Smart Cities

An Architectural Approach

André Duarte

1,2

, Carlos Oliveira

and Jorge Bernardino

1,3

Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, Coimbra, Portugal

Ubiwhere, Lda., Coimbra, Portugal

CISUC – Centre for Informatics and Systems of the University of Coimbra, Coimbra, Portugal

Keywords: Smart Cities, Machine to Machine (M2M), Machine Learning, Internet of Things (IoT).

Abstract: Smart cities are usually defined as modern cities with smooth information processes, facilitation mechanisms

for creativity and innovativeness, and smart and sustainable solutions promoted through service platforms.

With the objective of improving citizen’s quality of life and quickly and efficiently make informed decisions,

authorities try to monitor all information of city systems. Smart cities provide the integration of all systems

in the city via a centralized command centre, which provides a holistic view of it. As smart cities emerge, old

systems already in place are trying to evolve to become smarter, although these systems have many specific

needs that need to be attended. With the intent to suit the needs of specific systems the focus of this work is

to gather viable information that leads to analyse and, present solutions to address their current shortcomings.

In order to understand the most scalable, adaptable and interoperable architecture for the problem, existing

architectures will be analysed as well as the algorithms that make them work. To this end, we propose a new

architecture to smart cities.

1 INTRODUCTION

Nowadays most people live in urban areas. As

populations grow, they place increasing demand on

the city ecosystem and directly affect the entities

responsible for the city control. These challenges

make leaders adopt ways to engage with the

surroundings of their city, making them more

prepared and aware. The decisions they make not

only directly affect the city in a short term, but are

also

a means to improve the decision making process.

With the growth of human beings in urban areas

comes a significant growth in data. This data comes

from sensor networks scattered around the city or

from the sensors in a smartphone. As data was

produced there seemed to be a constant need to

integrate all of this data to provide services, therefore,

smart cities materialised.

There is a wide variety of city conceptions that

have built a new horizon for cities in their challenging

tasks in an increasingly cost-consciousness,

competitive and environmentally oriented setting.

Irrespective of whether the concept is smart city,

intelligent city, sustainable city, knowledge city,

creative city, innovative city, ubiquitous city, digital

city or city 2.0 (e.g. Komninos, 2002; Aurigi, 2005;

Carillo, 2006; Hollands, 2008, 305) they all paint a

picture of a modern city with smooth information

processes, facilitation mechanisms for creativity and

innovativeness, and smart and sustainable service

solutions and platforms (Anttiroiko et al., 2014).

However, there is still a general absence of joint

planning by city governments with utility providers

(e.g. water, in respect of environmental sustainability)

and other public services (e.g. health care). Cultural

barriers include commercial confidentiality, whereas

social media user groups work with open data

systems, causing problems for joint working of cities

with the private sector. This may create problems for

collaborative ventures between city governments and

businesses, and even with other public sector

agencies, as well as with voluntary and community

organisations. According to (Alazawi et al., 2014) a

smart city depends on the provision of information,

communication technologies and services to the

population via web based services. However, the

concept of smart city can, many times, be mistaken.

In order to be smart, a city does not need state of the

art technology, what it needs is interoperability

between various key aspects of the city, such as

governance, finance, transportation and many others.

563

Duarte A., Oliveira C. and Bernardino J..

Smart Cities - An Architectural Approach.

DOI: 10.5220/0005463105630573

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 563-573

ISBN: 978-989-758-097-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

The kind of changes that smart cities will bring to the

current world are many times said to be as similar to

those seen in the industrial revolution. The motivation

behind the concept is the ability to improve the city

ecosystem while focusing on people, allowing

technology to work for them and not with them, this

will result in a greater vision of society.

Furthermore this data brings many possibilities to

the cities because it makes smart systems’

proliferation possible. One of these cases can the

smart emergency management system, which is an

extremely important piece for the welfare and

wellbeing of people. According to (Feng and Lee,

2010) emergency management is a dynamic and

continuous process that involves preparing for

disaster before it happens. If these systems are in

place the probability of anticipating man-made or

natural disasters increases.

