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Smart City IoT model: a prospective analysis
considering indicators of social, technical and
operational aspects
MSc. Engª. Kelem Christine Pereira Jordão
Department of Communications (DECOM) of the Faculty
of Electrical and Computing Engineering (FEEC)
The State University of Campinas (UNICAMP)
Campinas-SP, Brazil
kcpj5@hotmail.com
MSc. Eng. Hermes José Loschi
Department of Communications (DECOM) of the Faculty
of Electrical and Computing Engineering (FEEC)
The State University of Campinas (UNICAMP)
Campinas-SP, Brazil
hermes@decom.fee.unicamp.br
MSc. Luiz Antonio de Sousa Ferreira
Department of Communications (DECOM) of the Faculty of
Electrical and Computing Engineering (FEEC)The State
University of Campinas (UNICAMP)Campinas-SP, Brazil
luiz.ferreira@outlook.com
Prof. Dr. Yuzo Iano
Department of Communications (DECOM) of the Faculty of
Electrical and Computing Engineering (FEEC)
The State University of Campinas (UNICAMP)
Campinas-SP, Brazil
yuzo@decom.fee.unicamp.br
Prof. Dr. David Bianchini
Center of Exact Sciences, Environmental and Technology
(CEATEC) of the Pontifical Catholic University of Campinas
(PUC CAMPINAS)
Campinas-SP, Brazil
davidb@puc-campinas.edu.br
Abstract— The concept of smart city is getting more and more
relevant for both academics and policy makers. Despite this,
several ongoing research approaches and industry efforts are
aiming at developing standards and best practices to define the
concept of IoT platform for Smart Cities. Some recent research
has been proposing the concept of subgroups for the planning
and organization of cities without losing this vision. In this
context, this paper present the Smart City IoT model, where its
characteristics compose items related to the city's infrastructure,
digitization, but also relates social aspects of its population, such
as educational and cultural level. Through the mapping
methodology it was possible to identify the indicators that should
be measured during the process of transformation of traditional
cities into Smart City IoT. As a result, it is evident that the
expected time of the innovation trigger from the IoT platform
must consider service indicators to measure the efficiency,
productivity of existing operations between the city and the
citizen.
Index Terms— Smart City IoT model, indicators, subgroups,
social, technical and digital aspects.
I. INTRODUCTION
The Internet of Things (IoT) is being adopted in different
application domains and is recognized as one of the key
enablers of the Smart City vision. Despite the standardization
efforts and wide adoption of Web standards and cloud
computing technologies, building large-scale Smart City IoT
platforms in practice remains challenging, interoperability
among heterogeneous devices is one of the most challenging
task [1]–[2] –[8]. On the other hand, too often, city departments
dive headfirst into the realm of connected technology without
coordinating their efforts or understand their demands. This
results in a variety of compatibility issues that leave cities with
high costs, and increasingly far from the real Smart City vision.
In addition, with this uncoordinated approach, key day-to-day
data ends up siloed off within departments, without due
treatment. This makes it difficult for city leaders to fully
capitalize on the treasure trove of insights made possible by the
IoT [3]–[4]–[6].
In this context, it is evident that besides the municipal
departments work together to implement IoT technologies, it is
necessary to understand and quantify the different demands
involved in the municipality. Some recent research has been
proposing the concept of subgroups for the planning and
organization of cities without losing their systemic vision, but
seeking in the division by subgroups (or subcenters) to
understand the particularities of each regions contained in the
city, identifying their existing and necessary infrastructure,
investing to develop a digital network, encouraging the
strengthening of their identity, and working connected to the
other subgroups, composing a unique city [12].
In this paper, we present the Smart City IoT model, where
its characteristics compose items related to the city's process,
digitization and infrastructure, but also relates social aspects of
its population, such as educational and cultural level. This
model reflects the idea that a smart IoT city - where people,
machines and process are connected - can only enjoy all
efficiency of technology capability, when developing aspects of
your intellectuality social and cultural, as a tool that difference
them of a simple IoT city. On this model, that is not limited to
technological aspects but goes beyond, is being proposed the
list of base indicators for mapping a smart city IoT with social,
operational and technical aspects.
The rest of this paper is organized as follows. Section II
describes the analysis of IoT standardization for Smart Cities.
Section III describes the subgroups concept for Smart Cities.
