Integrating a Multi-Agent Smart Parking System using Cloud

Technologies

Milton Boos Junior

1 a

, Lucas Sakurada

1 b

, Paulo Leit

1 c

, Paulo Alves

1 d

, Gleifer Vaz Alves

2 e

Andr

e Pinz Borges

2 f

and Diego Roberto Antunes

2 g

Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Polit

ecnico de Braganc¸a, Braganc¸a, Portugal

Federal University of Technology - Paran

a (UTFPR), Campus Ponta Grossa, Paran

a, Brazil

Keywords:

Multi-Agent System, Cloud Architecture, Smart Parking.

Abstract:

Smart parking (SP) systems are becoming a solution to address the increasing trafﬁc in major cities, which

are related to the trafﬁc congestion, unnecessary time spent searching for parking spots, and, consequently,

environmental issues. These systems intend to help drivers that are searching for available parking spaces in a

given desired location. This paper presents a cloud-based solution to integrate a Multi-Agent System (MAS)

for SP, which enables the modularization, scalability and robustness of such large-scale systems. The MAS

abstraction is a suitable approach to represent the dynamic features of a SP, where multiple drivers arrive,

request, search, and leave the parking spots. The cloud services enable to scale up the use of a MAS, being

an intermediary in the communication between the MAS and the end user, providing a broad architecture that

involves database, asynchronous functions activated by events and real-time message exchange. The cloud

agent-based system was deployed in the parking of an University campus, where users driving bicycles and

cars can request and schedule parking slots that are managed in a distributed manner by the MAS. The obtained

results show the user friendly interaction with the system, the scalability of the system in terms of drivers and

parking spots, as well as the efﬁcient management of the parking spots by the MAS system.

1 INTRODUCTION

Over the past few years, a considerable population

increase in large cities was noticed. According to

(United Nations, 2019), around 2050, the world’s ur-

ban population will reach 68%, exceeding the current

rate of 55%. As a result of this population growth,

these urban conglomerates tend to become messy and

disorganized over time, putting the management of

natural resources and energy at risk (Johnson, 2008).

The trafﬁc in urban areas is greatly affected by

this disorganization. According to (IBM NewsRoom,

2011), approximately 30% of a city’s trafﬁc is caused

by drivers actively searching for a parking spot. As

example, in London, drivers spend an average of 67

https://orcid.org/0000-0002-9735-8249

https://orcid.org/0000-0003-0145-1834

https://orcid.org/0000-0002-2151-7944

https://orcid.org/0000-0002-0100-8691

https://orcid.org/0000-0002-5937-8193

https://orcid.org/0000-0002-1716-8614

https://orcid.org/0000-0001-7098-2597

hours a year looking for on-street and off-street park-

ings (Cookson and Pishue, 2017). This requires the

need to ﬁnd solutions that reduce the time spent by

drivers on the roads and reducing trafﬁc ﬂow, re-

sources and emission of gases that are harmful to the

environment (Goetz, 2019). Although the creation

of new spaces facilitates the reduction of trafﬁc con-

gestion, the ideal solution would be to facilitate the

search for available parking spots, which reduces sig-

niﬁcantly the time spent in the trafﬁc.

Smart parking technologies emerge to solve this

problem, allowing the better management of parking

spaces, quick rental of vacant spots, or even reserve

a space as needed in a private parking. Implemen-

tations in this ﬁeld range from mobile applications,

that inform drivers about vacant spaces to park, to

the use of Artiﬁcial Intelligence (AI) to determine

the best parking spot for a given user according to

its needs. Most of the smart parkings are composed

of Cyber-Physical Systems (CPS), whose counter-

parts are computational processes such as software or

applications, and physical technologies such as sen-

sors and actuators, where both are integrated in the

Boos Junior, M., Sakurada, L., Leitão, P., Alves, P., Alves, G., Borges, A. and Antunes, D.