The systems already in place are decentralized,

which means that they do not communicate between

each other, making it almost impossible to prevent

disaster. This decentralization is due to the objectives

of the development. Most of the times these systems

are designed to address a specific case or to work as

an independent system that may receive information

from many parts, although without the aim to deliver

information to the necessary parties.

With the intent to address these shortcomings our

work will provide an architecture to a smart system in

the context of smart cities. This architecture will be

created with awareness of the system’s possibility to

scale and to adapt itself to different contexts. This

architecture will address the problem of receiving the

data, process it and then retrieve useful outputs to any

party that subscribes to a specific type of content.

This architecture can then be tuned to fit different use

cases and scenarios.

The remainder of this paper is structured as

follows. Section 2 presents related work on the topic

and aims to cover as much information as possible;

Section 3 is discusses related technologies and

intends to cover technological key aspects regarding

the theme; Section 4 shows functional use cases with

the objective of creating a baseline to support some of

the decisions made during the work; Section 5

presents an architecture for future practical

application of the analysed concepts and serves to

document it. Finally, section 6 presents our main

conclusions and suggests future work.

2 RELATED WORK

The problem presented in this paper, has been

partially developed in the past years with other

studies and projects. This section provides the

necessary background to understand the basis of the

developed work. It is important to acknowledge that

the documented analysis in the paper will be high-

level, in spite of covering as much information as

possible.

There are many papers that present solutions for

the issue that we are working on. The rest of this

section will address part of them, which we think to

be the best fit for our work.

In (Vakali et al., 2014) the concept of smart city

is discussed due to its current vagueness. Still,

according to (Vakali et al., 2014) this concept can

vary from the technologies and infrastructures of a

city to an indicator that measures the education level

of its inhabitants. Furthermore the work intends to

analyse the SEN2SOC experiment for its impact in

the current context of this topic. The SEN2SOC

(SENsor to SOCial) experiment promotes

interactions between sensors and social networks to

enhance the quality of data in SmartSantander.

The concept of smart city is also referred and

conceptualized in (Chourabi et al., 2012). The work

intends to create a framework that will sketch

practical implications for governments. Furthermore

the work enlists some success factors for smart cities,

which are: (1) management and organization; (2)

technology; (3) governance; (4) policy context; (5)

people and communities; (6) economy; (7) built

infrastructure; (8) natural environment. The proposed

framework will provide integration to all of these

factors and explain correlations between them.

Although the smart city concept began to be

defined in the previous work, more recent works seem

to extend this concept and provide different

definitions for it.

In (Piro et al., 2014) discuss that there is yet to

exist a theoretical definition of Smart City, although

cities are developing and shaping for not so distant

future. Furthermore the work enlists some of the

current definitions for the concept, that there is yet to

be completely defined.

The work also enlightens the necessity of

Information and Communication Technology (ICT)

services, with the intent to integrate them in a generic

scenario of a smart city. The approach is from a

service point of view, which means that it emphasises

the role of the services in the city. It is also important

to refer that real world cases are shown to prove the

importance of the topic.

Alongside with smart cities there are many other

concepts that need to be addressed, one of them is the

Internet of Things (IoT).

According to (Jara et al., 2014) this concept com-

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prises the full ecosystem of data in smart cities, which

in other words means that IoT generates massive

amounts of data that need to be processed by

algorithms and tools with the intent to be useful for a

city. This will also provide new ways to interact with

intelligent devices and create homogeneous platforms

that include both machines and humans working

together.

Still according to (Jara et al., 2014) this new

paradigm will shape the world and create a new

conception of the Internet and how people interact

with it, due to the constant interconnectivity between

people and the world. It will also provide the

necessary resources for the creation of new

applications and data driven platforms that will,

hopefully, improve the citizen’s quality of life.

This new way of reinventing the Internet will not

only provide endless possibilities to improve the

overall interaction between humans and machines but

also create new challenges, which need to be tackled,

to cities themselves.

Furthermore, the work aims to develop data-

driven models based on human actions to act as proof

of concept for Smart Cities. The system was

developed using the SmartSantander testbed, which

contains real-time systems and sensors scattered

around the city.

Additionally the work concludes that the devices

in the Internet of Things are able to gather data and

provide knowledge and that a new age of interaction

is about to appear, due to the increasing demand for

smart applications.