Based on our experience in a real-world case study, Section IV
presents the methodology proposed and Section V presents
results and discusses for a case of study. Finally, Section VI
presents the conclusion.
II. IOT STANDARDIZATION
In recent years, several ongoing research approaches and
industry efforts are aiming at developing standards and best
practices to define the concept of IoT platform for Smart Cities,
highlighting the studies on the requirements and designing
technologies [5]–[7]–[9]. However, we believe that one
previous analysis should occur, to identify and establish the
demands for IoT platform, consequently, this analysis becomes
a tool to build cohesive Smart City. In this context, dozens of
alliances and coalitions are forming in hopes of unifying the
fractured and organic IoT landscape. Rather than trying to fit
all of the IoT Protocols on top of existing architecture models,
some studies propose levels of organization, as described
below [10], [11]:
- Infrastructure (ex: 6LowPAN, IPv4/IPv6, RPL);
- Identification (ex: EPC, uCode, IPv6, URIs);
- Comms / Transport (ex: Wifi, Bluetooth, LPWAN);
- Discovery (ex: Physical Web, mDNS, DNS-SD);
- Data Protocols (ex: MQTT, CoAP, AMQP, Websocket,
Node);
- Device Management (ex: TR-069, OMA-DM);
- Semantic (ex: JSON-LD, Web Thing Model);
- Multi-layer Frameworks (ex: Alljoyn, IoTivity, Weave,
Homekit);
Whereas that IoT covers a huge range of industries and uses
cases ranging from a single constrained device to massive
cross-platform deployments of embedded technologies and
cloud systems connecting in real-time. These levels of
organization propose to classify the different protocols, in a
setting that best represents the device's role and its application
in the smart city, in addition to associating numerous legacy
and emerging communication protocols that allow devices and
servers to talk to each other in new, more interconnected ways
[11].
All levels of organization are inter-related, and theoretically
it is necessary all of them to make all of them work. In this
context missing one will break that model and stall the
communication process. One possible outcome of successful
standardization of IoT is the implementation of "IoT as a
Service" technology, if that service is offered and used in the
same way we use other "as a service" technologies today the
possibilities of applications in real life will be unlimited [10].
In this context of “IoT as a service”, we have the city
environmental, where the innumerous systems and process in
the urban territory (public transport, energy, water network,
garbage collection, paving stones, public illumination, sewer,
etc.;) will become technologically connected each other and
with users, transforming the dynamic, the efficiency, and life of
cities inhabitants. Therefore, it becomes extremely necessary to
understand and quantify the different demands involved in the
municipality, however this understanding it becomes complex
when performed in a holistic way [12].
III. SUBGROUPS CONCEPT FOR SMART CITIES
The proposal of this concept is structured the analysis of the
city no longer in a macro scale, where the territory is visualized
in its totality, but in a micro scale, where the local planning and
the identity of the studied place is rescued, aiming at to the
individual perception of the needs and challenges of each
subcenter of the city [13]. Some characteristics are observed
and planned on this micro scale:
1) Operational aspects: Smart cities, in contrast to centrally
planned cities, are organized on a decentralized model
(subcenters or subgroups), but connected to each center
composing a unique city [12]. In the meantime, it should seek
to explore concepts in the territory that will work to increase
the density and their multicentricity - since the subcenters are
generators of employment, infrastructure optimization,
housing, education, health, security, etc., within the same city -
providing the better use of existing empty spaces in regions
provided with urban infrastructure, and thus minimizing the
need for new investments in infrastructure to meet the demand
of the city. When well worked the subgroups density, it also
promotes a more efficient urban mobility system when it is
planned to integrate the territory. This is possible by identifying
and connecting the existing multi-centers within the territory,
by exploiting with better performance the use of collective
public transport and by providing alternatives for mobility,
which are accepted in the daily life of the population by
transmitting security, reliability and practicality [14]. Examples
are the cycle paths implemented to link the dynamics of the
city (residential areas with commercial areas, universities,
industrial, etc., and not just segregated bicycle paths in
neighborhoods, squares and parks) and the use of walking as an
alternative modal, sharing of vehicles, etc.; [15]. Such a change
in behavior will naturally contribute to reducing the number of
vehicles on the street, thereby reducing congestion extensions,
and increasing the accessibility and use of alternative modes.
