Synaptic City

An Architectural Approach using an OSGI Infrastructure and GMaps API

to Build a City Simulator

Gustavo H. R. P. Tomas

1,2

, Welington M. da Silva

1,2

, Kiev Gama

, Vinicius C. Garcia

and Alexandre Alvaro

Informatics Center, Federal University of Pernambuco (UFPE), Recife, Brazil

Recife Center for Advanced Studies and Systems (C.E.S.A.R), Sorocaba, Brazil

Federal University of S˜ao Carlos (UFSCar), Campus Sorocaba, Sorocaba, Brazil

Keywords:

Smart Cities, Internet of Things, Architecture.

Abstract:

Big cities have noticeable problems resulting from poor management of urban services. Monitoring a city

gives some parameters to evaluate the performance of urban services, allowing to identify their ﬂaws, and

elaborate strategic plains to correct them. Smart cities architectures are intended to propose Information and

Communication Technologies (ICTs) solutions to increase service efectiveness. Considering that to establish

a ICT infrastructure to support a smart city architecture is very expensive, once you have got the data and

you know what urban service you will attend to, you can use a city simulator to test how the architecture will

behave when facing the city’s problems. In this context we propose the Synaptic City architecture, together

with a city simulator, with which we modeled a Brazilian city, Recife, Pernambuco, in order to discuss its

main problems.

1 INTRODUCTION

The literature offers several deﬁnitions of the term

City, but the most accepted is described in Kuper (Ku-

per, 1995): a relatively large and permanent settle-

ment. Usually, a big city has a high population den-

sity, with its citizens living constant interaction with

industries, market and services. Under the operational

viewpoint, cities are based on a set of basic infrastruc-

ture: energy, water, transportation, infrastructure, in-

formation and communication, leisure, home, citizens

and public sanitation (Morvaj et al., 2011).

According to a UNESCO report released (Na-

tions, 2007), in 1950, 30% of the world population

lived in urban areas and in 2010 this percentage grew

to 50%. It is estimated that by 2050 the percentage of

people living in large urban centers will be 70%.

In the brazilian context, accordingto research con-

ducted by the Brazilian Institute of Geography and

Statistics (IBGE), published in the Diario Oﬁcial (an

ofﬁcial journal where the brazilian government pub-

lishes its actions, decisions and resolutions about the

state) (de Geograﬁa e Estat´ıstica (IBGE), 2012), in

July, 2012, Brazil reached 193,946,886 inhabitants,

representing an increase of approximately 1.65% in

comparison to 2010. With the growth of both the pop-

ulation and the complexity of the issues that concern

a city, there is a challenge in combining data from

different sources with Information and Communica-

tion Technologies (ICTs) in order to promote better

living conditions for citizens. This challenge, which

in other words is how to make a city become a Smart

City, has been widely discussed in the literature, from

projects and initiatives with different views on the

concept (Gifﬁnger and Pichler-Milanovi´c, 2007) (Su

et al., 2011) (Kanter et al., 2009).

However, there is no consensus regarding the def-

inition of this concept, nor as to the most appropri-

ate environment to use it. In Kehua et. al. (Su et al.,

2011), IBM deﬁnes smart cities as the use of informa-

tion and communication technologies to capture, an-

alyze and integrate relevant information into the core

cities systems. At the same time, a smart city can

make smart decisions for different types of needs, in-

cluding daily aspects, environmental protection, pub-

lic safety, city services and industrial and commercial

activities.

In order to make better decisions that facilitate the

citizens life, it is needed to constantly monitor the var-

ious environments that compose a city. Each environ-

427

H. R. P. Tomas G., M. da Silva W., Gama K., C. Garcia V. and Alvaro A..

Synaptic City - An Architectural Approach using an OSGI Infrastructure and GMaps API to Build a City Simulator.

DOI: 10.5220/0004421204270434

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 427-434

ISBN: 978-989-8565-60-0

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

ment must be mapped with different types of sensors,

such as temperature and presence (Hern´andez-Mu˜noz

et al., 2011). The greater the variability of sensors,

better the ﬁdelity level of this mapping in relation

to the real environment. This variability should be

treated in relation to frequency of data sent, data type

and variability of communication protocols (Filipponi

et al., 2010) (Hern´andez-Mu˜noz et al., 2011) (Lee

et al., 2011) (Zygiaris, 2012) (PlanIT, 2012) (Black-

stock et al., 2010).