In (Benkhelifa et al., 2014) the authors listed the

current disaster management projects. The purpose of

this work is to summarize existing projects regarding

this matter. This work is relevant due to its diversity

and detail while presenting the projects, it is

extremely important to have a baseline of what was

already studied and how it can, if possible, be

improved. It is important to state that the focus of this

work is wireless sensor networks. The most relevant

outputs of this work in this context were the

knowledge and awareness of the projects in this area.

This listing provided a wider perspective about the

topic and led to discoveries regarding the State of the

Art projects, which by itself ignited the discovery of

solutions and use cases for each problem.

One of the major problems encountered when

dealing with large amounts of data is the system’s

scalability. In order to understand how similar

systems operate when larger amounts of data are in

place (Albtoush et al., 2011) explains implementation

choices that should be made in order to avoid

problems. This provides useful outputs for the

viability and feasibility of the system. This work also

explains the necessity of risk assessment of the

system, not only during the implementation but also

during the working phase. Finally it is also important

because it defines a framework for emergency

management, which includes risk assessment and

disaster prevention in a multilevel and

multidimensional architecture.

With the intent of presenting the role of today’s

technologies in this field in (Alazawi et al., 2014) it is

stated that this type of systems is growing at fast pace.

In contrast to (Benkhelifa et al., 2014), this work

focuses on Vehicular Ad hoc Networks (VANETs),

sensors, social networks and Car-to-X, where X can

either be infrastructures or other cars. These

technologies are shaping the future with the objective

of giving a ubiquitous sensing of the surroundings.

Later on the work it is identified that these systems

produce large quantities of data, changing the context

of looking at them from small, simple solving

problems, to big data problems that require stronger

and more capable algorithms to be solved. Lastly it is

presented a problem regarding the interoperability of

these systems, which is yet to be solved. The

interoperability of these systems is important due to

the necessity of presenting a holistic view of the

problems in the city.

In the literature there are already some papers that

address the need to create a smart emergency system.

A good example of this is (Radianti et al., 2014),

where the authors present emergency systems and

then start to develop a platform that intends to mimic

these systems in a smarter way. The authors used a

smartphone based publish-subscribe system to

accomplish this. The platform helps users by sensing

their surroundings and assessing the current disaster

scenario, providing them with a safer way to exit the

building. It is interesting to analyse the

communication that was developed as it takes the data

of devices and delivers it, via a web-based broker, to

managers and interested parties. The broker also

forwards the data to a big database where it is

processed in order to retrieve sensor information in

useful ways (i.e. charts, reports).

There is also another important topic to cover that

is emergency management. According to (Feng and

Lee, 2010) it’s a process that continuously prepares

for disaster even before it happens. It intends to

protect people from natural or man-made disasters. It

is expected that it can integrate many emergency

sources to provide the best possible outcome for the

situation. The main purpose of this paper is to explore

the possibility of a service-oriented architecture for

emergency systems. The authors propose an

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architecture for this scenario and conclude that these

type of systems are of extreme importance in the

nowadays world.

In our work we intend to present an architecture

for a generic smart system that collects, processes and

delivers useful data to users. In the future a smart

emergency system will be developed and will

integrate information from many places, process it

and then retrieve it to interested parties. It is important

to understand that this work is a necessary step to

accomplish a system with the minimum possible

flaws. Also we will integrate technologies that lead us

to a more prepared system.

3 SMART CITIES

TECHNOLOGIES

Systems related with smart cities require different

technologies in order to be fully addressed, therefore

this section aims to cover and introduce some of

them.

It is important to understand that these types of

technologies are of extreme importance in this topic,

some of them are directly related to the data collection

and storing, while others focus on the processing part

of the data lifecycle. Although this section will cover

most of them, it will provide more information

regarding the processing part.

To begin with, the concept of Big Data (Friess and

Vermesan, 2013) shall be addressed. It is

understandable that having so many information

inputs (sensors, smartphones, etc.) leads to a huge

amount of information that needs a new type of

treatment.

