which different studies have been conducted. One example is a de-
centralized mobile-health system that leverages patients’ verifiable attributes in order to
run an authentication process and preserve the attribute and identity [
183
]. Another study
designs two schemes that focus on the privacy of medical records. These schemes ensure
that highly similar plaintexts can be transformed into distinctly different ciphertexts and
resist ciphertext-only attacks. Nevertheless, important performance metrics, such as com-
putation overhead, network connectivity, delay and power consumption, are ignored [
184
].
Low-cost wireless sensor networks also help to achieve a direct communication between a
user’s mobile terminals and wearable medical devices while enforcing privacy-preserving
strategies [
149
]. In addition, a study presented a big health application system based on
big data and the health internet of things (IoT). This study also proposed the cloud to
end fusion big health application system architecture [
185
]. One last study modifies the
design of sensor networks in a way that each one can manipulate four symbols (quaternary)
instead of two (binary), resulting in more efficient systems [186].
With respect to mobility, ITS are used in smart cities, having a positive impact on
saving resources, such as time and workforce, while reducing the use of fuels and emissions
into the atmosphere. ITS image-based mobility applications are simple and inexpensive,
but face decreased efficiency during lightning and weather changes [
18
]. Another chal-
lenge is faced by flexible traffic control, as well as collision avoidance systems as high
speed detections and data exchange are needed for successful V2V, V2P, V2I, and V2X
protocols [18,38,66]
. This information exchange process is susceptible to security threats,
such as malicious attacks or data leaks [
61
]. To address these challenges, more reliable
sensors and faster data transfer protocols need to be implemented.
In smart security, there is a big effort on detection, prevention, or anticipation of the
dangers that citizens and infrastructure of the smart cities can face. Sensors for security
present a tendency to improve the sensibility, resolution, and precision of the current
sensors. Almost all services in a smart city use digital data and are completely dependent
on the security and integrity of that data. Due to this reason, sensors must be hardened with
effective security solutions such as cryptography and advanced self-protection techniques.
Regarding smart water monitoring, sensors are used to measure water quantity
and quality data continuously and consistently. The obtained data can be processed and
visualized in real-time to the end-users, or forecasts can be developed for the water agencies.
These technologies allow minimization of the risks associated to poor water quality and
deficiencies in water supply. Future sensors need to be improved in cost and energy
consumption to withstand long periods of measurements without intervention, in addition
to an enhanced robustness, to resist adverse environmental conditions.
Solid waste management is crucial in any town or city, but take a new role in the smart
city scheme, and it is focused on a more clean, tidy, and healthy environment for living,
using sensors and IoT technologies to improve waste management [
42
,
43
]. Currently there
are only sensors that can identify wet, dry, or metal garbage; however, it would be optimal
to develop sensors that allow identifying waste in greater detail. For this reason, new
sensors oriented to waste segregation need to be developed and implemented. Segregation
is a key component in the waste management system, as it allows much of what is discarded
to be recycled or reused, resulting in a reduction in the amount of garbage that reaches
landfills [128,172].
Appl. Sci. 2021,11, 8198 21 of 29
Innovations in energy consumption monitoring in buildings, public lighting systems,
and urban spaces using ICTs are an excellent option due to their adaptability. The liter-
ature proposes the implementation of virtual sensors by building information modeling
(BIM), integrated with IoT devices including qualification tools to develop ecological
buildings [
134
,
187
]. Intelligent lighting systems with sensors adaptable to weather con-
ditions, hours of use and presence of people or vehicles [
20
] where the street lamps
serve as Wi-Fi connection points, allowing interconnected networks over the entire urban
area monitoring the quality of the environment, noise levels, and surveillance, among
others. Battery or energy consumption, high volume of data storage and security, life
span and replacement, size, cost, installation, and maintenance are the main deficiencies
when designing a WSN. Studies have proposed the implementations of energy efficiency
surveillance using multimodal sensors [188] and low power hardware systems [189]. The
implementation of low-cost sensors and using energy harvesting are the most attractive
technologies for buildings’ sensors in the future. Artificial intelligence, big data, and
machine
learning [190,191]
become essential due to the vast amount of data gathered and
analyzed in the presented applications.
5. Conclusions
Following the reported trends of population growth and mobility to urban environ-
ments, it is clear that in the years to come, cities will face a constantly increasing need to
satisfy the demands of their citizens [
1
]. Diverse strategies have been implemented in cities
from all continents to move towards smartness as a means to enhance management of their
resources, offer more efficient and trustable services, improve the liveability of the city, and
promote government, academia, and citizens’ engagement.
Geographically speaking, continents such as Europe and Asia have the highest amount
of reported smart city implementations, followed by America, Oceania, and Africa. High-
income countries such as United States and China presented a high number of smart
city deployments at different cites. Although other continents and cities present fewer
smart cities, it is only a matter of time for more to arise, as they follow the steps of their
predecessors [
28
]. Several applications of sensors for smart cities were identified and
described within this work. To summarize, most of the applications involved the sensing
and sharing of data to offer on-demand services (medical records and check-ups, urban
transportation, water, and energy consumption), while others are oriented to improve the
liveability of the city (citizens’ security, green spaces management, water quality, waste
collection, public lighting). Among the main challenges of smart city implementations,
each sector has its own; however, common factors such as the improvement of sensors,
massive data analytics implementations (big data), and citizens’ distrust in data sharing
can be identified.
Although in future smart cities, the security of the society and community is to be
ensured by digitalization and inter-institutional cooperation, human security, generally
speaking, is guaranteed by the individual’s behavior. It should not be forgotten that safety
cannot be forced; it can only be educated, and there is a need to form an internal motive
for safe and ethical behavior by creating and fostering a culture of safety in such a new
digital environment. Finally, it is also important to consider that in order to implement the
proposed smart city solutions, collaboration, and partnership with government agencies is
imperative [
3
]. Deep understanding of each context of implementation and key intercon-
nections between sectors (e.g., transport–energy, energy–water–food, resource efficiency
and recovery, etc.), along with meaningful community engagement and involvement in the
planning and use of new technologies in the urban infrastructure is essential to enhance
political feasibility, transparency, equity, and financial sustainability.
As societies around the world begin to better understand how technological progress
can improve quality of life and foster clean economic development, smarter communities
will be driving the future of cities towards a more liveable, inclusive, net-zero carbon and
sustainable future by mid-century [192].
Appl. Sci. 2021,11, 8198 22 of 29
Author Contributions:
Conceptualization, J.d.J.L.-S. and R.A.R.-M.; methodology, M.A.R.-M., R.E.P.-
G.; validation, J.d.J.L.-S., R.A.R.-M. and S.C.M.; investigation, M.A.R.-M., S.K., D.A.P.-R., E.R.-L.,
M.G.-M., M.C.H.-L., A.E.M., J.M., J.I.H., R.E.P.-G., R.A.R.-M., A.M.M., M.R., B.L., P.-H., S.C.M.,
J.d.J.L.-S.; writing—original draft preparation, M.A.R.-M., S.K., D.A.P.-R., E.R.-L., M.G.-M., M.C.H.-L.,
A.E.M., R.E.P.-G., R.A.R.-M., A.M.M., M.R., B.L.P.-H.; writing—review and editing, J.M., J.I.H., S.M.
and J.d.J.L.-S.; supervision, R.A.R.-M., S.C.M. and J.d.J.L.-S.; project administration, J.M., J.I.H. and
J.d.J.L.-S.; funding acquisition, R.A.R.-M. All authors have read and agreed to the published version
of the manuscript.
Funding:
This project is funded by the Campus City initiative from Tecnologico de Monterrey. The
APC was funded by Tecnologico de Monterrey.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
Authors would like to thank the support of Tecnologico de Monterrey through
the Campus City initiative and the California-Global Energy, Water & Infrastructure Innovation
Initiative at Stanford University.