Thus, it is important to developa central layer, that

is able to attend several devices with different tech-

nologies. Additionally, this layer should be reliable

to handle multiple simultaneous requests for informa-

tion generated from the combination of several data

sources (Filipponi et al., 2010) (Hern´andez-Mu˜noz

et al., 2011).

Nevertheless, for testing and validating this cen-

tral layer it is necessary to use several sensors. These

sensors are difﬁcult to be implemented in real envi-

ronments, mainly due to high cost and some public

policies (Sanchez et al., 2011). Therefore, the need

to simulate the mapping of a real environment arises,

with several different types of sensors sending infor-

mation for an indeﬁnite time. This simulator must be

ﬂexible enough to model any city, independently of

cultural and demographic characteristics.

In this context, this work proposes a smart city ar-

chitecture and validates it using a data simulator. Sec-

tion 2 discusses related work in a minimally validated

stage, found in literature. Section 3 describes the

Synaptic City Architecture proposal divided in three

layers. Each layer is presented and all technologies

used for implementation are described. Section 4

presents the case study, describing the target city and

all related scenarios. Finally, Section 6 concludes

discussing results from the architecture and data sim-

ulator.

2 RELATED WORK

In order to have an updated snapshot of what is hap-

pening in an urban environment it is necessary to

sense, keep track, of whatever is happening in its sur-

roundings. Sensors allow computer systems to inter-

pret a real world situation, based on raw data, that

must to be related to a whole context to generate use-

ful information.

Monitoring a city gives some parameters to eval-

uate the performance of urban services, allowing to

identify their ﬂaws, and elaborate strategic plans to

correct them. Strictly speaking, sensors are usually

capable of measuring physical quantities and convert

it into a digital signal, that by its turn will become

the raw data aforementioned. Adding some compu-

tational capabilities to the practical sensor concept,

i.e., “smartening” a sensor, makes it able to act as a

distributed, contextual and reconﬁgurable node in a

highly dynamic, distributed and heterogeneous envi-

ronment, collecting data, generating information, al-

lowing implementation and support to pervasive com-

puting environments (Lei, 2003)(Tan, 2010).

When we expand this behavior to a context where

all objects are connected and acting as data sources -

as sensors - applying to some common goal, we reach

the Internet of Things (IoT) concept(Atzori et al.,

2010). According to Tan and Wang (Tan, 2010), the

Internet of Things is responsible for linking the phys-

ical and the information world.

Nowadays, as there are emerged projects in which

“crowd sensing” is highly utilized, providing data

from real world devices to developers create some

value proposition using them.

One of these projects is described in (Cosm,

2012), where people are invited to connect their de-

vices to the COSM platform and, developers and

companies, besides connecting devices can integrate

apps to securely store and exchange data. It offers

real-time and scalable controlling, monitoring and

analysis of the available data.

Additionally, the Magic Broker platform (Erbad

et al., 2008) was adapted to smart cities, aiming

to provide a consistent model and interfaces stan-

dardization for building Internet of Things applica-

tions. It addresses an architecture model driven by

citizens engagement, to build and deploy application

suited to their needs, exploiting the use of smart sen-

sors, sensor web technologies and IoT technologies,

such as Radio Frequency Identiﬁcation (RFID). The

project’s target is to compose a middleware, using

OSGi bundles (i.e., software components) (Alliance,

2007), capable of supporting a wide range of devices

and programming models to support wide area dy-

namic composition of devices and services.

The (Tecnic, 2012) presents a web centric sensing

platform, called WoTkit, that facilitates the connection

between real-world objects and the Internet, engaging

users as “participatory sensors”, allowing developers

to build “revolutionary services”, as they say. The fo-

cus is to help people to reduce costs through com-

pelling services, enabling the development of mobile

applications that sense and control the real-world.

Demand for smart cities that consolidate and de-

liver contextualized information about services of

cities is latent and becomes stronger as cities grow

and technologies emerge. Initiatives worth mention-

ing, such as the NYC BigApps (City, 2012) and

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RioApps (Prefeitura do Rio de Janeiro, 2012) con-

tests, where urban data is made available through

software interfaces so people are encouraged and re-

warded for creating outstanding applications using it,

thus creating some value proposition to its citizens,

improving urban daily life, resource usage, and qual-

ity of urban services.

Finally, our work aims to join the ﬂexibility in

coupling new devices to the architecture - as COSM

does - but, instead of letting it be associated to user-

deﬁned subjects, we predeﬁned some concerns cov-

ered by Synaptic City, standardizing the data that will

be made available. Both ﬂexibility and standardiza-

tion will be achieved through a middleware capable

of handling data from different devices, maintaining it

uniformly. All these data will be provided to develop-

ers so they can build and compose useful services and

applications in daily life of citizens, exploring new

possibilities for urban services management based on

crowdsourcing.