Significant gains in air quality in cities by reducing the
emission of gaseous pollutants, consequently improving air
quality by reducing the level of greenhouse gas emissions per
capita [16].
2) Social aspects: Each subgroups should be closely linked
to the development of knowledge, research, innovation, culture,
arts, economics, technology, of the place studied and have in
these areas their raw material to differentiate them from others
by stimulating the creation of ideas, building a culture of
knowledge and intelligence in all segments of the subcenter
and thus rescuing the strengthening of the city's competitive
advantages as a larger group [16]. The intelligent city possesses
in its DNA concepts and elements sustained by the creativity of
individuals, companies, capital, institutions of knowledge,
academia, governments, educated and productive population,
etc.; [17]. It is through the contributions promoted by creativity
that a city can maintain its identity, develop its characteristics
and stimulate its potential towards differentiation and
competitiveness even in the face of globalized competition.
This is only possible by encouraging creativity, providing
access to new and varied knowledge, as well as respect for
diversity and differences as they are sources for creativity and
innovation. Ideas that are connected to new technologies in a
constant and growing act within the territory, improving the
quality of life of its inhabitants [18].
3) Digital aspects: Once the subcenters have been identified,
it is also intended to complement the organization of sites by
promoting interconnection between existing systems
(commerce, transportation, public administration, infrastructure
use, health, work , education, environment, etc.) by making
intensive use of digital technologies (applications, sensors,
citizen services, etc.;), thereby promoting efficiency in services
provided, process improvement, agility, quality, within the
territory [19].They promote to their inhabitants, through
interconnectivity, a community digital space with the
construction of networks of knowledge, and providing spaces
where the exchange of information and experiences occurs.
These are the ones that enable the participatory innovation
process conducted by citizens, employees and end users [13].
IV. METHODOLOGY PROPOSED
Understanding the concept of smart city through the
subgroups perspective, mapping their main characteristics and
electing some possibilities of “IoT as a service” within the
urban environment by provide the data interconnected, it is
possible to identify the base range of indicators that a Smart
City IoT should demonstrate during the process of city
transformation, as shown in Table 1: Smart City IoT: Service
indicators to measure the efficiency and productivity of
operations between the city and the citizen.
This methodology has been applied as a real experience in
Brazil, where some cities were selected for the study. For the
cities chosen, the smart city indicators were observed and
measured with the aim to understand how far away the cities
are from experiencing IoT as service in an urban environment,
having the operational, social and digital aspects selected for
that.
V. RESULTS AND DISCUSSIONS
Regardless of the challenges involved in being intelligent, it
is notorious the need for reformulation that Brazilian urban
territory must cross. Problems such as waste, lack of water and
energy resources, urban agglomeration, congestion, growing
demand in the volume of data transmission, among others, are
some of the issues faced by municipalities and which urgently
need to be studied.
Even in the face of this scenario in which becoming an
intelligent city became a necessity, the first observation of this
present study is that there is a gap between the indicators
measured in the traditional cities and the necessary indicators
to have the Smart City, being more expressive the difference to
become a Smart City IoT. Additionality of that, the data
available on the Brazilian cities, are more related to the
efficiency of the service (as if they were working
independently), than the responsibility of this service must to
have with the other systems and with the citizens.
VI. CONCLUSION
This scenario of difficulty in collecting the data for the
realization of the evaluation, extends to all 3 groups evaluated
(social, operational and digital). The suggested model for the
Smart City IoT presented in this article, relates and connects
concepts that were previously uncorrelated. The actual work of
collecting and measuring the cities will require prior
preparation to ascertain their level of development with
regarding to smart city, and then assess their level in relation to
Smart City IoT model.
ACKNOWLEDGMENT
The authors would like to thank the CAPES (Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior), CNPq
(Conselho Nacional de Desenvolvimento Científico e
Tecnológico), FAPESP (Fundação de Amparo à Pesquisa do
Estado de São Paulo), DECOM (Departamento de
Comunicações), FEEC (Faculdade de Engenharia Elétrica e de
Computação), UNICAMP (Universidade Estadual de
Campinas), Center of Exact Sciences, Environmental and
Technology (CEATEC), IFSULDEMINAS and the Pontifical
Catholic University of Campinas (PUC CAMPINAS).
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Table 1: Smart City IoT; Service indicators to measure the efficiency and the productivity of existing operations between the city and the citizen.