Conflicts of Interest:
The authors declare no conflicts of interest. The funders had no role in the
writing of the manuscript.
References
1.
Calzada, I. Metropolitan and city-regional politics in the urban age: Why does “(smart) devolution” matter? Palgrave Commun.
2017,3, 1–17, doi:10.1057/palcomms.2017.94.
2.
Batista e Silva, F.; Freire, S.; Schiavina, M.; Rosina, K.; Marín-Herrera, M.A.; Ziemba, L.; Craglia, M.; Koomen, E.; Lavalle, C.
Uncovering temporal changes in Europe’s population density patterns using a data fusion approach. Nat. Commun.
2020
,
11, 1–11, doi:10.1038/s41467-020-18344-5.
3. Joss, S. Future cities: Asserting public governance. Palgrave Commun. 2018,4, 1–4, doi:10.1057/s41599-018-0087-7.
4.
Pellicer, S.; Santa, G.; Bleda, A.L.; Maestre, R.; Jara, A.J.; Skarmeta, A.G. A global perspective of smart cities: A survey. In
Proceedings of the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing,
Taichung, Taiwan, 3–5 July 2013; pp. 439–444, doi:10.1109/IMIS.2013.79.
5.
Bai, X.; Nagendra, H.; Shi, P.; Liu, H. Cities: Build networks and share plans to emerge stronger from COVID-19. Nature
2020
,
584, 517–520, doi:10.1038/d41586-020-02459-2.
6.
Staleti´c, N.; Labus, A.; Bogdanovi´c, Z.; Despotovi´c-Zraki´c, M.; Radenkovi´c, B. Citizens’ readiness to crowdsource smart city
services: A developing country perspective. Cities 2020,107, 102883, doi:10.1016/j.cities.2020.102883.
7. Pranggono, B.; Arabo, A. COVID-19 pandemic cybersecurity issues . Internet Technol. Lett. 2021,4, 4–9, doi:10.1002/itl2.247.
8.
He, Y.; Aliyu, A.; Evans, M.; Luo, C. Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping
review. J. Med. Internet Res. 2021,23, 1–18, doi:10.2196/21747.
9.
Ahad, M.A.; Paiva, S.; Tripathi, G.; Feroz, N. Enabling technologies and sustainable smart cities. Sustain. Cities Soc.
2020
,
61, 102301, doi:10.1016/j.scs.2020.102301.
10.
Moustaka, V.; Theodosiou, Z.; Vakali, A.; Kounoudes, A.; Anthopoulos, L.G. Enhancing social networking in smart cities: Privacy
and security borderlines. Technol. Forecast. Soc. Chang. 2019,142, 285–300, doi:10.1016/j.techfore.2018.10.026.
11.
Braun, T.; Fung, B.C.; Iqbal, F.; Shah, B. Security and privacy challenges in smart cities. Sustain. Cities Soc.
2018
,39, 499–507,
doi:10.1016/j.scs.2018.02.039.
12. Kadry, S. Safe drive in smart city. Smart Solut. Future Cities 2016, 1–7, doi:10.1109/SSFC.2016.7447879.
13.
Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Group, T.P. Preferred Reporting Items for Systematic Reviews and Meta-Analyses:
The PRISMA Statement. PLoS Med. 2009,6, 1–6, doi:10.1371/journal.pmed.1000097.
14.
Yang, L.; Yan, H.; Lam, J.C. Thermal comfort and building energy consumption implications—A review. Appl. Energy
2014
,
115, 164–173, doi:10.1016/j.apenergy.2013.10.062.
15.
Gunduz, M.Z.; Das, R. Cyber-security on smart grid: Threats and potential solutions. Comput. Netw.
2020
,169, 107094,
doi:10.1016/j.comnet.2019.107094.
16.
Zhao, H.X.; Magoulès, F. A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev.
2012
,
16, 3586–3592, doi:10.1016/j.rser.2012.02.049.
17.
Carli, R.; Cavone, G.; Othman, S.B.; Dotoli, M. IoT based architecture for model predictive control of HVAC systems in smart
buildings. Sensors 2020,20, 781, doi:10.3390/s20030781.
18.
Hancke, G.P.; de Silva, B.d.C.; Hancke, G.P. The role of advanced sensing in smart cities. Sensors
2013
,13, 393–425,
doi:10.3390/s130100393.
Appl. Sci. 2021,11, 8198 23 of 29
19.
Benevolo, C.; Dameri, R.P.; Auria, B.D. Smart Mobility in Smart City. In Empowering Organizations: Enabling Platforms and Artefacts;
Springer: Cham, Switzerland, 2016; Volume 11, pp. 13–28, doi:10.1007/978-3-319-23784-8.
20.
Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things J.
2014
,1, 22–32,
doi:10.1109/JIOT.2014.2306328.
21.
Capdevila, I.; Zarlenga, M.I. Smart City or Smart Citizens? The Barcelona Case J. Strategy Manag.
2015
,8, 266–282,
doi:10.2139/ssrn.2585682.
22.
Sovacool, B.K.; Griffiths, S. Culture and low-carbon energy transitions. Nat. Sustain.
2020
,3, 685–693, doi:10.1038/s41893-020-
0519-4.
23.
Yu, D.; Yin, J.; Wilby, R.; Lane, S. Disruption of emergency response to vulnerable populations during floods. Nat. Sustain.
2020
,
3, 728–736, doi:10.1038/s41893-020-0516-7.
24.
Ejaz, W.; Anpalagan, A. Internet of Things for Smart Cities: Technologies, Big Data and Security; Springer: Cham, Switzerland, 2019;
pp. 1–15.
25.
Okai, E.; Feng, X.; Sant, P. Smart Cities Survey. In Proceedings of the 2018 IEEE 20th International Conference on
High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th Inter-
national Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK, 28–30 June 2018; pp. 1726–1730,
doi:10.1109/HPCC/SmartCity/DSS.2018.00282.
26.
Sanchez, L.; Muñoz, L.; Galache, J.A.; Sotres, P.; Santana, J.R.; Gutierrez, V.; Ramdhany, R.; Gluhak, A.; Krco, S.; Theodor-
idis, E.; Pfisterer, D. SmartSantander: IoT experimentation over a smart city testbed. Comput. Netw.
2014
,61, 217–238,
doi:10.1016/j.bjp.2013.12.020.
27.
Anthopoulos, L. Smart utopia VS smart reality: Learning by experience from 10 smart city cases. Cities
2017
,63, 128–148,
doi:10.1016/j.cities.2016.10.005.
28.
Lu, D.; Tian, Y.; Liu, V.Y.; Zhang, Y. The performance of the smart cities in China-A comparative study by means of self-organizing
maps and social networks analysis. Sustainability 2015,7, 7604–7621, doi:10.3390/su7067604.
29.
Joo, Y.M.; Tan, T.B. Smart cities in Asia: An introduction. In Smart Cities in Asia: Governing Development in the Era of Hyper-
Connectivity ; Edward Elgar Publishing: Cheltenham, UK, 2020; pp. 1–17, doi:10.4337/9781788972888.00007.
30.
Useche, M.P.; Carlos, N.S.J.; Vilafañe, C. Medellin (Colombia): A Case of Smart City. In Proceedings of the 7th International
Conference on Theory and Practice of Electronic Governance ICEGOV ’13, Seoul, Korea, 22–25 October 2013; pp. 231–233,
doi:10.4324/9781351124225-4.
31.
Gaffney, C.; Robertson, C. Smarter than Smart: Rio de Janeiro’s Flawed Emergence as a Smart City. J. Urban Technol.
2018
,
25, 47–64, doi:10.1080/10630732.2015.1102423.
32.