3 SYNAPTIC SMART CITY

ARCHITECTURE PROPOSAL

One of the biggest concerns when creating a smart

city software architecture is sharing responsibilities

among the entities, keeping it consistent, guarantee-

ing extensibility and ﬂexibility to the supported ser-

vices. Thus, we divide the Synaptic Smart City ar-

chitecture in three layers, as shown in 1: physical,

middleware and application.

Figure 1: Synaptic City Architecture.

The physical layer (1) is responsible for hosting

the sensors. In our test bed we had no proper phys-

ical sensors to use, so we decided to simulate them

using a Mobile and a Web application. The former

took the form of an Android trafﬁc application con-

sisted in a single button on the screen, which the user

should press in case of facing a trafﬁc jam; the latter

consisted of a Web application that comprised a City

Simulator. This layer has as responsibilities capturing

raw data coming from different sensors - concerning

to trafﬁc or water/electricity consumption - and for-

warding it to its speciﬁc producer bundle (2) at the

middleware layer.

When the data reaches the middleware layer (3) it

is saved in a database (4) with the purpose of main-

taining historical data about the addressed concerns.

Once saved, it is published in a message-oriented

middleware under a speciﬁc message topic (5) from

which the application - through the consumer bundles

(6) - can retrieve the incoming data. As all the con-

cerned topics are in this layer, contextualized infor-

mation can be generated, or different concerns can be

related, and a new topic with a new type of informa-

tion will be available.

In the application layer (7), the data can be ac-

quired through a subscription to a concerned topic in

the message-oriented middleware. The interesting as-

pect of this layer is that an application can subscribe

to different concerns and create its own contextual

knowledge on the available data, increasing the rich-

ness of the information provided to the user.

For capturing useful information in the smart

cities contexts it is necessary to involve citizens.

Therefore, it is important to choose technologies that

can encourage people to provide (layer 1) and con-

sume (layer 7) data, without which this interaction is

unpleasant. Thus, due to the increasing number of

people with smartphones (Hall, 2012), we decided to

create two applications for this type of device. The

smartphone operating system chosen was Android,

because of the facility in deploying new applications

and the market share it holds. Our applications were

built on the Froyo version (2.2), but they are compat-

ible with newer Android versions.

By using the trafﬁc application, one can identify

through the map the trafﬁc bottleneck and the amount

of cars in every city area. The trafﬁc application feeds

the base providing location reports every 20 minutes.

The second application is responsible for showing in-

formation about the sustainability level of a building,

according to its reported water and energy consump-

tion. Furthermore, some scenarios are designed to

simulate a situation in which several people will be

using these applications. These scenarios will be dis-

cussed throughout this work.

We deployed the discussed layers in the Ama-

zon Web Services (AWS) infrastructure, that offers

a highly reliable, elastic, scalable and low cost solu-

tion (Services, 2012). There was no special reason for

that choice, we only wanted available infrastructure,

able to give support to as much requests as possible,

as well as a high number of connected sensors send-

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ing data constantly; in case of a increasing in one of

these, it would scale automatically, without having to

rethink about hardware or deployment issues.

3.1 Middleware: OSGI

The OSGi platform was chosen for developing the

Middleware layer (2). According to OSGi refer-

ence (Alliance, 2007), “The OSGi technology is a

set of speciﬁcations that deﬁne a dynamic compo-

nent system for Java. These speciﬁcations enable a

development model where applications are (dynam-

ically) composed of many different (reusable) com-

ponents”. OSGi reduces complexity by providing a

modular architecture for large-scale distributed sys-

tem, inhering some important advantages for smart

cities architecture scenario, such as modularity, hot-

deployment, scalability, and security. The OSGi com-

ponent system was used to build some widely used

projects like Eclipse, GlassFish, IBM Websphere, Or-

acle/BEA Weblogic, Jonas, JBoss, among other.

The OSGi advantages match with Synaptic City

goals, mainly modularity for build new services.

Thus, for implementation purposes we chose Equinox

Bundles (Foundation, 2012) component, due to

familirity with Eclipse enviroment and integration

with Jetty Web Server.