Aspects
“IoT as a service”
Smart City Indicators.
Operational
1. Efficiency service on the urban territory, creating a culture of value-added and new
business models to services offered by the city:
1.1. Public transportation: through the sensors, video cameras and GPS upgrades for
public transport is possible managing the real-time traffic flow in the city,
predicting accidents, reducing congestions, optimizing the daily dynamic of
logistic transportation. Other initiative involving the transport public and IoT
services is the parking monitoring to better manage the congestion in the city.
1.2. Energy, water, gas and related management by controlling supply and demand
and allowing the operators of these services themselves to act with energy
efficiency measures on the premises of consumers.
1.3. The intelligent irrigation system that collects data on soil moisture and other
environmental factors from a wireless sensor network to reduce water and
energy waste.
1.4. Garbage collection; A solar-powered trash receptacle that incorporates a trash
compactor that alerts sanitation crews when the bin is full, has reduced the
pickup garbage frequency.
1.5. Public illumination; Public lighting companies may decide to automatically turn
off part of the lights of certain streets in periods without pedestrian traffic;
1.6. Leisure areas through a park or square near the house: In the same way, the
public irrigation service of the public squares, can determine the cut of
irrigation water because the humidity sensors detect rain or that the gardens are
already saturated with humidity.
1.7. And innumerous other possibilities on the urban territory;
1. For a Smart City, is necessary mapping the main dynamic and the demands involved in
the municipality. With this new concept, the city is being prepared, planned and
organized to receive the IoT service:
1.1. Mapping the subgroups and your density: working to optimize the infrastructure
existent within the same city, providing the better use of existing empty spaces in
regions provided with urban infrastructure, and thus minimizing the need for new
investments.
1.2. Mapping the mobility diversity, the close public transport and home to work
displacement time.
1.3. Exploiting the better performance of collective public transport thought
subgroups, and providing alternatives for mobility, which are accepted in the
daily life of the population by transmitting security, reliability and practicality.
1.4. Identify and monitory the locals of afforestation, leisure areas, parks open sewage
and garbage accumulated around the households.
1.5. Monitoring the demand by subgroups of water, sewage and energy, and garbage
collection;
1.6. Urban Infrastructure conditions related to urban well-being: public lighting,
pavement, sidewalk, curb, manhole or wolf's mouth, ramp for wheelchairs and
public places;
2. Reduction of the waste and operational expenses through the services provided by the
urban territory to the citizen, increasing productivity and improving the safety of its
workers;
2.1. Sensors in pipes, pumps and other water infrastructures to monitor conditions
and control water loss, identify and repair leaks, or change pressure if
necessary.
2.2. Intelligent meters installed in end consumers enable real-time monitoring of
demand and leak detection by residents and property managers, reducing costs.
2.3. Monitor water levels on viaducts to prevent accidents and monitor traffic
congestion.
2. For each of the services provided by the municipality, is necessary understanding the
efficiency and the quality of the services offered by the city to the citizen. The concept
here is understanding that the citizen is the customer in this relationship between city
and your habitants:
2.1. Number of failures in the provision of services;
2.2. Number of equipment failures during the service;
2.3. Cost involving emergency situations in the operations of the municipality;
2.4. Unnecessary and extra procedures;
2.5. Time lost due to accidents that could be predicted;
2.6. Excess inventory;
2.7. Cost involved in investigating complaints;
2.8. Ineffective operating instructions;
3. Increase actions related to the municipality environmental (air, water conditions,
noise, etc.), mapping the locations responsible of bad environmental conditions:
3.1. Service of monitoring (24 hours per day) all locations where the airflow,
visibility, and range of gases (CO, CO2, NO2, O2, SH2 and PM-10) are more
imminent, and create conditions for the improvement of these unfavorable
conditions.
3. The city needs to know its limitations and weaknesses, with the mapping of regions of
risk as well as regions that demand more attention regarding the emission of polluting
gases from the environment.
3.1. Emission of gases associated to the greenhouse effect;
3.2. Use of substances that deplete the ozone layer;
3.3. Concentration of pollutants in air;
3.4. Burns and fires;
3.5. Water quality that supplies the municipality;
3.6. Noise pollution; etc.;
4. Optimize asset productivity across city environmental through predict maintenance
on equipment and machinery.