Yigitcanlar, T.; Kamruzzaman, M. Smart Cities and Mobility: Does the Smartness of Australian Cities Lead to Sustainable
Commuting Patterns? J. Urban Technol. 2019,26, 21–46, doi:10.1080/10630732.2018.1476794.
33.
Niculescu, A.I.; Wadhwa, B. Smart cities in south east Asia: Singapore concepts-An HCI4D perspective. In Proceedings of the
ASEAN CHI Symposium’15, Seoul, Korea, 18–23 April 2015; pp. 20–23, doi:10.1145/2776888.2780362.
34.
Endesa, F. Smart Cities. Available online: https://www.fundacionendesa.org/es/recursos/a201908-smart-city (accessed 13
November 2020).
35. Cities, B. Bright Cities. Available online: https://www.brightcities.city/ (accessed 13 November 2020).
36.
IMD Real Learning Real Impact; SCO Observatory. Smart City Index 2020. Available online: https://www.imd.org/smart-city-
observatory/smart-city-index/ (accessed 13 November 2020).
37.
Wu, L.; Zhi, Y.; Sui, Z.; Liu, Y. Intra-urban human mobility and activity transition: Evidence from social media check-in data.
PLoS ONE 2014,9, e97010, doi:10.1371/journal.pone.0097010.
38.
Chen, S.; Hu, J.; Shi, Y.; Peng, Y.; Fang, J.; Zhao, R.; Zhao, L. Vehicle-to-Everything (v2x) Services Supported by LTE-Based
Systems and 5G. IEEE Commun. Stand. Mag. 2017,1, 70–76, doi:10.1109/MCOMSTD.2017.1700015.
39.
Chang, C.Y.; Chien, L.C.; Chang, Y.H.; Kuo, E.C.; Hwang, Y.S. A smart public security strategy: The New Taipei City Technology
defense plan. Procedia Comput. Sci. 2019,159, 1715–1719, doi:10.1016/j.procs.2019.09.342.
40.
Baig, Z.A.; Szewczyk, P.; Valli, C.; Rabadia, P.; Hannay, P.; Chernyshev, M.; Johnstone, M.; Kerai, P.; Ibrahim, A.; Sansurooah,
K.; Syed, N.; Peacock, M. Future challenges for smart cities: Cyber-security and digital forensics. Digit. Investig.
2017
,22, 3–13,
doi:10.1016/j.diin.2017.06.015.
41.
Anagnostopoulos, T.; Zaslavsky, A.; Kolomvatsos, K.; Medvedev, A.; Amirian, P.; Morley, J.; Hadjieftymiades, S. Challenges
and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey. IEEE Trans. Sustain. Comput.
2017
,2, 275–289,
doi:10.1109/TSUSC.2017.2691049.
42.
Aktemur, I.; Erensoy, K.; Kocyigit, E. Optimization of Waste Collection in Smart Cities with the use of Evolutionary Algorithms.
In Proceedings of the 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications
HORA 2020, Ankara, Turkey, 26–27 June 2020; pp. 1–8, doi:10.1109/HORA49412.2020.9152865.
43.
Pardini, K.; Rodrigues, J.J.; Diallo, O.; Das, A.K.; de Albuquerque, V.H.C.; Kozlov, S.A. A smart waste management solution
geared towards citizens. Sensors 2020,20, 2380, doi:10.3390/s20082380.
44.
Mattos, W.; R. L. Gondim, P. M-Health Solutions Using 5G Networks and M2M Communications. IT Prof.
2016
,18, 24–29,
doi:10.1109/MITP.2016.52.
Appl. Sci. 2021,11, 8198 24 of 29
45.
Mustapa, M.A.; Abu Bakar, M.H.; Mustapha Kamil, Y.; Syahir, A.; Mahdi, M.A. Bio-Functionalized Tapered Multimode Fiber
Coated With Dengue Virus NS1 Glycoprotein for Label Free Detection of Anti-Dengue Virus NS1 IgG Antibody. IEEE Sens. J.
2018,18, 4066–4072.
46.
Zheng, Y.; Rundell, A. Biosensor immunosurface engineering inspired by B-cell membrane-bound antibodies: Modeling and
analysis of multivalent antigen capture by immobilized antibodies. IEEE Trans. NanoBiosci. 2003,2, 14–25.
47.
Sekitani, T.; Someya, T. Stretchable organic integrated circuits for large-area electronic skin surfaces. MRS Bull.
2012
,37, 236–245,
doi:10.1557/mrs.2012.42.
48.
Kim, Y.S.; Lee, J.; Ameen, A.; Shi, L.; Li, M.; Wang, S.; Ma, R.; Jin, S.H.; Kang, Z.; Huang, Y.; Rogers, J. Multifunctional Epidermal
Electronics Printed Directly Onto the Skin. Adv. Mater. 2013,25, 2773–2778, doi:10.1002/adma.201204426.
49.
Ko, H.; Stoykovich, M.; Song, J.; Malyarchuk, V.; Choi, W.; Yu, C.J.; Geddes, J.; Xiao, J.; Wang, S.; Huang, Y.; et al. A hemispherical
electronic eye camera based on compressible silicon optoelectronics. Nature 2008,454, 748–753, doi:10.1038/nature07113.
50.
Wang, Y.; Yang, R.; Shi, Z.; Zhang, L.; Shi, D.X.; Wang, E.; Zhang, G. Super-Elastic Graphene Ripples for Flexible Strain Sensors.
ACS Nano 2011,5, 3645–3650, doi:10.1021/nn103523t.
51.
Pan, L.J.; Chortos, A.; Yu, G.; Wang, Y.; Isaacson, S.; Allen, R.; Shi, Y.; Dauskardt, R.; Bao, Z. An ultra-sensitive resistive pressure
sensor based on hollow-sphere microstructure induced elasticity in conducting polymer film. Nat. Commun.
2014
,5, 3002,
doi:10.1038/ncomms4002.
52.
Chen, Y.; Lu, B.; Chen, Y.; Feng, X. Biocompatible and ultra-flexible inorganic strain sensors attached to skin for long-term vital
signs monitoring. IEEE Electron. Device Lett. 2016,37, 496–499, doi:10.1109/LED.2016.2536036.
53.
Zhang, Y.; Black, A.; Wu, N.; Cui, Y. Comparison of “Dry Sensing” and “Wet Sensing” of a Protein With a Graphene Sensor. IEEE
Sens. Lett. 2018,2, 1–4, doi:10.1109/lsens.2018.2883146.
54.
Yazdi, E.; Willig, A.; Pawlikowski, K. On channel adaptation in IEEE 802.15.4 mobile body sensor networks: What can be Gained?
In Proceedings of the IEEE International Conference on Networks, ICON, Singapore, Singapore, 12–14 December 2012; pp.
262–267, doi:10.1109/ICON.2012.6506567.
55.
Liao, Y.; Leeson, M.S.; Higgins, M.D. Flexible quality of service model for wireless body area sensor networks. Healthc. Technol.
Lett. 2016,3, 12–15, doi:10.1049/htl.2015.0049.
56.
Al-Turjman, F.; Lemayian, J.P. Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities:
An overview. Comput. Electr. Eng. 2020,87, 106776, doi:10.1016/j.compeleceng.2020.106776.
57.
Fanti, M.P.; Mangini, A.M.; Rotunno, G.; Fiume, G.; Favenza, A.; Gaetani, M. A Cloud Computing Architecture for Eco Route
Planning of Heavy Duty Vehicles. In Proceedings of the 2018 IEEE 14th International Conference on Automation Science and
Engineering (CASE), Munich, Bavaria, Germany, 20–24 August 2018; pp. 730–735, doi:10.1109/COASE.2018.8560498.
58.
Difilippo, G.; Fanti, M.P.; Fiume, G.; Mangini, A.M.; Monsel, N. A Cloud Optimizer for Eco Route Planning of Heavy Duty
Vehicles. In Proceedings of the 2018 IEEE Conference on Decision and Control (CDC), Miami, FL, USA, 17–19 December 2018; pp.