For validation purposes, three services were cre-

ated: trafﬁc, water and energy consumption. For

each service, a producer and a consumer bundle

were built. The producer bundle is responsible for

putting information about services on OSGi. More-

over, the consumer bundle is responsible for getting

all information and making it available via REST

interface. In Blackstock et al. (Blackstock et al.,

2010) they proposed a platform called Magic Bro-

ker (MB2), which provides some basic abstractions,

such as events, state, and content management ser-

vices. Based on that, the producer-consumer commu-

nication is done via OSGi’s Event Admin component,

an event-oriented communication architecture based

on queue and topics, resembling a message-oriented

middleware.

In the trafﬁc service, the producer bundle puts in-

formation needed for trafﬁc reports, such as latitude,

longitude and report time. From this information, the

consumer joins several reports and calculates a range

of intense trafﬁc around 200 meters. So, consumer

makes available a block list of areas with trafﬁc is-

sues in the last 20 minutes.

In the case of water and energy services, the same

informations was used. The producer bundle con-

tinuously sends the following information: the wa-

ter/energy consumption, entity type and entity posi-

tion (latitude and longitude), during an undeﬁned pe-

riod. The entity type must be residential, commercial

or industrial. The consumer bundle makes available

an average consumption on the last week, as well as

a classiﬁcation based on the population average con-

sumption.

Table 1 summarizes the information to all bundles

services interface. If more services need to be added,

it is necessary just to create a producer and consumer

bundles and plug them in architecture, thanks to OSGi

hot deploy capability.

3.2 Physical: City Data Simulator

The simulator plays an important role in the Synaptic

City architecture. Since we did not have any physical

sensors available, we needed to create something to

serve as data producer on the Physical layer (1), that

could be attached to the architecture and act just like

any other sensor was supposed to. That is why we

implemented the City Simulator.

To make the simulation closer to the real world

we created entities to represent some urban elements:

residences, buildings, data center, cars, city hall, ho-

tel, football stadium. Each one, except for cars,

is identiﬁed uniquely from its geographical location.

With this entities set it is possible to simulate several

urban scenarios, with a geolocalized distribution rep-

resented with GMaps API. To validate the proposed

architecture concept, we built some speciﬁc scenarios

in Recife, Pernambuco, Brazil.

Furthermore, one can simulate any urban environ-

ment simply placing the map on the targeted city, se-

lecting an entity and putting it in the most appropri-

ate place. There is no restriction on the number and

types of entities that can be added. Once this is done,

it is possible to send the report of each entity man-

ually or select the auto city simulator mode. In this

mode, each entity periodically sends their informa-

tion: in the case of cars, trafﬁc reports are sent; for

other entities, each report with water and energy con-

sumption are sent.

For entities that provide water and energy infor-

mation, the report values vary according to their con-

sumption rate, based on real world values. For ex-

ample, over a month, a data center should consume

more water and energy than a city hall, mainly be-

cause the data center should be in operation all the

time with a high and almost no variable consumption,

while on weekends the city hall has a decrease on its

consumption values. Therefore, consumption reports

of the data center must have greater values than the

ones measured in a city hall.

To utilize the data generated by the entities, we

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Table 1: Information vs. bundles summary.

Service Producer Consumer

latitude Block List:

Trafﬁc longitude -block latitude

report time -block longitude

-amount of reports

latitude latitude

Water longitude longitude

entity consumption consumption rate

entity type consumption mean

latitude latitude

Energy longitude longitude

entity consumption consumption rate

entity type consumption mean

created some classiﬁcation reports in which, accord-

ing to the measured consumption, each entity is said

to be sustainable or not.

4 CASE STUDY

The architecture efﬁciency is measured according to

its behavior in real world scenarios. Speciﬁcally in

smart cities architectures, the validation in real en-

vironments is difﬁcult because often one needs in-

frastructure such as sensors network throughout the

city and the availability of Internet connection. More-

over, government issues usually hamper this valida-

tion, mainly in aspects related to public areas.

From the simulator viewpoint, the efﬁcacy is mea-

sured according to the similarity level between the

simulated and the real environment. Therefore, this

section aims to address the case study conducted to

validate the Synaptic City architecture and the City

Simulator. Starting with a contextualization of the

city that the simulator aims to represent, we will de-

scribe the studied scenarios and how the needed in-

formation was captured and used.