4.1. Wireless technology on the roads to measure variables such as temperature,
humidity and traffic volume. Sensor data is sent over a wireless network to a
server for processing and analysis. This information allows road crews to
prioritize maintenance during adverse weather conditions, which are
responsible for 1/4 of vehicle accidents.
4.2. Bridge sensors, viaducts, buildings, etc.; Which monitor all aspects of the
structure's "structural health", such as vibration, pressure, humidity and
temperature, the degree of movement of the structure, the speed that seismic
4. For all locations in the city where it requires maintenance (bridges, overpasses, asphalt,
etc.) the smart city needs to have mapped these locations, as well as the maintenance
plan required for each.
4.1. Monitoring the equipment used by the municipality in the city dynamics, applying
the predictive, preventive and corrective maintenance.
4.2. Seek the balance between maintenance to minimize costs and downtime of the
machinery, maximizing their productivity;
waves (in case of earthquakes) travel through the building, and how Structure
changes.
Digital
5. Digitalization and connectivity of the functions responsible for the services rendered
within the urban territory, as well as its inhabitants. Such communication will
produce an increase in productivity, as well as the reliability of the operations
provided by the municipality to the citizen;
5. One of the pillars of support of the smart city corresponds to its level of digitalization of
the municipality. Therefore, the following indicators should be mapped and worked for
your achievement:
5.1. Mass internet access
5.2. Open data and transparency as a strategy for citizen engagement
5.3. Involvement of the citizen at all levels of social class
5.4. Growth of the digital industry
5.5. Collaborative digital platforms, increasing the likelihood of innovation in the
subgroups
5.6. Citizens are guided to deliver ideas through participatory innovation, independent
learning and feedback.
5.7. Information being provided in real time to the citizen, giving him greater
participation in decision making and therefore more conscious, participatory and
collaborative.
6. Network sensors, for collecting data and transmission with all the different
components of a typical network, including routers, bridges in different topologies,
LAN, MAN and WAN;
7. The connection to different parts of the network can be including Wi-Fi, Bluetooth, Paw
Power, Wi-Max, Ethernet, Long Term Evolution (LTE) as well as the recent promising
Li-Fi technology, so it should be measured:
a. Municipal level of sensing (WSN = Wireless Sensor Network, cameras,
RFID, etc.;);
b. Popular wireless sensor networks include accelerometers, voltage meters,
anemometers, "weighing in motion" devices and temperature sensors. Other
previous initiatives have sensor, vehicle, and traffic light connections to
control the flow of traffic in cities.
c. Variety of telecommunication technologies;
d. Access speed of telecommunications technologies;
Social
7. The IoT ecosystem demands a level of knowledge that exceeds those existing in
traditional cities. Not only a demand for the operationalization of the new services
that will be appearing, but a new demand of knowledge for the population.
7.1. As new business models emerge, knowledge-gathering work will be needed to
deal with the technology being presented, adapting to new solutions and
evolving to the constant improvement of existing models;
8. For a smart city, the incentive for creativity corresponds to the DNA of its essence. The
city should have mechanisms to encourage and map its operations focused on creativity,
such as:
8.1. Cities intimately committed to the development of the knowledge and knowledge
of its inhabitants.
8.2. Valorization of the culture of knowledge and intelligence to strengthen the
competitive advantages of the territory
8.3. Space to stimulate the development and application of innovative ideas within
urban territory
8.4. Incentive creation, providing access to new and varied knowledge (diversity)
8.5. Ideas that are connected to new technologies in a constant and growing act within
the urban territory, improving the quality of life of its inhabitants
8.6. Communities in which it exercises articulation, cooperation, tolerance,
multiculturalism around objects and common responsibilities in the city.
8. Potentialize and diversify the service provided by the municipality by clusters, as well
as the attendance of new services;
9. For a smart city, it is interesting that it has mapped the characteristics of its population,
such as: GDP per capita;
9.1. Longevity;
9.2. Diversity of employees by sector of economy;
9.3. Human development
9.4. Creativity and Cultural indexes;
9.5. Etc.
9. Increase the happiness of the inhabitants of the municipality to reduce and bear costs.
10. For a smart city, it is interesting the work of its identity, develop and stimulate its
growth, such as:
10.1. Preservation and promotion of cultural values;
10.2. Incentive the sports, cultural activities, etc.;
10.3. Gender equality and freedom of thought social inclusion.