7142–7147, doi:10.1109/CDC.2018.8619149.
59.
Koehl, A. Urban transport and COVID-19: Challenges and prospects in low- and middle-income countries. Cities Health
2020
, 1–6,
doi:10.1080/23748834.2020.1791410.
60.
Hanbyul, S.; Ki-Dong, L.; Shinpei, Y.; Ying, P.; Philippe, S. LTE Evolution for Vehicle-to-Everything Services IEEE Commun. Mag.
2016.54, 22–28.
61. Wang, J.; Shao, Y.; Ge, Y.; Yu, R. A survey of vehicle to everything (V2X) testing. Sensors 2019,19, 334, doi:10.3390/s19020334.
62.
Toglaw, S.; Aloqaily, M.; Alkheir, A.A. Connected, Autonomous and Electric Vehicles: The Optimum Value for a Successful
Business Model. In Proceedings of the 2018 Fifth International Conference on Internet of Things: Systems, Management and
Security, Valencia, Spain, 15–18 October 2018; pp. 303–308, doi:10.1109/IoTSMS.2018.8554391.
63.
Ozatay, E.; Onori, S.; Wollaeger, J.; Ozguner, U.; Rizzoni, G.; Filev, D.; Michelini, J.; Di Cairano, S. Cloud-Based Velocity
Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution. IEEE Trans. Intell. Transp. Syst.
2014
,
15, 2491–2505, doi:10.1109/TITS.2014.2319812.
64.
Fanti, M.P.; Mangini, A.M.; Favenza, A.; Difilippo, G. An Eco-Route planner for heavy duty vehicles. IEEE/CAA J. Autom. Sin.
2021,8, 37–51, doi:10.1109/JAS.2020.1003456.
65.
Zhang, J.; Lu, Y.; Lu, Z.; Liu, C.; Sun, G.; Li, Z. A new smart traffic monitoring method using embedded cement-based piezoelectric
sensors. Smart Mater. Struct. 2015,24, 1–8, doi:10.1088/0964-1726/24/2/025023.
66.
Hussein, A.; García, F.; Armingol, J.M.; Olaverri-Monreal, C. P2V and V2P communication for pedestrian warning on the basis of
autonomous vehicles. In Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Rio de
Janeiro, Brazil, 1–4 November 2016; pp. 2034–2039, doi:10.1109/ITSC.2016.7795885.
67.
Ghazal, B.; Elkhatib, K.; Chahine, K.; Kherfan, M. Smart traffic light control system. In Proceedings of the 3rd International
Conference on Electrical, Electronics, Computer Engineering and their Applications, EECEA 2016, Beirut, Lebanon, 21–23 April
2016; pp. 140–145, doi:10.1109/EECEA.2016.7470780.
68.
Ahas, R.; Aasa, A.; Yuan, Y.; Raubal, M.; Smoreda, Z.; Liu, Y.; Ziemlicki, C.; Tiru, M.; Zook, M. Everyday space–time geographies:
Using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. Int. J. Geogr. Inf. Sci.
2015
,
29, 2017–2039, doi:10.1080/13658816.2015.1063151.
Appl. Sci. 2021,11, 8198 25 of 29
69.
Cuenca-Jara, J.; Terroso-Saenz, F.; Valdes-Vela, M.; Gonzalez-Vidal, A.; Skarmeta, A.F. Human mobility analysis based on social
media and fuzzy clustering. In Proceedings of the Global Internet of Things Summit GIoTS 2017, Geneva, Switzerland, 1–6 June
2017; pp. 1–6, doi:10.1109/GIOTS.2017.8016266.
70.
Fukuzaki, Y.; Murao, K.; Mochizuki, M.; Nishio, N. Statistical analysis of actual number of pedestrians for Wi-Fi packet-based
pedestrian flow sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015, Osaka,
Japan, 7–11 September 2015; pp. 1519–1526, doi:10.1145/2800835.2801623.
71.
El Mahrsi, M.K.; Come, E.; Oukhellou, L.; Verleysen, M. Clustering Smart Card Data for Urban Mobility Analysis. IEEE Trans.
Intell. Transp. Syst. 2017,18, 712–728, doi:10.1109/TITS.2016.2600515.
72.
Vlahogianni, E.I.; Kepaptsoglou, K.; Tsetsos, V.; Karlaftis, M.G. A Real-Time Parking Prediction System for Smart Cities. J. Intell.
Transp. Syst. Technol. Plan. Oper. 2016,20, 192–204, doi:10.1080/15472450.2015.1037955.
73.
Zhao, J.; Xu, H.; Liu, H.; Wu, J.; Zheng, Y.; Wu, D. Detection and tracking of pedestrians and vehicles using roadside LiDAR
sensors. Transp. Res. Part C Emerg. Technol. 2019,100, 68–87, doi:10.1016/j.trc.2019.01.007.
74.
Walter, M.; Eilebrecht, B.; Wartzek, T.; Leonhardt, S. The smart car seat: Personalized monitoring of vital signs in automotive
applications. Pers. Ubiquitous Comput. 2011,15, 707–715, doi:10.1007/s00779-010-0350-4.
75.
Rahimian, P.; O’Neal, E.E.; Zhou, S.; Plumert, J.M.; Kearney, J.K. Harnessing Vehicle-to-Pedestrian (V2P) Communication
Technology: Sending Traffic Warnings to Texting Pedestrians. Hum. Factors 2018,60, 833–843, doi:10.1177/0018720818781365.
76.
Roccotelli, M.; Nolich, M.; Fanti, M.P.; Ukovich, W. Internet of things and virtual sensors for electromobility. Internet Technol. Lett.
2018,1, e39, doi:10.1002/itl2.39.
77.
Fanti, M.P.; Mangini, A.M.; Roccotelli, M.; Nolich, M.; Ukovich, W. Modeling Virtual Sensors for Electric Vehicles Charge Services.
In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 7–10
October 2018; pp. 3853–3858, doi:10.1109/SMC.2018.00653.
78.
Fanti, M.P.; Mangini, A.M.; Roccotelli, M. An Innovative Service for Electric Vehicle Energy Demand Prediction. In Proceedings
of the 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), Prague, Czech Republic, 19
June–2 July 2020; Volume 1, pp. 880–885, doi:10.1109/CoDIT49905.2020.9263804.
79.
Human Security Unit of the United Nation. Human Security Handbook an Integrated Approach for the Realization of the SDG’s; United
Nations: New York, NY, USA, 2016; pp. 1–47.
80. Maye, D. ’smart food city’: Conceptual relations between smart city planning, urban food systems and innovation theory. City
Cult. Soc. 2019,16, 18–24, doi:10.1016/j.ccs.2017.12.001.
81.
Alipio, M.I.; Dela Cruz, A.E.M.; Doria, J.D.A.; Fruto, R.M.S. On the design of Nutrient Film Technique hydroponics farm for
smart agriculture. Eng. Agric. Environ. Food 2019,12, 315–324, doi:10.1016/j.eaef.2019.02.008.
82.
Matindoust, S.; Baghaei-Nejad, M.; Abadi, M.H.S.; Zou, Z.; Zheng, L.R. Food quality and safety monitoring using gas sensor
array in intelligent packaging. Sens. Rev. 2016,36, 169–183, doi:10.1108/SR-07-2015-0115.
83.
Lu, L.; Zhu, Z.; Hu, X. Hybrid nanocomposites modified on sensors and biosensors for the analysis of food functionality and
safety. Trends Food Sci. Technol. 2019,90, 100–110, doi:10.1016/j.tifs.2019.06.009.
84.