4.1 Target City

The city chosen for the experiment was Re-

cife, capital of Pernambuco, Brazil. According

to statistics from Brazilian Institute of Geog-

raphy and Statistics (IBGE) (de Geograﬁa e

Estat´ıstica (IBGE), 2012), the metropolis con-

tains approximately 1.555.039 inhabitants,

which places as the 8th most populated city in

Brazil. From a sociological viewpoint this cluster

of people implies in the occurrence of several

problems that affect directly existing services, such

as transportation, security, water and electricity

supply/consumption, sanitation and natural resources

utilization; from a technological viewpoint, this

implies in several opportunities to turn problems into

solutions to make life pleasurable living, through

improvements and new ways for providing urban

services.

Moreover, Recife is one of the host cities of the

FIFA Confederations Cup 2013 and the FIFA World

Cup 2014 with urban mobility demands. Focusing on

trafﬁc, the Recife city has about 600,000 registered

vehicles. The city’s road network is not ready for that

amount of vehicles due to its narrow streets and few

roadways as optional routes. On energy and water

issues, citizens have no system that quantify the level

of their consumption or even measure the quality of

service in real time.

In the following subsection, we will analyze some

scenarios chosen to represent these problems.

4.2 Scenarios

By using City Data Simulator it is possible simu-

lates several urban scenarios, combining different en-

tities. To validate the proposed architecture concept,

we built some speciﬁc scenarios that mimic Recife’s

reality. Three scenarios were chosen to represent crit-

ical situations that occur frequently.

(a) Recife citizens are passionate about soccer and

fanatics by the three top teams: Nautico, Sport

and Santa Cruz. When there is a soccer game be-

tween these three great teams, several fans go to

stadium to root and encourage their teams. Thus,

the ﬁrst scenario aims to illustrate this event, in

which a soccer match between Sport and Nautico

is held at Ilha do Retiro stadium. The avenues

that give access to the stadium are narrow and are

not prepared to meet that amount of cars. Fortu-

nately, the stadium is located in a residential area

and, from some buildings around the stadium, it

is possible to watch the entire game.

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(b) For being a coastal city, some periods of the year

it rains a lot in the city of Recife. In turn, the

main avenues that give access to the peripheral

region does not have an effective drainage sys-

tem, causing inconvenient trafﬁc jams. There-

fore, the second scenario aims to illustrate this sit-

uation, speciﬁcally highlighting Imbiribeira Av-

enue, which crosses the city and gives access

to the International Airport of Recife/Guararapes

and a major neighboring city, Jaboat˜ao dos

Guararapes.

thanks to public policies to encourage art and cul-

ture. As an example, we can cite international

artists concerts. The last scenario describes the

situation in which a large event is held at the Jos´e

Rego Maciel Stadium (known as Arruda) belong-

ing to Santa Cruz Futebol Clube. Currently Ar-

ruda has capacity for 60,044 people, and there

are several access routes to the stadium, some of

them are narrow, with low ﬂow of cars, and others

wider, with a more intense ﬂow.

5 RESULTS

Once the architecture design is completed and the

producers and consumers bundles, as well as the

simulator, are all created, the resulting information

must be analyzed. Speciﬁcally in the Recife context,

these results are interesting from a practical stand-

point. Their analysys will be separated in two aspects:

Synaptic City Architecture and data city simulator.

5.1 Synaptic City Architecture Results

Starting with the architecture results, the usage of the

OSGi framework made the architecture well modu-

larized and ﬂexible, in terms of the amount of ser-

vices provided. Additionally, each service can receive

data from different sensors, as long as a new bun-

dle producer is created and all the data are sent us-

ing the Event Admin topic concept. The architecture

serves multiple client applications via JSON content

provided by bundles consumers, as implemented in

the web and as smartphone applications.

This applications variability is an important factor,

because it increases the likelihood that citizens will be

informed as soon as possible about any event in the

city. By receiving this information, citizens can make

the best decisions aiming their quality of life, taking

advantage of improved urban services. In this ﬁrst

experiment, the types of applications are restricted to

those only with Internet access.

Furthermore, the service composition is a require-

ment easily answered by that architecture organiza-

tion. To compose various services, you just have

to capture the data of the respective topics, combine

them appropriately and make them available via web

interface for any other application.

5.2 Data City Simulator Results

As the simulator, we highlight the possibility of cre-

ating different types of sensors. Considering that to

establish an ICT infrastructure to support a smart city

architecture is very expensive, once you have got the

data and you know what urban service you will attend

to, you can use a city simulator to test how the archi-

tecture will behave when facing the city you are try-

ing to represent. Besides, different cities have differ-

ent problems, and using a simulator makes it easy to

model how these problems affect the performance of

urban services and how they are related to each other.

By knowing the input data and the output information,

a simulator allows to create different scenarios to test

the architecture effectiveness.

