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.;
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.
Academic Editor: Juan-Carlos Cano
Received: 29 June 2021
Accepted: 31 August 2021
Published: 3 September 2021
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1School of Engineering and Sciences, Tecnologico de Monterrey, Eugenio Garza Sada 2501 Sur,
Monterrey 64849, Mexico; email@example.com (M.A.R.-M.); firstname.lastname@example.org (D.A.P.-R.);
email@example.com (E.R.-L.); firstname.lastname@example.org (A.E.M.); email@example.com (J.M.); firstname.lastname@example.org (J.I.H.);
email@example.com (R.A.R.-M.); firstname.lastname@example.org (J.d.J.L.-S.)
ESIME Unidad Ticoman, Instituto Politecnico Nacional, Mexico City 07340, Mexico; email@example.com
3School of Engineering and Sciences, Tecnologico de Monterrey, San Luis Potosi 78211, Mexico;
4Faculty of Economy, Universidad Autonoma de Nuevo Leon, San Nicolas de los Garza 66451, Mexico;
5School of Engineering, Macquarie University, Sydney, NSW 2109, Australia;
6Department of Electrical and Information Engineering, Politecnico di Bari, 70126 Bari, Italy;
firstname.lastname@example.org (A.M.M.); email@example.com (M.R.)
7Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA; firstname.lastname@example.org
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.
smart networks; digitization; smart transit; cyber security; smart city; smarter communi-
ties; smart sensors; smart meters; internet of things (IoT); facial recognition; cyber privacy
“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 [
]. This means that in the following decades, urban centers will face a
growing number of problems such as: (1) energy supply, (2) CO
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 .
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 [
]. 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 [
]. 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 [
]. The use of new
technologies such as artiﬁcial intelligence, where personal data play a critical role, are
facing increasing challenges [
]. 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 [
]. 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 .
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 , as well as these principles:
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;
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
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.
shows the percentage of revised papers under categories “Health”, “Secu-
rity”, “Mobility”, “Water”, “Waste”, “Energy”, and “Smart Cities”;
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:
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”;
Water: The selection of the articles for the survey was carried out using the
keywords “water” AND “sensors” AND “smart cities”;
Waste: The selection of the articles for the survey was carried out using the
keywords “waste” AND “sensors” AND “smart cities”;
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;
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”;
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
No. of references
No. of Citations
No. of references
1 2 3 4
No. of references
2000 2010 2020
No. of references
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.
Pie charts representing the distribution of the (
) Type of reference and (
) Topic of the selected references for
3.1. Smart City
The goals of smart cities initiatives are to develop economically, socially, and envi-
ronmentally sustainable cities [
]. Generally, the ideal model of a smart city is based
on the incorporation of the following subsystems and technologies: distributed energy
generation (micro-generation) [
], smart grids (interconnected and bidirectional smart
], smart metering (intelligent measurement of energy consumption data) [
smart buildings (eco-efﬁcient buildings with integrated energy production systems) [
smart sensors (intelligent sensors to collect data and keep the city connected) [
Appl. Sci. 2021,11, 8198 5 of 29
(implementation of electric vehicles) [
], information and communication technologies
(ICT)  and smart citizens (key piece of a smart city) .
Cities constitute a complex socio-technical system [
]. In order to design the
best solutions for cities, their inhabitants, social entities and governments need to be
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 [
]. 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 .