Zhou, J.; Cao, Z.; Dong, X.; Vasilakos, A.V. Security and Privacy for Cloud-Based IoT: Challenges. IEEE Commun. Mag.
2017
,
55, 26–33, doi:10.1109/MCOM.2017.1600363CM.
85.
Habibzadeh, H.; Soyata, T.; Kantarci, B.; Boukerche, A.; Kaptan, C. Sensing, communication and security planes: A new challenge
for a smart city system design. Comput. Netw. 2018, doi:10.1016/j.comnet.2018.08.001.
86.
Ray, P.P.; Dash, D.; Kumar, N. Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and
future directions. Comput. Commun. 2020,160, 111–131, doi:10.1016/j.comcom.2020.05.029.
87.
Keshta, I.; Odeh, A. Security and privacy of electronic health records: Concerns and challenges. Egypt. Inform. J.
2020
,
doi:10.1016/j.eij.2020.07.003.
88.
Kaku, K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in
Sentinel Asia. Int. J. Disaster Risk Reduct. 2019,33, 417–432, doi:10.1016/j.ijdrr.2018.09.015.
89.
Clark, N.E. Towards a standard licensing scheme for the access and use of satellite earth observation data for disaster management.
Acta Astronaut. 2017,139, 325–331, doi:10.1016/j.actaastro.2017.07.007.
90.
Liu, L.; fan Li, C.; kun Sun, X.; Zhao, J. Event alert and detection in smart cities using anomaly information from remote sensing
earthquake data. Comput. Commun. 2020,153, 397–405, doi:10.1016/j.comcom.2020.02.023.
91.
Miyata, E.; Miyata, H.; Fukasawa, E.; Kakizaki, K.; Abe, H.; Katsumata, M.; Sato, M. Nuclear Inst . and Methods in Physics
Research , A Hybrid semiconductor radiation detectors using conductive polymers Nucl. Instruments Methods Phys. Res. Sect.
Accel. Spectrometers. Detect. Assoc. Equip. 2020.955, 1–7.
92.
Tanioka, Y.; Gusman, A.R. Near-field tsunami inundation forecast method assimilating ocean bottom pressure data: A synthetic
test for the 2011 Tohoku-oki tsunami. Phys. Earth Planet. Inter. 2018,283, 82–91, doi:10.1016/j.pepi.2018.08.006.
93.
Henríquez, B.P. Information Technology: The Unsung Hero of Market-Based Environmental Policies. 2004. Available online:
https://gspp.berkeley.edu/assets/uploads/research/pdf/RFF_Resources_152_infotech.pdf (accessed 1 September 2021).
94.
de Chalendar, J.A.; Taggart, J.; Benson, S.M. Tracking emissions in the US electricity system. Proc. Natl. Acad. Sci. USA
2019
,
116, 25497–25502, doi:10.1073/pnas.1912950116.
Appl. Sci. 2021,11, 8198 26 of 29
95.
Badii, C.; Bilotta, S.; Cenni, D.; Difino, A.; Nesi, P.; Paoli, I.; Paolucci, M. High Density Real-Time Air Quality Derived Services
from IoT Networks. Sensors 2020,20, 5435, doi:10.3390/s20185435.
96.
Henríquez, B.P. Environmental Commodities Markets and Emissions Trading: Towards a Low-Carbon Future; Routledge: London, UK,
2012.
97.
Pérez Henríquez, B. Handbook on the Resource Nexus; Chapter California Innovations @ the Water Energy Nexus (WEN); Routledge:
London, UK, 2018; p. 18.
98.
Yan, K.; Zhang, Y.; Yan, Y.; Xu, C.; Zhang, S. Fault diagnosis method of sensors in building structural health monitoring system
based on communication load optimization. Comput. Commun. 2020,159, 310–316, doi:10.1016/j.comcom.2020.05.026.
99.
Ayyildiz, C.; Erdem, H.E.; Dirikgil, T.; Dugenci, O.; Kocak, T.; Altun, F.; Gungor, V.C. Structure Health Monitoring
Using Wireless Sensor Networks on Structural Elements. Ad Hoc Netw.
2019
,82, 68–76; Erratum to
2020
,105, 68–76,
doi:10.1016/j.adhoc.2019.102057.
100.
Smart street lighting system: A platform for innovative smart city applications and a new frontier for cyber-security. Electr. J.
2016,29, 28–35, doi:10.1016/j.tej.2016.11.011.
101.
Laufs, J.; Borrion, H.; Bradford, B. Security and the smart city: A systematic review. Sustain. Cities Soc.
2020
,55, 102023,
doi:10.1016/j.scs.2020.102023.
102.
Ross, A.; Banerjee, S.; Chowdhury, A. Security in smart cities: A brief review of digital forensic schemes for biometric data.
Pattern Recognit. Lett. 2020,138, 346–354, doi:10.1016/j.patrec.2020.07.009.
103.
Mezzera, L.; Carminati, M.; Di Mauro, M.; Turolla, A.; Tizzoni, M.; Antonelli, M. A 7-Parameter Platform for Smart and Wireless
Networks Monitoring On-Line Water Quality. In Proceedings of the 25th IEEE International Conference on Electronics Circuits
and Systems, ICECS 2018, Bordeaux, Gironde, France, 9–12 December 2019; pp. 709–712, doi:10.1109/ICECS.2018.8618014.
104.
Gonçalves, R.; Soares, J.J.; Lima, R.M. An IoT-based framework for smartwater supply systems management. Future Internet
2020,12, 114, doi:10.3390/FI12070114.
105.
Mohd Ismail, M.I.; Dziyauddin, R.A.; Salleh, N.A.A.; Muhammad-Sukki, F.; Bani, N.A.; Izhar, M.A.M.; Latiff, L.A. A review
of vibration detection methods using accelerometer sensors for water pipeline leakage. IEEE Access
2019
,7, 51965–51981,
doi:10.1109/ACCESS.2019.2896302.
106.
Kodali, R.K.; Sarjerao, B.S. A low cost smart irrigation system using MQTT protocol. In Proceedings of the IEEE In-
ternational Symposium on Technologies for Smart Cities TENSYMP 2017, Kochi, Kerala, India, 14–16 July 2017; pp. 1–5,
doi:10.1109/TENCONSpring.2017.8070095.
107.
Mamun, K.A.; Islam, F.R.; Haque, R.; Khan, M.G.; Prasad, A.N.; Haqva, H.; Mudliar, R.R.; Mani, F.S. Smart Water Quality Moni-
toring System Design and KPIs Analysis: Case Sites of Fiji Surface Water. Sustainability 2019,11, 7110, doi:10.3390/su11247110.
108. Quadar, N.; Chehri, A.; Jeon, G.; Ahmad, A. Smart water distribution system based on IoT networks, a critical review. In Smart
Innovation, Systems and Technologies; Springer: Singapore, 2021; Volume 189, pp. 293–303, doi:10.1007/978-981-15-5784-2_24.
109.
Kulkarni, P.; Farnham, T. Smart City Wireless Connectivity Considerations and Cost Analysis: Lessons Learnt from Smart Water
Case Studies. IEEE Access 2016,4, 660–672, doi:10.1109/ACCESS.2016.2525041.
110.
d. Oliveira, K.V.; Esgalha Castelli, H.M.; José Montebeller, S.; Prado Avancini, T.G. Wireless Sensor Network for Smart Agriculture
using ZigBee Protocol. In Proceedings of the 2017 IEEE First Summer School on Smart Cities (S3C), Natal, Brazil, 6–11 August
2017; pp. 61–66, doi:10.1109/S3C.2017.8501379.
111.
Lopes, S.F.; Pereira, R.M.; Lopes, S.O.; Coutinho, M.; Malheiro, A.; Fonte, V. Yet a smarter irrigation system. In Lecture Notes of the
Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Springer Cham: Cham, Switzerland,
2020; Volume 323, pp. 337–346, doi:10.1007/978-3-030-51005-3_28.