In the context of this work we should analyze the

results obtained with the simulated scenarios of Re-

cife. These scenarios represent everyday situations

and should be analyzed, mainly, from the viewpoint

of citizens welfare. Figure 2 illustrates the three sce-

narios. In all of them, the circles around each entity

represents that, at the moment, consumption/trafﬁc

reports are being sent .

The scenario shown in Figure 2(a) represents the

classic soccer match between Sport and Nautico. An-

alyzing this scenario, we can notice that the football

stadium is sending reports constantly. Likewise, the

buildings around the stadium are consuming enough

water and energy. This probably means that people

are watching the game at home increasing energycon-

sumption. In turn, the trafﬁc reports (represented by

cars) are concentrated in the main access to the sta-

dium, causing a heavy trafﬁc jam. Figure 2(a) illus-

trates the information sent by the buildings.

Regarding the scenario illustrated in Figure 2(b),

a trafﬁc jam can be noticed at the Imbiribeira Avenue,

which gives access to important points in the city,

such as the International Airport. Simultaneously, we

notice an increase in both water and electricity con-

sumption for buildings around, possibly due the mea-

sures being taken to mitigate the problems caused by

trafﬁc bottlenecks and ﬂooding. Also in Figure 2(b)

there is an illustration of the information sent by each

trafﬁc report.

Finally, the scenario shown in Figure 2(c), we can

see that the main access to the stadium are congested,

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(a) Soccer Game (b) Imbiribeira Avenue

Figure 2: Results Three Recife scenarios.

but there are some alternatives roads that can mini-

mize the time spent to get to the event. Moreover,

we can observe changes of consumption in buildings

around the stadium, possibly because of the opportu-

nities created by the event, as parking lots and com-

merce. Finally, one can observe a bullet with the in-

formation that is sent from a stadium entity.

With this information acquired from the three sce-

narios, citizens can take some decisions to improve

their quality of life, optimize their time and reduce the

stress. Related to the ﬁrst scenario 2(a) it is possible

to choose going to the stadium before the scheduled

time and pick the best path in real time. Consider-

ing the second scenario 2(b), it is possible to devi-

ate routes to avoid ﬂood points. The third scenario

2(c) can be mitigated choosing among different ac-

cess routes to the international concert.

Besides this vision from the viewpoint of the cit-

izen, one can analyze this information from the per-

spective of business opportunities. For example, in

scenario 2(a) traders could exploit the brand of the

teams in points with high concentration of people, and

knowing the stadium and buildings consumption en-

ergy suppliers could work on optimizations, extract-

ing consumption patterns from the scenarios, allow-

ing to model and forecast some chaotic events, such

as power outages or even consumption peaks for spe-

ciﬁc events, and determine the appropriate correc-

tive/preventive actions.

6 CONCLUSIONS

Throughout this work, some aspects that an smart city

architecture must meet were soon discussed. As a

case study, we described the Synaptic City architec-

ture. The Synaptic City constitutes a project with a

proposal to build a modular, scalable and robust ar-

chitecture, coupled with a wide simulator.

All results of this work were obtained from

the use data simulator, in which no empir-

ical experiment was conducted. Neverthe-

less, in this validation context, the architecture

shown to be ﬂexible, especially regarding the ser-

vices composition aspect. This characteristic is very

important because in a smart city context is important

to combine information from different services.

At the end of this paper, we concluded that the ar-

chitecture deﬁnition cannot be focused only on tech-

nological issues, but on issues related to daily lives

of citizens and ways to make a participative action

become a part of an effective solution to urban prob-

lems.

For future work, we will develop new topics in the

Synaptic architecture with contextualized services, as

well as model some local scenarios, such as com-

panies, with sensors monitoring a more restricted

scope, allowing to manage services in smaller do-

mains. These federated solutions will stablish a new

concept of smartness in a city, built from a collection

of well managed subdomains, enabling more focused

approaches to solve local problems based on a holis-

SynapticCity-AnArchitecturalApproachusinganOSGIInfrastructureandGMapsAPItoBuildaCitySimulator

433

tic view - in which each federation has its own topic

from which other ones can get information - aiming

to set up a high quality urban environment with very

pleased citizens.

ACKNOWLEDGEMENTS

This work was partially supported by the National In-

stitute of Science and Technology for Software En-

gineering (INES)

, FAPESP

, grant 2012/10157-1,

FACEPE

, grants 573964/2008-4, APQ-1037-1.03/

08 and CESAR

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