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 [
]. In Asia: Singapore,
Hong Kong, Shanghai, Beijing, Songdo, Seoul, and also smart cities in Taiwan, Indonesia,
Thailand, and India [
]. In North America: Toronto, Vancouver, New York,
Washington, and Seattle [
]. In South America: Medellin and Rio de Janeiro [
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 [
] and Hong Kong [
citizen security by trafﬁc monitoring in Rio de Janeiro [
], and natural disaster mon-
itoring in Singapore and Indonesia [
]; public service, smart government, and com-
munication transformation in cities of China (Wuhan, Shanghai, Beijing, Dalian, Tianjin,
Hangzhou, Wuxi, Shenzhen, Chengdu, and Guangzhou) [
]; adaptation of cultural spaces
in Medellin [
]; citizen engagement and data enhancement in New York and Wash-
]; deployment of experimental testbeds and living labs in Santander [
]; integration of local and foreign universities in Tampere [
] and Songdo [
deployment of smart green projects and policies in Seoul [
] and Toronto [
]; ﬁber optic
and smart grids in Geneva [
]; energy efﬁciency and innovation enhancement in Vi-
]; improved water consumption in Copenhagen, Hong Kong, and
deployment of electric charging stations in Malaga and Paris and Amsterdam [
]; Big data
integration and analysis in India and Thailand [
]; wired communities, pedestrian mobil-
ity, and mass transit solutions in Sydney, Brisbane, Adelaide, Melbourne, and Perth [
carbon emission reduction in Seattle [
]; smart waste collection systems in Helsinki [
], Barcelona [
]; smart parking in Milton Keynes [
]. 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
facilities Automated waste
collection Smart grids
ICT in health,
Health Lab Clean energy
Vienna Smart parking, car
sharing Energy efﬁciency
Bike lane network Water quality
monitoring Optimized waste
disposal Energy efﬁciency
Sensors in recycling
centers Smart metering
App for public
transport Smart Waste
Malaga Electric vehicles,
Remote healthcare Incident
Trafﬁc and public
management Smart Containers Centralized
Smart Parking, GPS
monitoring Smart park
irrigation Smart public
Paris eHealth, smart
medical records Bike sharing,
transportation Fiber-optic, smart
Siren alerts for
Trafﬁc maps, public
Apps for water
Apps for energy
Smart card IDs
for citizens Open, real-time
trafﬁc data Smart waste
smart cards for
system Smart buildings
Seoul Bus service based
on data analytics
system for law
India Smart transport
systems Clean energy,
Smart urban zone
Appl. Sci. 2021,11, 8198 7 of 29
Table 1. Cont.
City Health Security Mobility Water Waste Energy
DC Bike sharing, smart
Smart traﬁc lights Real-time
stairs and air
Perth Cyber-security and
ICTs in daily urban
N°of cities per Country
0 4 8 12 16
0 4 8 12 16
| | | |
North America Europe Africa
South America Asia Oceania
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.
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 .
Biosensors are fundamental when monitoring health, and different applications can
be identiﬁed in medical diagnoses [
], and antigen detection [
], among others. Inor-
ganic ﬂexible electronics have witnessed relevant results, including E-skin [
electronics (see Figure 4) [
], and eye cameras [
]. Some common materials used to
create these sensors are carbon-based or conductive organic polymers, which present poor
]. 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 .
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 [
]. 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-
]. 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 .
) Epidermal electronic system (EES) made of a skin replica created from the forearm,
before and; (
) after application of a spray-on-bandage; (
) Colourized microscopy image of the EES
with conductive gold ﬁlms of 100
m and; (
) its magniﬁed view. (
) Microscopy image of EES with
gold ﬁlms of 10 µm and; (f) its magniﬁed view .
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 [
]. 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 .
Due to repetitive starts and stops, fuel consumption and carbon emissions increase
during trafﬁc jams [
]. 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
]. 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 .
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 [
]. 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) [
], and vehicle–everything (V2X)
connections (see Figure 5) .
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 [
]. 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 [
], 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 [
]. Efforts in increasing pedestrian’s safety are
valuable contributions in improving urban mobility [
]. Regarding trafﬁc, conventional
trafﬁc light systems are deﬁned in a non-ﬂexible structure, such that light transitions have
deﬁned delays and onsets [
]. Dynamic changes in trafﬁc volume, congestions, accidents,
and pedestrian conﬂuence, should have been considered to provide an optimized trafﬁc
s movement and behavior in urban settings have been monitored mainly
using cell phones, by monitoring call detail records (CDRs) [
], social media check-
], MAC address reading [
], and smart cards detection in public transport [
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-
], and ferromagnetic sensors buried in the asphalt for smart-parking
Vision-based sensors, such as infrared (IR) [
] and light detection and ranging (LiDAR) [
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 [
]. Mobile phone apps have allowed P2V and V2P applications for collision
Appl. Sci. 2021,11, 8198 10 of 29
Two types of V2V connections: (
) from vehicles to vehicles with an intermediate transfer
connection and; (b) directly from vehicles to other vehicles .