Integrating a Multi-Agent Smart Parking System using Cloud Technologies.

DOI: 10.5220/0010978300003179

In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022) - Volume 1, pages 681-689

ISBN: 978-989-758-569-2; ISSN: 2184-4992

681

same network (Xu et al., 2018). Multi-Agent Sys-

tems (MAS) (Wooldridge, 2002) is seen as a suitable

approach to implement such smart CPS, based on a

collection of autonomous components that interact,

negotiate and coordinate efforts among themselves to

achieve their goals. Therefore, the system becomes

distributed and able to self-regulate in case of failures

or disruptions. In a Smart Parking Multi-Agent Sys-

tem (SPMAS), drivers and parking spots are seen as

agents, that communicate with each other to negoti-

ate the allocation of parking spots according to the

driver’s desires, namely the location, date and price.

This paper explores the integration of cloud tech-

nologies with MAS to implement a distributed smart

parking system, introducing ﬂexibility to integrate

different modules, providing end-to-end security and

system decentralization. This cloud architecture also

integrates the user interface (UI) with the vehicle’s

drivers and the interface with the physical assets,

namely the controllers regulating the access to the

parking spots. The proposed approach extends a

previous MAS based smart parking system (Saku-

rada et al., 2019) that was implemented to manage

the parking of vehicles in an University campus, by

adding cloud computing functionalities to enable the

modularization, scalability and robustness of such

large-scale system.

2 TECHNOLOGICAL

APPROACHES FOR SMART

PARKING SYSTEMS

Currently, most of the smart parking solutions are im-

plemented by applications that aim to simplify the

search for parking spots by informing their location

using GPS (Global Positioning System) or allowing

some sort of digital payment for the used spots.

The system described in (Khanna and Anand,

2016) helps the users to know in real time the park-

ing spots availability, using mobile apps connected to

the Cloud. The system uses ultrasonic sensors con-

nected to an ESP8266 board to check if the parking

slot is occupied. The ESP8266 is connected to a pro-

cessing unit (Raspberry Pi) that transmits the gathered

data to the cloud using the MQTT (Message Queu-

ing Telemetry Transport) protocol. Due to its central-

ized approach, if the processing unit fails, the users

will not be able to search the available parking slots.

Another Cloud-based approach is presented in (Pandit

et al., 2019), which uses infrared sensors, to detect the

vehicles, connected to an Arduino board that captures

the data and sends to a speciﬁc cloud provider.

The system uses the HTTP protocol for the data

transmission and and allows the data reading in real

time.

Park King (Ajchariyavanich et al., 2019) is an In-

ternet of Things (IoT) based smart parking system in-

tegrated to a cloud in an University Campus. It uses

an IoT module for monitoring the parking spot avail-

ability and a Web application where users can reserve

a parking slot. To access the parking, users must read

the QR Code via the smartphone, generated by the

web application at the end of the reservation process,

and show it to the sensor that validates and allows the

vehicle to enter in the parking. A NodeMCU board

sends the data to a cloud database, which is used by

the system to display the data and to control reserva-

tions. As discussed earlier, this centralized approach,

based on a processing unit, can fail and external users

will not be able to search and reserve parking spots.

The smart parking management system described

in (Melnyk et al., 2019) aims to minimize the search

time for an empty spot in large car parkings and to

make the spot localization easier. It uses a mobile app

that communicates with the parking infrastructure us-

ing the MQTT protocol for requests, payment and no-

tiﬁcations. Although the system includes the payment

functionality, a dynamic pricing approach based on

location, demand or time is not provided. A parking

system based on wireless sensors that communicate

with a network router responsible to send the infor-

mation to a local server, which is responsible for syn-

chronizing information with an integrated cloud plat-

form, is proposed by (Mohammadi et al., 2019). End

users interact with the system through the cloud.