In (Friess and Vermesan, 2013) the authors refer

to big data as “(…) the processing and analysis of

large data repositories, so disproportionately large

that is impossible to treat them with the conventional

tools of analytical databases.” The authors also

explain that this data is produced by machines, that

are much faster than human beings, and according to

Moore’s Law this data will grow exponentially.

Furthermore the authors start pointing out the major

contributors for data production (i.e. web logs, RFID,

sensor networks, social data, etc…).

It is also referred that Big Data requires different

technologies to process the massive amounts of data

within a comprehensive amount of time thus, some

tools are presented in order to show the current

standards in this field.

Additionally, regarding this topic, the authors

explain that major companies in the big data topic

have a tendency to use Hadoop (Gu and Li, 2013) due

to its reliability, scalability and distributed

computing.

In (Jara et al., 2014) the authors present a

challenge to Big Data, which is of great relevance for

our work. This challenge is, perhaps, one of the most

important concepts correlated with Big Data not only

because of the large amount of data but also because

of the IoT paradigm.

The challenge presented is the new way of

interaction between humans and the Internet via smart

devices. This challenge exists, because of the way

that the Internet was created, until now the Internet

was based on a human to human kind of interaction,

because it delivers content produced by humans for

other humans. This kind of communication will not

disappear, however new types of interactions will

appear as smart objects integrate the nowadays world.

These new types of interactions produce large

amounts of data, this is where Big Data comes into

play. As has been described in this section Big Data

helps us to store this large amounts of data, with the

objective of being analysed by intelligent algorithms

and tools to extract information and provide

knowledge that will empower the applications made

recurring to it.

At this point it’s possible to conclude that Big

Data requires special treatment as it is bigger and

contains more information than typical data. For that

some algorithms and tools shall be addressed with the

intent to choose the most suitable to the presented

system.

As posted above major companies around the

world to process big data are utilizing Hadoop.

Hadoop is a framework that processes big data in a

distributed environment (Apache Hadoop, 2014).

Also, it is planned to scale up from single to

multiple machines, where each of them provides

space and computational power. This framework can

also handle failures in applications. It seems like a

good way to implement the system. However, in more

recent works, despite being around since 2009, Spark

(Gu and Li, 2013) started to be used instead of

Hadoop.

In (Gu and Li, 2013) the authors made a

comparison between the Spark and Hadoop aiming to

show which was more suitable for production. It is

important to understand that Hadoop is an

implementation of the MapReduce framework

developed by Google. According to (Gu and Li,

2013) this framework is not designed to support

applications with iterative nature, as it cannot keep

data during execution time. Because of this, at each

iteration, it needs to access disk. On the other hand,

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Spark, despite being a MapReduce-like framework, is

designed to address its current shortcomings

regarding iterative applications.

Finally the authors concluded that both

frameworks are good, but their application requires a

good analysis of the situation. If there is a lot of

memory to run the application Spark is definitely

faster than Hadoop, on the other hand Hadoop uses

less memory but a lot more space in disk.

Other types of data processing are also interesting

in the Internet of Things (IoT) context, due to their

ability of processing data streams. For instance we

can point out Complex Event Processing (CEP)

(Chen et al., 2014) and Storm (Toshniwal et al.,

2014). Notice that CEP is only a method of analysing

and processing streams of data, on the other hand

Storm is a distributed computation framework that

helps with the processing of large streams of data.

CEP is defined in (Chen et al., 2014) as an

effective mechanism that analyses data includes it in

a context and triggers events. CEP can, for instance,

analyse streams of temperature and determine if the

changes in that temperature are normal or abnormal.

It can also relate different types of event that lead to

a single complex event, such as: (1) flames; (2)

temperature spike; (3) sudden humidity decrease.

From these three events the system could infer that a

fire was happening. Additionally (Chen et al., 2014)

aims to develop an architecture for the IoT based on

distributed complex event processing. The intent

behind distributed CEP is to shorten the bandwidth

and the necessary computation.

Storm (Toshniwal et al., 2014) is a real-time

distributed stream data processing engine that

manages data streams. It was designed to be scalable,

resilient, extensible, efficient and easy to administer

which makes it a very robust and usable structure.