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 [
]. 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 [
]. 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) [
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 [
]. 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
]. Several methods are proposed to monitor the quality and safety of the food
during production and distribution, including gas sensor array [
] for the analysis of
chemical reaction occurred in spoiled food. Hybrid nanocomposites and biosensors have
also been reported in food security context .
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 [
]. 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).
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) .
Health security: In-body inserted devices are designed to communicate with health-
centers and hospitals [
]. 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 [
]. 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 [
], radiation level detectors [
tsunami inundation forecast methods assimilating ocean bottom pressure data [
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 [
]. CEMS are allowing better
tracking of powerplant emissions in real time to inform decarbonization strategies for
the grid [
]. 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
]. 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 [
]. 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
]. 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 .
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 [
]. 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 [
]. Ref. [
] 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 .
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 [
]. 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 [
The principal usefulness of smart water systems lies in controlling valves and pumps
remotely  measuring quality , pressure, ﬂow, and consumption .
Consumption monitoring includes metering and model applications to describe con-
sumption patterns. Water loss management encompasses leakage detection and local-
]. 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
]. In the agriculture, the use of IoT devices is a common way to make
irrigation more efﬁcient and effortless [
]. 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-
], also oxidation-reduction potential and turbidity [
], and presence of
toxic substances [
]. In some cases, novel probes, such as for residual chlorine [
nitrate and nitrite, were implemented (see Figure 7) [
]. 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
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 .
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 [
]. 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
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) [
and in some cases IR sensors [
]. A load cell is also used to detect the weight of
the bin [
]. In the literature, various sensors are used to detect harmful gases [
movements near the container [
], and metal sensors to separate metallic waste [
and to measure humidity [
], as well as temperature [
]. The My Waste Bin IoT
container presented in  is shown in Figure 8.
Appl. Sci. 2021,11, 8198 14 of 29
Front and back view of the My Waste Bin, an IoT smart waste container, enabling real-time
GPS tracking and weight monitoring .
3.3.6. Sensors for Energy Efﬁciency
Energy is an essential resource for the operation of the many activities occurring in
]; therefore, the efﬁcient use of this resource is paramount to reduce costs and
promote environmental and economic sustainability .
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
Ground transportation represents the main sink of energy consumption (
45%) and the
major source of air pollutants in urban centers [
]. 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 [
]. 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 [
] and 30% of greenhouse
gas (GHG) emissions [
]. The main physical and non-physical factors involved
in indoor environment quality are shown in Figure 9[
]. These factors are measured
using wireless sensors [
], virtual sensors [
], and artiﬁcial neural networks [
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
]. 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 [
], and the building’s structural design [
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. .
Public lighting systems represent almost 20% of world electricity consumption, and it
is responsible for 6% of GHG [
]. Therefore, it is essential to centralize street lighting
control and smart management to reduce energy consumption, maintain maximum visual
comfort and occupant requirements [
]. The variable most used in lighting systems is
the lighting power density (LPD) [
]. Neural networks, wireless sensors, algorithms, and
statistical methods are used to estimate the energy consumption and the corresponding
]. Environmental factors, pedestrians’ ﬂow, weather conditions, and
brightness levels inﬂuence light intensity [
]. 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 .
Most of the time, the monitoring of the variables inﬂuencing energy consumption
in buildings and public lighting is carried out wirelessly [
]. Various intelligent
], such as artiﬁcial vision, are used to measure temperature, relative humid-
], electric current, gas ﬂow, air quality [
], lighting, luminosity [
], solar radi-
], and acoustic emission [
]. Measurement devices include light-dependent
resistor (LDR) [
], IR radiation [
], 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
]. 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 .
Table 2. Sources of energy consumption in cities.