When using a centralized smart parking solution,

it is possible to notice the lack of scalability that can

lead to problems caused by the technical failure of

sensors, or precariousness in the negotiation and pric-

ing of spots that constantly depend on a central server.

Many of these solutions can solve certain problems

related to parking, but most do not present an ap-

proach that indeed makes use of smart based tech-

nology or yet fails to provide a decentralized solution

that mitigates the dependency of central node and the

need for responsiveness. The mentioned applications

only inform about the vacant parking spots and do not

consider any analytical knowledge.

Aiming to tackle the aforementioned issues,

MAS-based solutions provide the cooperation among

different components in a distributed way, where each

agent acts in an autonomous and decentralized way

to achieve a whole objective (Leit

ao and Karnouskos,

2015). Different MAS-based approaches for smart

parking systems can be found in (Sakurada et al.,

2019; Pham et al., 2015; Castro et al., 2017). In

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682

most of MAS-based approaches the cooperation be-

tween agents is carried out by the information shar-

ing and enforcing constraints using rule-based proce-

dures (Chieh-Chang Li et al., 2004). The ability to

operate even if one of the components fails due to the

malfunctioning is one of the beneﬁts when using a

MAS technology within a smart parking architecture,

because it provides a distributed communication that

handles normalization of the system in such cases, as

well as multiple parallel requests.

A study was made to compare possible ap-

proaches to solve consensus problems in such dis-

tributed systems, and particularly addressing the ne-

gotiation strategies in an agent-based smart parking

system (Alves et al., 2019). Furthermore, the usage of

holonic agents in the MAS approaches applied to the

smart parking scenarios is also being explored, which

may have a higher capacity to deal with the failures

and higher scalability.

The incentive to technological advancement pro-

vides a better scenario for the implementation of a

smart parking in a context of large urban conglomer-

ates; however, an infrastructure that can support this

entire environment is necessary. Likewise, it is im-

portant to emphasize that the cost of IoT components,

e.g., sensors and microcontrollers, can be a decisive

factor when implementing a smart parking project.

Additionally, IoT systems provide a huge amount of

data trafﬁc, which can be costly depending on the

number of users. Using local servers may not be a

choice that favors scalability, and it is extremely nec-

essary to take care of security factors, since a network

failure can cause serious damage.

In this context, Cloud computing is evolving as a

new computing model designed to offer dynamic and

on-demand computing environments for users, pro-

viding a fast, secure and customizable service (Hayes,

2008; Bharti and Goudar, 2012). Cloud can also com-

bine applications delivered as services over the Inter-

net, as well as the hardware and systems software in

the data centers that provide those services (Armbrust

et al., 2010). Based on these concepts, many applica-

tions have been developed using the services offered

by Cloud providers, which brings beneﬁts related to

virtually unlimited data processing and storage re-

sources, and no investment needed with the mainte-

nance of the computational infrastructure.

On the one hand, there are some smart parking so-

lutions that use Cloud services but are not MAS-based

solutions. On the other hand, some MAS-based ap-

proaches for smart parking do not present the use of

Cloud technology to assure scalability and decentral-

ized services. The combination of these two worlds,

i.e. a MAS-based smart parking solution using Cloud

services, will bring signiﬁcant beneﬁts to scale up

some of the main MAS features like distribution, de-

centralization and autonomy.

3 CLOUD BASED SPMAS

ARCHITECTURE

The proposed solution is based on a cloud architecture

that integrates a MAS system that uses a society of

agents representing drivers and parking spots to man-

age the parking system operation, as seen in Figure 1.

The use of Cloud technology allows to provide scal-

able applications, better cost-beneﬁt ratio and abstract

hardware speciﬁcations.

Figure 1: Cloud MAS Smart-Parking Architecture.

This architecture uses a three-tier approach:

Clients, who interact with the system via the web or

mobile app; the Cloud, which uses components to en-

sure user authentication, data storage and a scalable

real-time two-way communication in the system; and

the MAS, responsible for managing the entire parking

system in a distributed and intelligent way.