Figure 1 presents a storm topology, which is the real

time component that runs all the logic. Topologies are

then divided in spouts and bolts. Spouts, represented

by the water tap in Figure 1, and are the source of the

streams of data. Bolts, represented by bolts on the

topology, intend to consume the data sent by spouts,

process it and then produces processed outputs.

Furthermore

Figure

provides a fault tolerant and scalable

architecture for handling data. Additionally this

architecture provides the concept of worker that can

be interpreted as a node which is programmed to

execute a specific task. These tasks may vary,

although a good example can be using a worker to

process the stream with the Esper queries. In other

words each Bolt is associated with a query to be

applied in the stream. This will create an efficient a

quick way to process the incoming stream and query

it for different types of alarming events.

Additionally this two technologies together help

one another, in other words Esper needs something to

organize and provide data which means that some

system needs to be implemented to provide Esper

with the data. This is where Storm is useful, it can

handle the data management and Esper will handle

the queries. This approach will join both systems to

enhance both of their main capabilities when dealing

with these type of data.

Figure 1: Storm Topology (Apache Storm, 2014).

A very interesting aspect of Storm and CEP, is

that they both can work together to provide an

excellent way of processing and analysing data in our

scenario.

To access this data, sensors and other devices are

required. With the intent of making a more

transparent communication, the concept of Machine

to Machine (M2M) (Wan et al., 2012) emerged.

According to (Wan et al., 2012) M2M refers to the

automatic communication between, computers,

sensors and other devices in the surroundings. This

topic is relevant because it makes sensor-to-server

communication and sensor-to-sensor possible. This

allows the system to constantly check for new data

and vice versa.

This concept leads us to another one related to the

communication that is publish-subscribe services.

According to (Ordille et al., 2009) these services

broadcast information to the subscribed parties. In

these types of systems a subscriber is a device that

will receive information from the publisher. This

translates into a much more transparent system,

because the publisher can send information to the

subscribers and vice versa. Finally in (Radianti et al.,

2014) their publishers are treated as the ones that

generate information in the form of events.

Subscribers are treated as the ones that subscribe to

arbitrary flows of information. And brokers are a

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middle layer between the two participants to pass

along the information.

In short, these technologies, due to their relevance

in this topic, seem to be an absolute need. They

provide a coherent and robust ecosystem to help

developers create and deploy their applications. The

combination between Storm and Esper seems to be

very interesting, since it provides an elegant approach

to the topic.

In the latter sections some of these technologies

will be addressed again, from an implementation

point of view, the main goal is to provide ideas for a

future implementation, leaving comments on which

technology is the most suitable choice for a specific

component of the architecture.

4 USE CASES

In this section current use cases of similar systems

will be addressed. This will result in a better

knowledge base for the current standards in the area.

For this, not only examples of smart cities will be

presented but also examples of emergency systems

that became smarter with the inclusion of these new

concepts.

Lately many smart cities have emerged, such as

Amsterdam (Amsterdam Smart City, 2014),

Santander (Santander Facility, 2014), Barcelona

(Barcelona Open Cities Challenge, 2014), and many

others. These cities, due to constant innovation

projects and investments, have a tendency to be

pioneers in the adoption of new standards in this field.

These cities use smart systems help the decision and

facilitate the decision making process.

In Finland, the city of Helsinki is running a

cooperation cluster called Forum Virium Helsinki

(Forum Virium Helsinki, 2014) to provide a platform

to develop ICT-based services in cooperation with

enterprises, public authorities and citizens as end-

users. The platform is concentrated on five project

areas, one of them being a smart city initiative

focusing on the development of mobile phone

services to facilitate urban travelling and living. It

also opens up public data so that companies and

citizens can create new services by combining and

processing the data in innovative ways. This

resembles the LivingLab movement that has spread

across Europe in the 2000s (The European Network

of Living Labs, 2014).

The city of Santander, for instance, uses sensors

to monitor the environment, parking areas, parks,

gardens and irrigation systems. These sensors are

scattered around the city in order to produce alerts

that will notify end users with useful knowledge of

the situation.

The data is captured by an IoT node that monitors

indicators such as temperature, noise or light. This

data then travels through repeaters positioned in

higher grounds, which send it to the gateways. Lastly

this data is stored in a database or sent to other

machines where it’s needed.