Sources of Energy Consumption Variable Sensor Application
Thermohydrometer Temperature and relative humidity
CO2/CO CO2/CO Concentrations
pollution/Concentration NO2/NO NDIR (Non-Dispersive
Infrared) NO2/NO Concentrations
Air renovations VQT airﬂow HVAC system/equipment, building
Presence Passive Infrared (PIR) Count/Occupation/Movement of
Lighting Indoor/Outdoor Light
Light Depending Resistor (LDR)
Home networks, Wi-Fi LED
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 [
]. In general, communication infrastructures can
be classiﬁed as local area networks (LAN), wide area networks (WAN), and global area
networks (GAN) [
]. LAN sensors can communicate with gateways through different
protocols, such as: long range (LoRa) [
], Bluetooth [
], and low power radio [
Furthermore, new technologies have arisen in recent years, such as ZigBee [
narrowband internet of things (NB-IoT) [
]. On the other hand, gateways in WAN
connections send the information to the cloud through SigFox, long range wide area
network (LoRaWAN) [
], while GAN encompasses more complex technologies and
all the cellular networks GSM-GPRS [
]. Other protocols include IP and API, TCP,
Dash7 and MQTT [
], along with standards such as IEEE802.15.4, IEEE802.11.x, and
However, as the number of network nodes increase, interference problems can take
place, particularly in very dynamic environments, such as health and mobility [
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 [
]. 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 [
]. Similarly, sharing data through connected vehicles
can occur through incorporated or third-party systems [
], but accuracy and availability
widely depend on the interface used [
]. As the number of users increases, the safety,
access, and efﬁciency can be affected. Here, the vehicle itself is considered an integrated
sensor platform [
], since it can work as a data sender, receiver, and router within the
V2V or V2I communications used in ITS .
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-
]. For cycling sports, sensors are used most frequently in long-distance
running and swimming [
]. 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 [
], monitoring of vital signs via wireless [
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
], implementation of a network-based multi-channel frequency EM for reha-
bilitation patients with exercises [
], design of a system with three sensors to monitor the
ECG, body weight, and pulse of the patient [
], portable home health monitoring system
using ECG, fall detection, and GPS to monitor people outside [
], 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 [
]; 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 [
] and prevention [
], identifying road trafﬁc
] and studying the driving behavior and applying real-time feedback [
Interesting applications in urban mobility have been proposed as well, such as smart
trafﬁc lights [
], smart parking [
], collision prediction and avoidance [
WIM detection [
], and mobility visualization through heatmap representations [
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 [
trafﬁc jams location, frequency, and duration [
], and combined spatio-temporal trafﬁc
clustering and analysis .
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 [
]. For example,
a system that allows real-time monitoring of surface water quality in different aquatic
], systems for monitoring the quality of the river that crosses a city [
and the implementation of a system that could be used in pipelines networks to monitor
the quality of water [
]. 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 [
]. The water distribution systems need to be monitored to ensure that
correct water quality is distributed and for detecting pollutants .
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
]. 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 [
]. 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 [
]. 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 [
]. 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 [
], smart homes [
and home networks [
], Light Emitted Diode-wireless (LED) indoor and outdoor
lighting ﬁxtures , geothermal technology , hygrothermal comfort , cybernetic
cities, ubiquitous connectivity [
], HPCense (seismic activity), smart thermostat [
indoor lighting apps [
], microgrids [
], structural health monitoring of buildings [
ecological buildings , 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 . 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 [
]. 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
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,
]. 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
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 [
] 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 [
] and SmarTech [
] 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
]. 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
Study cases of smart cities implementations in Tecnologico de Monterrey, (
) Health: EEG monitoring for
educational services; (
) Security: Space monitoring in the context of COVID-19 pandemic; (
) Mobility: Urban accessibility
analysis in Monterrey City; (
) Water: Detection of SARS-CoV-2 RNA in freshwater environments from Monterrey City;
) Waste: GPS monitoring and app for recycling vehicles; (
) Energy: Impact of A/C usage on light-duty vehicle’s
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 [
]. 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 [
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
]. 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 [
]. 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 .
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 [
]. 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
. This information exchange process is susceptible to security threats,
such as malicious attacks or data leaks [
]. 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 [
]. 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
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
]. Intelligent lighting systems with sensors adaptable to weather con-
ditions, hours of use and presence of people or vehicles [
] 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  and low power hardware systems . 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
become essential due to the vast amount of data gathered and
analyzed in the presented applications.
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 [
]. 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
]. 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
]. 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 .
Appl. Sci. 2021,11, 8198 22 of 29
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.
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.
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.
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