3.1 Multi-Agent System

The multi-agent smart parking system architecture

comprises a set of intelligent, autonomous and proac-

tive agents distributed along the edge and cloud com-

puting layers. Two types of agents were deﬁned,

namely driver and spot agents, which offer more ﬂex-

ibility, modularity, scalability and on-the-ﬂy reconﬁg-

urability for smart parking solutions.

As seen in Figure 2, each parking spot has an

associated spot agent running in the edge computa-

tional layer, being a suitable approach by providing

autonomy and fast response to monitor each vacancy.

Moreover, the spot agents are interconnected with the

physical assets, namely sensors and actuators, aiming

to manage the access of the physical parking spots.

On the other hand, the driver agents, representing the

drivers, are accessed by an UI and are running in the

Integrating a Multi-Agent Smart Parking System using Cloud Technologies

683

cloud, taking advantage of more computational capa-

bilities, including, e.g., the possibility to embed more

robust AI algorithms for intelligent decision support.

Figure 2: Multi-agent Parking System Architecture.

The global functioning of the system emerges

from the interaction between the driver and spot

agents. For this purpose, the agents are endowed with

a set of behaviors to achieve their goals, particularly

the searching for available parking spots, negotiation

strategies, reservation process, and physical intercon-

nection. In this sense, driver agents can dynamically

and in real-time start a negotiation with several spot

agents to ﬁnd and reserve a parking spot respecting

the speciﬁcations deﬁned by the driver (e.g., location,

price and time). The spot agents are also enabled

to initiate the negotiation, recommending free park-

ing spots, e.g., by the inclusion of machine learning

techniques aiming to predict the occupancy of parking

spots, the analysis of the parking use history, the uti-

lization seasonality (e.g., work and vacation period)

and the usage forecast based on the weather forecast.

After the desired parking spot is reserved, the

driver can access the parking spot in the proper slot

of time. The interconnection between the spot agents

and the physical assets is essential to enable the ac-

cesses of the physical spots. In this sense, these cyber-

physical interactions should follow standards like the

recent IEEE 2660.1 (Leit

ao et al., 2021), which rec-

ommends the best practices for integrating software

agents with low-level automation functions. For this

purpose, this interconnection follows a loosely cou-

pled interaction model based on the publish-subscribe

schema, providing the fundamental requirements for

the system’s proper functioning, where the time con-

straints are not critical, and the scalability and moni-

toring are much more signiﬁcant for this application.

Furthermore, the architecture takes advantage of

holonic principles’ recursive capabilities to simplify

the development of large-scale smart parking systems

by building holarchies of driver and spot agents. In

this sense, a spot agent can be at the same time the

“whole”, e.g., representing a set of parking spots and

the “part”, e.g., representing a unique parking spot.

The same is applied for the driver agents, e.g., repre-

senting a company (set of drivers) and a unique driver.

Although an overview of smart parking systems

has been presented, this work focuses on the bene-

ﬁts of the cloud technology to enhance and integrate

MAS solutions, particularly MAS-based smart park-

ing systems. Negotiation methods and principles of

holonic agents are out of the scope from this paper.

3.2 Speciﬁcation of the Cloud

Architecture

A cloud solution must allow drivers to request reser-

vation allocations from the MAS, as well as check the

availability of parking spots in real time, providing

some essential services to the application. First of all,

the backend is constituted by a ﬂexible Authentication

module responsible for the role management and ac-

cess control to the system’s resources. In this sense,

the driver’s requests, e.g., parking spot searches and

reservations, will only be executed if the user is au-

thenticated with the cloud provider.