112.
Martínez, R.; Vela, N.; el Aatik, A.; Murray, E.; Roche, P.; Navarro, J.M. On the use of an IoT integrated system for water quality
monitoring and management in wastewater treatment plants. Water 2020,12, 1096, doi:10.3390/W12041096.
113.
Simitha, K.M.; Subodh Raj, M.S. IoT and WSN Based Water Quality Monitoring System. In Proceedings of the 3rd International
Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, Coimbatore, Tamil Nadu, India, 12–14
June 2019; pp. 205–210, doi:10.1109/ICECA.2019.8821859.
114.
Pasika, S.; Gandla, S.T. Smart water quality monitoring system with cost-effective using IoT. Heliyon
2020
,6, e04096,
doi:10.1016/j.heliyon.2020.e04096.
115.
Yang, W.; Wei, X.; Choi, S. A Dual-Channel, Interference-Free, Bacteria-Based Biosensor for Highly Sensitive Water Quality
Monitoring. IEEE Sens. J. 2016,16, 8672–8677, doi:10.1109/JSEN.2016.2570423.
116.
Chen, Y.; Han, D. Water quality monitoring in smart city: A pilot project. Autom. Constr.
2018
,89, 307–316,
doi:10.1016/j.autcon.2018.02.008.
117.
Esmaeilian, B.; Wang, B.; Lewis, K.; Duarte, F.; Ratti, C.; Behdad, S. The future of waste management in smart and sustainable
cities: A review and concept paper. Waste Manag. 2018,81, 177–195, doi:10.1016/j.wasman.2018.09.047.
118.
Folianto, F.; Low, Y.S.; Yeow, W.L. Smartbin: Smart waste management system. In Proceedings of the 2015 IEEE 10th International
Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015, Singapore, 7–9 April 2015; pp. 1–2,
doi:10.1109/ISSNIP.2015.7106974.
119.
Medvedev, A.; Fedchenkov, P.; Zaslavsky, A.; Anagnostopoulos, T.; Khoruzhnikov, S. Waste Management as an IoT-Enabled
Service in Smart Cities. In Proceedings of the 15th International Conference, NEW2AN 2015, and 8th Conference, ruSMART 2015,
St. Petesburg, Russia, 26–28 August 2015; pp. 104–115, doi:10.1007/978-3-319-23126-6_10.
Appl. Sci. 2021,11, 8198 27 of 29
120.
Bharadwaj, a.S.; Rego, R.; Chowdhury, A. IoT Based Solid Waste Management System. In Proceedings of the 2016 IEEE Annual
India Conference (INDICON), Bangalore, Karnataka, India, 16–18 December 2016; pp. 1–6. doi: 10.1109/INDICON.2016.7839147.
121.
Abdullah, N.; Alwesabi, O.A.; Abdullah, R. IoT-based smart waste management system in a smart city. In Advances in Intelligent
Systems and Computing; Springer International Publishing: Cham, Switzerland, 2019; Volume 843, pp. 364–371, doi:10.1007/978-3-
319-99007-1_35.
122.
Aiswatha, J.; Pankajakshan, A.; Nair, A.M.; Taha, A.B. Garbage monitoring robot. Proc. AIP Conf.
2020
,2222, 040019,
doi:10.1063/5.0004294.
123.
Lozano, Á.; Caridad, J.; De Paz, J.F.; González, G.V.; Bajo, J. Smart waste collection system with low consumption LoRaWAN
nodes and route optimization. Sensors 2018,18, 1465, doi:10.3390/s18051465.
124.
Jagtap, S.; Gandhi, A.; Bochare, R.; Patil, A.; Shitole, A. Waste Management Improvement in Cities using IoT. In Proceedings of
the 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control, PARC 2020,
Mathura, Uttar Pradesh, India, 28–29 February 2020; pp. 382–385, doi:10.1109/PARC49193.2020.236631.
125.
Kang, K.D.; Kang, H.; Ilankoon, I.M.; Chong, C.Y. Electronic waste collection systems using Internet of Things (IoT): Household
electronic waste management in Malaysia. J. Clean. Prod. 2020,252, 119801, doi:10.1016/j.jclepro.2019.119801.
126.
Tripathi, D.K.; Dubey, S.; Agrawal, S.K. Survey on IOT based smart waste bin. In Proceedings of the 2020 IEEE 9th International
Conference on Communication Systems and Network Technologies, CSNT 2020, Gwalior, India, 10–12 April 2020; pp. 140–144,
doi:10.1109/CSNT48778.2020.9115793.
127.
Memon, S.K.; Shaikh, F.K.; Mahoto, N.A.; Memon, A.A. IoT based smart garbage monitoring & collection system using WeMos
& Ultrasonic sensors . In Proceedings of the 2nd International Conference on Computing, Mathematics and Engineering
Technologies (iCoMET), Sukkur, Pakistan, 30–31 January 2019; pp. 1–6.
128.
Raj, J.R.; Rajula, B.I.P.; Tamilbharathi, R.; Srinivasulu, S. AN IoT Based Waste Segreggator for Recycling Biodegradable and
Non-Biodegradable Waste. In Proceedings of the 6th International Conference on Advanced Computing and Communication
Systems, ICACCS 2020, Coimbatore, Tamil Nadu, India, 6–7 March 2020; pp. 928–930, doi:10.1109/ICACCS48705.2020.9074251.
129.
De Paz, J.F.; Bajo, J.; Rodríguez, S.; Villarrubia, G.; Corchado, J.M. Intelligent system for lighting control in smart cities. Inf. Sci.
2016,372, 241–255, doi:10.1016/j.ins.2016.08.045.
130.
International Energy Agency. CO
2
Emissions From Fuel Combustion—Overview 2017; IEA: Washington, DC, USA, 2017; ISBN
978-92-64-27819-6.
131.
Mogro, A.; Huertas, J. Assessment of the effect of using air conditioning on the vehicle’s real fuel consumption. Int. J. Interact.
Des. Manuf. 2021,15, 271-285.
132.
Quirama, L.F.; Giraldo, M.; Huertas, J.I.; Jaller, M. Driving cycles that reproduce driving patterns, energy consumptions and
tailpipe emissions. Transp. Res. Part D Transp. Environ. 2020,82, 102294.
133.
Giraldo, M.; Huertas, J.I. Real emissions, driving patterns and fuel consumption of in-use diesel buses operating at high altitude.
Transp. Res. Part D Transp. Environ. 2019,77, 21–36.
134.
Geng, Y.; Ji, W.; Wang, Z.; Lin, B.; Zhu, Y. A review of operating performance in green buildings: Energy use, indoor environmental
quality and occupant satisfaction. Energy Build. 2019,183, 500–514, doi:10.1016/j.enbuild.2018.11.017.
135.
Suryadevara, N.K.; Mukhopadhyay, S.C.; Kelly, S.D.T.; Gill, S.P.S. WSN-based smart sensors and actuator for power management
in intelligent buildings. IEEE/ASME Trans. Mechatronics 2015,20, 564–571, doi:10.1109/TMECH.2014.2301716.
136.
Ejaz, W.; Muhammad, N.; Shahid, A.; Jo, M. Efficient Energy Management for Internet of Things in Smart Cities. IEEE Commun.
Mag. 2016,55, 84–91, doi:10.1109/MCOM.2017.1600218CM.
137.
Ploennigs, J.; Ahmed, A.; Hensel, B.; Stack, P.; Menzel, K. Virtual sensors for estimation of energy consumption and thermal
comfort in buildings with underfloor heating. Adv. Eng. Inform. 2011,25, 688–698, doi:10.1016/j.aei.2011.07.004.
138.
Li, H.; Hong, T.; Lee, S.H.; Sofos, M. System-level key performance indicators for building performance evaluation. Energy Build.