Regarding the environmental scenario, from a

user’s point of view, the available indicators are the

temperature, CO level, luminosity and noise, this

allows them to receive useful inputs for their

wellbeing throughout the day.

The environmental monitoring system is

important because it shows how sensors interact with

the server and how the server communicates back to

the sensors and other subscribers that need this type

of information. To summarise, we will discuss the

“Participatory Sensing” concept (Description of

implemented IoT services, 2014) to obtain a better

knowledge about how users interact with the

platform, and in which way is it relevant to their day-

to-day life. Figure

illustrates the concept of

participatory sensing from a user’s point of view,

which helps us understand how a typical user

interacts with this kind of technologies and also how

they provide useful inputs to understand the type of

data a user needs during application usage. It is

possible to visualise that a user can, in this case,

publish events, search for events, visualise historical

data, subscribe and unsubscribe to events and receive

notifications.

Figure 2: Participatory Sensing - Use Case Diagram

[Adapted from (Description of implemented IoT services,

2014)].

The components of the participatory sensing

system are: a mobile client for end users to utilise; a

server, capable of iterating through data and

providing links between the apps and the

SmartSantander platform also known as “Pace of The

City Server”; and a module that allows devices to

called “Universal Alert System” (UAS) system,

which aims to fire user’s notifications.

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The “Participatory Sensing” concept allows users

to actively participate in the city ecosystem. The

information is then sent to the SmartSantander

platform. The concept starts to get even more

interesting when users become subscribers of the city

systems and are able receive updates of the current

status of the city or the road they have to cross to

reach their destination. This type of instant real time

information directly affects the city from a user’s

point of view due to its constant availability and

usefulness. The system is available for smartphone

via the app and for none smartphone users, via SMS

or call.

Additionally Santander city provides other

interesting case studies, which are “Precision

Irrigation” and “Smart Metering”.

Precision irrigation is a service that intends to

provide a useful way of monitoring plants necessities

and guarantee that they are fulfilled. Rather than

being applied to a whole park, this system is applied

by sections or individual plants. Also, the system not

only focus on water management but also in other

plant needs and their species and growth patterns to

minimize the effort from the staff. Even though it

looks a bit off the topic this system allowed to realise

the necessity of designing the system to accept

communications with REST and WebSockets, which

are the communication technologies used by it.

Smart Metering system aims to provide IoT based

solutions to monitor energy usage in offices. To

address this problem new components have been

added to the architecture to generate, collect and store

the data and information. In addition to these,

intelligent components have also been created in

order to provide useful information in user-friendly

way. These components provide real time analysis of

data and consequent knowledge extraction. With this

it can identify energy failures and reports on energy

consumption that can be drilled down to a specific

case.

The last system analysed was (Cecchinel et al.,

2014), which is a prototype, named

SMARTCAMPUS that aims to equip the SophiaTech

campus with sensors to inspire the creation of new

applications. Once more the system was chosen due

to its usefulness and value in terms of possible inputs

for our system.

The SMARTCAMPUS deals with many types of

sensors to collect the data. To tackle this challenge the

authors propose the architecture seen on Figure 3.

This architecture divides in two main focal points: the

message collector which intends to collect all data

from the internet or sensor networks, to further store

in a database that acts as a message queue; and the

message processing that aims to process the messages

stored in the queue. These components then store the

processed information in a database.

Furthermore the architecture contains a

configurator, which acts as a routine that can be called

periodically to propagate a specific sensor

configuration through the network. It also contains a

database that contains the current sensor parameters,

an API to provide an administrator interface to

connect with sensors and a data API that directly

accesses data to provide statistics or other types of

knowledge.

5 PROPOSED ARCHITECTURE

This section aims to present our architecture to

address the typical Smart City scenario. This

architecture will provide a way to gather information

Figure 3: Middleware Architecture (Cecchinel et al., 2014).

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from many sources process it and provide useful

information to the interested parties.

One of the most important things to understand is

that nowadays data comes mostly in streams, which

presents an issue due to the tools needed to process it.