2020,209, 109703, doi:10.1016/j.enbuild.2019.109703.
139.
Kumar, A.; Singh, A.; Kumar, A.; Singh, M.K.; Mahanta, P.; Mukhopadhyay, S.C. Sensing Technologies for Monitoring Intelligent
Buildings: A Review. IEEE Sens. J. 2018,18, 4847–4860, doi:10.1109/JSEN.2018.2829268.
140.
Akhter, F.; Khadivizand, S.; Siddiquei, H.R.; Alahi, M.E.E.; Mukhopadhyay, S. Iot enabled intelligent sensor node for smart city:
Pedestrian counting and ambient monitoring. Sensors 2019,19, 3374, doi:10.3390/s19153374.
141.
Jo, O.; Kim, Y.K.; Kim, J. Internet of Things for Smart Railway: Feasibility and Applications. IEEE Internet Things J.
2018
,
5, 482–490, doi:10.1109/JIOT.2017.2749401.
142.
Xiong, J.; Li, F.; Zhao, N.; Jiang, N. Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared
sensor network. Sensors 2014,14, 7209–7228, doi:10.3390/s140407209.
143.
International Energy Agency, I. International Energy Agency. Available online: https://www.iea.org/ (accessed 13 November
2020).
144.
Lau, S.P.; Merrett, G.V.; Weddell, A.S.; White, N.M. A traffic-aware street lighting scheme for Smart Cities using autonomous
networked sensors. Comput. Electr. Eng. 2015,45, 192–207, doi:10.1016/j.compeleceng.2015.06.011.
145.
Schaffers, H.; Komninos, N.; Pallot, M.; Trousse, B.; Nilsson, M.; Oliveira, A. Smart Cities and the Future Internet: Towards
Cooperation Frameworks for Open Innovation. Proc. Future Internet Conf.
2011
,6656, 431–446, doi:10.1007/978-3-642-20898-0_31.
146.
Higuera, J.; Hertog, W.; Perálvarez, M.; Polo, J.; Carreras, J. Smart lighting system ISO/IEC/IEEE 21451 compatible. IEEE Sens. J.
2015,15, 2595–2602, doi:10.1109/JSEN.2015.2390262.
Appl. Sci. 2021,11, 8198 28 of 29
147.
Kelly, S.D.T.; Suryadevara, N.K.; Mukhopadhyay, S.C. Towards the implementation of IoT for environmental condition monitoring
in homes. IEEE Sens. J. 2013,13, 3846–3853, doi:10.1109/JSEN.2013.2263379.
148.
Idwan, S.; Mahmood, I.; Zubairi, J.A.; Matar, I. Optimal Management of Solid Waste in Smart Cities using Internet of Things.
Wirel. Person. Commun. 2020,110, 485–501, doi:10.1007/s11277-019-06738-8.
149.
Huang, H.; Gong, T.; Ye, N.; Wang, R.; Dou, Y. Private and Secured Medical Data Transmission and Analysis for Wireless Sensing
Healthcare System. IEEE Trans. Ind. Inform. 2017,13, 1227–1237, doi:10.1109/TII.2017.2687618.
150.
Cao, B.; Ge, Y.; Kim, C.W.; Feng, G.; Tan, H.; Li, Y. An Experimental Study for Inter-User Interference Mitigation in Wireless Body
Sensor Networks. Sens. J. IEEE 2013,13, 3585–3595, doi:10.1109/JSEN.2013.2267053.
151.
Kim, B.; Choi, J.h.; Cho, J. A Hybrid Channel Access Scheme for Coexistence Mitigation in IEEE 802.15.4-based WBAN. IEEE
Sens. J. 2017,17, 7189–7195, doi:10.1109/JSEN.2017.2752286.
152.
Velusamy, B.; Pushpan, S.C. An Enhanced Channel Access Method to Mitigate the Effect of Interference among Body Sensor
Networks for Smart Healthcare. IEEE Sens. J. 2019,19, 7082–7088, doi:10.1109/JSEN.2019.2913002.
153.
Baltusis, P. On-board vehicle diagnostics. In Convergence Transportation Electronics Association; SAE International: Warrendale, PA,
USA, 2004; p. 15.
154.
Mario Gerla, Eun-Kyu Lee, Giovanni Pau, U.L. Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular
Clouds The Genesis of IOT. In Proceedings of the 2014 IEEE World Forum on Internet of Things (WF-IoT), Seoul, Korea, 6–8
March 2015; pp. 241–246.
155.
Gruwsved, A.; Söderback, I.; Fernholm, C. Evaluation of a vocational training programme in primary health care rehabilitation:
A case study. Work 1996,7, 47–61, doi:10.3233/WOR-1996-7107.
156.
Silva, A.S.M. Wearable Sensors Systems for Human Motion Analysis. Ph.D. Thesis, Universidade do Porto, Porto, Portugal, 2014.
157.
Camomilla, V.; Bergamini, E.; Fantozzi, S.; Vannozzi, G. In-Field Use of Wearable Magneto-Inertial Sensors for Sports Performance
Evaluation. In Proceedings of the 33rd International Conference on Biomechanics in Sports, Poitiers, Vienne, France, 29 June–3
July 2015; pp. 1–4.
158.
Dey, N.; Ashour, A.S.; Shi, F.; Fong, S.J.; Sherratt, R.S. Developing residential wireless sensor networks for ECG healthcare
monitoring. IEEE Trans. Consum. Electron. 2017,63, 442–449, doi:10.1109/TCE.2017.015063.
159.
Teaw, E.; Hou, G.; Gouzman, M.; Tang, K.W.; Kesluk, A.; Kane, M.; Farrell, J. A wireless health monitoring system. In Proceedings
of the 2005 IEEE International Conference on Information Acquisition, Hefei, China, 21–25 June 2005; p. 6.
160.
Park, J.; Cho, J.; Choi, J.; Nam, T. A zigbee network-based multi-channel heart rate monitoring system for exercising rehabilitation
patients. In Proceedings of the TENCON 2007—2007 IEEE Region 10 Conference, Taipei, Taiwan, 30 October–2 November 2007;
pp. 1–4, doi:10.1109/TENCON.2007.4428927.
161.
Becher, K.; Figueiredo, C.; Muhle, C.; Ruff, R.; Mendes, P.; Hoffmann, K.P. Design and Realization of a Wireless Sensor Gateway
for Health Monitoring. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology
Society, Buenos Aires, Argentina, 1–4 September 2010; pp. 374–377, doi:10.1109/IEMBS.2010.5627996.
162.
Wang, L.H.; Hsiao, Y.M.; Xie, X.Q.; Lee, S.Y. An outdoor intelligent healthcare monitoring device for the elderly. IEEE Trans.
Consum. Electron. 2016,62, 128–135, doi:10.1109/TCE.2016.7514671.
163.
Yan, H.; Huo, H.; Xu, Y.; Gidlund, M. Wireless sensor network based e-health system - implementation and experimental results.
IEEE Trans. Consum. Electron. 2010,56, 2288–2295.
164.
Zaldivar, J.; Calafate, C.T.; Cano, J.C.; Manzoni, P. Providing accident detection in vehicular networks through OBD-II devices
and Android-based smartphones. In Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks, Bonn, North
Rhine-Westphalia, Germany, 4–7 October 2011; pp. 813–819, doi:10.1109/LCN.2011.6115556.
165.
Fernandes, B.; Alam, M.; Gomes, V.; Ferreira, J.; Oliveira, A. Automatic accident detection with multi-modal alert system
implementation for ITS. Veh. Commun. 2016,3, 1–11, doi:10.1016/j.vehcom.2015.11.001.
166.
Sohail, A.M.; Khattak, K.S.; Iqbal, A.; Khan, Z.H.; Ahmad, A. Cloud-based detection of road bottlenecks using obd-ii telematics.