The tool that we projected to use, to process streams

of data is Storm, which has already been documented

in this paper. Even though Strom, by itself, cannot

retrieve results one hundred percent accurate, due to

being stream oriented, we plan to overcome this

problem by implementing a parallel processing block

with Hadoop. This will, not only provide exact results

when the large amount of data is processed, but also

provide a better knowledge of the data.

The approach was inspired by the lambda

architecture (Lambda Architecture, 2014) with a

concrete direction of using the publish/subscribe

pattern. The background from other related projects

allowed us to perceive that some technologies may

not suit very well the collection and direct processing

of data. Thus, we opted by a more complex approach

that allows to a more scalable and reliable system.

This type of approach also led us to extend the

capability of receiving data from multiple sources,

which is extremely important in the context of IoT.

Furthermore, we shall analyse the proposed

architecture, present in Figure 4.

Our architecture is projected to act as an API to

provide a connection between data in the IoT and the

final user, with the intent of providing relevant

information regarding emergency situations.

The system will receive a data stream from IoT

nodes, which is then duplicated to be processed by the

batch and the speed layer. After that the data is

merged with the intent of providing the result with the

biggest confidence level associated. When the data is

merged a bottleneck can happen, although this

situation will be prevented by accepting the first

result to appear with the highest confidence level.

This can happen in two ways: (1) the stream layer

finishes and the batch layer continues to process.

With this scenario the stream layer result will be

returned with a confidence level attached to it; (2) the

stream and batch layer finish at the same time. In this

case the data will be merged to provide the most

accurate output.

After the data is merged it reaches another

processing block, which intends to filter and redirect

the acquired knowledge to the subscribed parties.

Additionally this block sends the processed data to

the statistical data block. The latter block not only

keeps track of statistical data to help us understand

patterns along the year but also provides data to

construct KPI’s, charts and reports.

After the processing is all done, users can access

the data in two ways: (1) via the data API, which is

projected for developers who want to build

applications around this context; (2) via the data

output, which will serve to return the data to the

subscribed parties. Additionally the API will provide

a way of notifying other sensors in the field, which

means that if a sensor sends a fire alert, other sensors

around it will be asked for their current situation to

localize the hazard with maximum precision. This

type of communication is also important if the fire is

located near a road since the system can be prepared

to notify street lights to prevent drivers from entering

the affected road. Also in the highways a lane can be

closed and the traffic redirected to other lanes or even

roads.

Figure 4: Proposed architecture.

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This architecture can be applied in many different

scenarios; one of them will be addressed so that we

can establish an example to explain some of its

functions. Let’s assume we have three types of

sensors: smoke, flames and temperature. These

sensors are constantly sending a stream of data into

our system, the idea is to process this data in order to

figure out whether we are in the presence of a fire or

not. The system has a threshold that serves as a

maximum possible value for a normal event, when

crossed they trigger events that can lead to, in this

case, a fire. Having different types of sensors allows

us to better understand whether the fire is happening.

Different combinations of events can occur, thus the

system must have something to divide the ones that

are indeed problematic. Furthermore we shall

materialise this example:

 If there is smoke, flames and the temperature

passes the threshold, then we have a fire;

 If there is smoke, no flames and the temperature

is rising, it is possible to have a fire.

Many more combinations can be presented, although

these explain the concept that we are trying to achieve.

Furthermore each module of the presented

architecture should be accounted for when choosing

the right technologies, in order to access the full

potential of it. Hence we need to account for the data

stream that is arriving. For instance, it should use a

publish-subscribe messaging system, which will

handle the stream and split it into events that can be

processed by the rest of the modules. The events that

have been split will be processed by the both layers.

At this point, in the speed layer, there are two

important things to acknowledge: (1) it is advised to

use a complex event processing system due to the

nature of the system, this will provide an event based

approach which will necessarily climax with event

correlations and a smarter way of dealing with the data

stream that constantly change. This approach will also

provide the ability of integrating many types of events

at once, this will expand system acceptance in terms

of receiving events and inevitably prepare it to explore

further sensor integrations; (2) an in memory database

for storing alarming events is also useful, because of

the high demand from the system.