In Proceedings of the 22nd International Multitopic Conference, INMIC 2019, Islamabad, Pakistan, 29–30 November 2019; pp.
1–7, doi:10.1109/INMIC48123.2019.9022754.
167.
Rolim, C.; Baptista, P.; Duarte, G.; Farias, T.; Shiftan, Y. Quantification of the Impacts of Eco-driving Training and Real-time
Feedback on Urban Buses Driver’s Behaviour. Transp. Res. Procedia 2014,3, 70–79. doi:10.1016/j.trpro.2014.10.092.
168.
Chen, S.H.; Pan, J.S.; Lu, K. Driving behavior analysis based on vehicle OBD information and adaboost algorithms. In Proceedings
of the International Multiconference of Engineers and Computer Scientists, Hong Kong, China, 18–20 March 2015; Volume 1, pp.
102–106.
169.
von Landesberger, T.; Brodkorb, F.; Roskosch, P.; Andrienko, N.; Andrienko, G.; Kerren, A. MobilityGraphs: Visual Analysis
of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering. IEEE Trans. Vis. Comput. Graph.
2016
,22, 11–20,
doi:10.1109/TVCG.2015.2468111.
170.
Andrienko, N.; Andrienko, G.; Rinzivillo, S. Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics.
Inf. Syst. 2016,57, 172–194, doi:10.1016/j.is.2015.08.007.
171.
Andrienko, G.; Andrienko, N.; Bak, P.; Keim, D.; Wrobel, S. Visual Analytics of Movement; Springer: Berlin, Germany, 2013; Volume
1, pp. 1–387, doi:10.1007/978-3-642-37583-5.
Appl. Sci. 2021,11, 8198 29 of 29
172.
Jaikumar, K.; Brindha, T.; Deepalakshmi, T.K.; Gomathi, S. IOT Assisted MQTT for Segregation and Monitoring of Waste for
Smart Cities. In Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems
(ICACCS), Coimbatore, Tamil Nadu, India, 6–7 March 2020; pp. 887–891, doi:10.1109/ICACCS48705.2020.9074399.
173.
Rao, P.V.; Azeez, P.M.A.; Peri, S.S.; Kumar, V.; Devi, R.S.; Rengarajan, A.; Thenmozhi, K.; Praveenkumar, P. IoT based waste
management for smart cities. In Proceedings of the 2020 International Conference on Computer Communication and Informatics,
ICCCI 2020, Coimbatore, Tamil Nadu, India, 22–24 June 2020; pp. 20–24, doi:10.1109/ICCCI48352.2020.9104069.
174.
Cerchecci, M.; Luti, F.; Mecocci, A.; Parrino, S.; Peruzzi, G.; Pozzebon, A. A low power IoT sensor node architecture for waste
management within smart cities context. Sensors 2018,18, 1282, doi:10.3390/s18041282.
175.
Cook, D.J. Learning Setting-Generalized Activity Models for Smart Spaces. IEEE Intell. Syst.
2010
,2010, 1, doi:10.1109/MIS.2010.112.
176.
Albino, V.; Berardi, U.; Dangelico, R.M. Smart cities: Definitions, dimensions, performance, and initiatives. J. Urban Technol.
2015
,
22, 3–21, doi:10.1080/10630732.2014.942092.
177.
Ramírez-Moreno, M.A.; Díaz-Padilla, M.; Valenzuela-Gómez, K.D.; Vargas-Martínez, A.; Tudón-Martínez, J.C.; Morales-
Menendez, R.; Ramírez-Mendoza, R.A.; Pérez-Henríquez, B.L.; Lozoya-Santos, J.d.J. EEG-Based Tool for Prediction of University
Students’ Cognitive Performance in the Classroom. Brain Sci. 2021,11, 698, doi:10.3390/brainsci11060698.
178.
Ramirez, M.; TEC. PiBot, the Multifunctional Robot Developed at Tec. Available online: https://transferencia.tec.mx/en/2021/0
6/24/pibot-el-robot-multifuncional-desarrollado-en-el-tec/ (accessed 21 August 2021).
179.
Gaxiola-Beltrán, A.L.; Narezo-Balzaretti, J.; Ramírez-Moreno, M.A.; Pérez-Henríquez, B.L.; Ramírez-Mendoza, R.A.; Krajzewicz,
D.; Lozoya-Santos, J.d.J. Assessing Urban Accessibility in Monterrey, Mexico: A Transferable Approach to Evaluate Access to
Main Destinations at the Metropolitan and Local Levels. Appl. Sci. 2021,11, 7519, doi:10.3390/app11167519.
180.
Mahlknecht, J.; Alonso-Padilla, D.; Ramos, E.; Reyes, L.; Álvarez, M. The presence of SARS-CoV-2 RNA in different freshwater
environments in urban settings determined by RT-qPCR: Implications for water safety. Sci. Total Environ.
2021
,784, 147183,
doi:10.1016/j.scitotenv.2021.147183.
181. Colombia, S.C. Smart City Colombia. Available online: https://www.smartcitycolombia.org/ (accessed 21 August 2021).
182. SmarTech. SmarTech, Thinking of the Future. Available online: https://smartech.biz/indexE.html (accessed 21 August 2021).
183.
Lu, R.; Lin, X.; Shen, X. SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for Mobile-Healthcare
Emergency. Parallel Distrib. Syst. IEEE Trans. 2013,24, 614–624, doi:10.1109/TPDS.2012.146.
184.
Chan, A. Symmetric-Key Homomorphic Encryption for Encrypted Data Processing. In Proceedings of the 2009 IEEE International
Conference on Communications, Dresden, Saxony, Germany, 14–18 June 2009; pp. 1–5, doi:10.1109/ICC.2009.5199505.
185.
Ma, Y.; Wang, Y.; Yang, J.; Miao, Y.; Li, W. Big Health Application System based on Health Internet of Things and Big Data. IEEE
Access 2016,5, 7885–7897, doi:10.1109/ACCESS.2016.2638449.
186.
Saleh, N.; Kassem, A.; Haidar, A.M. Energy-efficient architecture for wireless sensor networks in healthcare applications. IEEE
Access 2018,6, 6478–6496, doi:10.1109/ACCESS.2018.2789918.
187.
Tang, S.; Shelden, D.; Eastman, C.; Pishdad-Bozorgi, P.; Gao, X. A review of building information modeling (BIM) and
the internet of things (IoT) devices integration: Present status and future trends. Autom. Constr.
2019
,101, 127–139,
doi:10.1016/j.autcon.2019.01.020.
188.
Jelicic, V.; Magno, M.; Brunelli, D.; Bilas, V.; Benini, L. Benefits of Wake-Up Radio in Energy-Efficient Multimodal Surveillance
Wireless Sensor Network. Sens. J. IEEE 2014,14, 3210–3220, doi:10.1109/JSEN.2014.2326799.
189.
Mekonnen, T.; Porambage, P.; Harjula, E.; Ylianttila, M. Energy Consumption Analysis of High Quality Multi-Tier Wireless
Multimedia Sensor Network. IEEE Access 2017,5, 15848–15858.
190.
Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities
2019
,89, 80–91, doi:10.1016/j.cities.2019.01.032.
191.
Davenport, T.H.; Ronanki, R. Artificial intelligence for the real world. Harv. Bus. Rev.
2018
Available online: https:
//hbr.org/webinar/2018/02/artificial-intelligence-for-the-real-world (accessed 1 September 2021).
192.
Pérez Henríquez, B. Transportation, Land Use, and Environmental Planning; Chapter Energy Sources for Sustainable Transportation
and Urban Development; Elsevier: Amsterdam, The Netherlands, 2019; pp. 281–298, doi:10.1016/B978-0-12-815167-9.00015-3.