In the batch layer algorithms with predictive

capabilities should be added to enhance the system

overall quality and usefulness. This will provide ways

to calculate KPI’s, draw charts and predict whether it

is important or not to be in maximum alert level. From

a high level perspective this type of inputs seem to

have a great importance, with applications such as

divide a specific fire protection team to a zone which

is prone to peaks of fire during the summer or redirect

traffic because a particular road is more likely to be

affected by the floods in the winter.

The rest of the processing components in the

system can be executed with any programming

language and should withstand the volume and

velocity of data, also the code statements should be

optimized to minimize overheads and bottlenecks.

The databases should be chosen according to the needs

of each specific scenario. It is important to understand

that many database systems can be chosen to

incorporate the solution, although for each specific

situation a brief analysis of the problem should be

made in order to perceive the best possible choice. As

a practical example we can point out that the database

in the speed layer should be in-memory, on the other

hand the statistical storage could be an NO-SQL

database that supports large quantities of data to

enhance overall system scalability and has a good read

mechanism due that its main focus is reads.

Moreover other important aspect to discuss is the

communication. The way the system is designed, and

from the lessons learned from the use cases, the best

technologies should be REST, WebSockets and

MQTT. REST will provide an easy and consistent way

to access the API, providing endpoints for events and

the ability to execute filters in the queries.

WebSockets are useful because due to the facilitations

in terms of real-time communication. The MQTT

protocol is important to establish connection between

the system and sensors and actuators scattered in the

city in order to extract real-time information.

Additionally other important aspect is the

inclusion of a message broker, which will accepts

messages from the source divide the stream of data in

messages that are easier to process and correlate for a

better, more useful and more accurate output, which is

delivered to a consumer.

6 CONCLUSIONS AND FUTURE

WORK

This paper intends to document the current state of the

art in smart city systems and their related technologies.

Over the coming sections the problem has been

documented and some use cases were studied to

provide the most possible inputs with the intent to

understand which challenges existed and needed to be

tackled.

The analysed documents provided several useful

outputs to establish a

good baseline in terms of

architecture and tools to be used. For instance the

concept of participatory sensing, from

SmartSantander, led us to think that with so much user

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data available the system could be adapted to process

it with the intent of retrieving knowledge from it.

Another example of a good tool to process data in IoT

is the lambda architecture, which provides the best of

the stream layer processing allied with the batch layer

that provides more accurate results.

The knowledge extracted from the state of the art

systems and technologies guarantees that our

contributions were, as expected, scalable, adaptable,

feasible and viable.

Furthermore, we aim to develop a system that will

address the current shortcomings in this context. This

system will be more directly related to emergency

management. Therefore we aim to construct a

platform that receives disaster data from many

sources, process it via established components and

lastly retrieves it to any party that subscribed to the

specific type of event. Consequently this paper also

serves as a document to establish an architecture for

that type of system, serving as a first practical

application of it. An initial overview of the

technologies that can be used was also made with the

intent of providing the necessary steps to implement a

similar system, or at least provide some additional

knowledge regarding this topic.

A smart emergency system is important in the

current context due to its usefulness and transparency

while dealing with data, as it can provide predictions

and problems before they happen to managers. Thus,

with the use of this type of system data becomes

clearer and leads to a more prepared and quicker

response to any emergency or disaster.

Another interesting application, which empowers

the system, is social mining, which due to the

importance of social networking in nowadays society

seems like and excellent way to complement the

inputs of the system.

This is important to complement the system

because it can detect disasters via a post in a social

network. The post does not need to be in a specific

format, the algorithms will only be looking for

keywords that will trigger the attention of the system.

Although this data is extremely relevant, it is

important to guarantee that it isn’t false. A possible

solution for this problem can be a request to the

sensors that are placed in that specific site.

In short, Internet of Things is successfully thriving

in the current world, therefore these type of systems

will continue to emerge alongside it. An excellent way

to evolve and prepare future cities is to be more

interconnected and aware, in essence enabling better

decision-making.

ACKNOLEDGEMENTS

This work was partially financed by iCIS – Intelligent

Computing in the Internet Services (CENTRO-07-

ST24 – FEDER – 002003), Portugal.

This work was also made possible with the help of

Ubiwhere, Lda, which provided useful inputs in

discussions and also the facilities.

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