Based on the data structuring model related to

parking systems provided by FIWARE (FIWARE,

2021), a platform that provides standardized data

models, it was possible to adapt to a document-based

database with NoSQL structure, using essential at-

tributes according to the model studied, as well as

adding new attributes that ﬁt the project. In this ap-

plication, a document-based database is considered to

store information about each parking, as well as users

and their reservations. The main advantages of this

model are the ﬂexibility for new data types in the fu-

ture and the scalability of the database by horizontal

partitioning (e.g. grouping by parking areas).

The back-end platform provides the direct con-

nection between the users and the Cloud service. It

provides fully scalable functionalities for user authen-

tication, storage in NoSQL databases, high perfor-

mance data management and a real-time engine for

notiﬁcations using the WebSocket protocol.

When considering the communication between

the MAS and the Cloud, the Publish-Subscribe model

provides the broadcast of real-time messages (e.g.,

using lightweight and performance protocols like

MQTT) for the multiple devices through several top-

ics, which are the central elements of the entire data

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684

ﬂow. To send a message to agents in the system, it

is necessary for the sender to simply publish a pay-

load containing the data in a topic. In a smart parking

MAS context, topics should cover scenarios in which

the target destination is either spot agents or drivers,

and cover features like search for available spots, re-

quest and ﬁnish a reservation, arriving and departing.

As soon as the message is published, the Cloud plat-

form will act as a message broker, transferring this

payload individually to all devices interested in the

topic through a subscription. This process is impor-

tant to maintain the scalability of the system, enabling

an end-user request (e.g. a search) to be delivered to

all agents in the system in a distributed manner.

There are occasions where a speciﬁc chunk of

code must be executed when a given event occurs in

the Cloud environment, such as adding data to the

database, or publishing a message on a topic, and to

prevent unwanted replication, functions triggered by

those events are used. Due to the execution based on

the occurrence of events, the use of this methodol-

ogy reduces the use of computational processing of

the server, as well as the need to periodically exe-

cute functions. Similar to the use of a cloud-based

Publish-Subscribe service, the use of an event-based

approach on the server enables the high scalability for

the system, since these functions enable the parallel

execution of multiple instances, providing great per-

formance in handling the requests

. Figure 3 shows

the possible events that can trigger these functions.

Figure 3: Event-based functions representation.

For the system operation and its integration with

the cloud platform, several use cases were deﬁned.

User Authentication: Authentication methods are

used to provide secure access of users to the sys-

tem. Registration can be done through the identity

providers, phone numbers or simply by email and

password. The system assigns a unique identiﬁer to

the user, which will be attached to every request. In

addition, a document is created with user’s personal

information in the database, where all the performed

actions are added, e.g., requesting reservations or en-

For example, the Google Cloud Functions service in

https://cloud.google.com/functions

tering and leaving spots. After this process is com-

pleted, the application must allow the user to access

the system using his credentials.

Search Available Spots: The application must of-

fer the option to search for available spots in a given

parking area. All spot agents receive the user’s re-

quest, but only available spots will respond informing

their status. Therefore, the driver agent receives the

responses from the spot agents about their availabil-

ity, displaying the identiﬁer and coordinates of each

available spot.

Request Spot Reservation: It is the most important

functionality of the application, in which the user can

make a request for a reservation (see Figure 4), spec-

ifying parameters that impact on the MAS decision.

These parameters include the desired location, which

can vary depending on geography, and can be related

to sectors of an University or ﬂoors in a Shopping

Center, for example. Informing the type of vehicle

to be used is also important, so that the MAS assigns

the suitable spot for the corresponding type of vehi-

cle. Finally, the user needs to deﬁne the date and time

for the reservation as well as to inform which would

be the desired spot, the maximum acceptable distance

from the spot’s location and the maximum price that

the user is willing to pay.

Figure 4: Schema for requesting a spot.

Note that for the MAS system to come up with a

decision for the best parking spot, through the nego-

tiation among the driver and spot agents, it is neces-

sary that the user informs how much the chosen lo-

cation and price must weight in the agents’ decision.

Weights are mutually exclusive on a 100% scale, i.e.

when selecting 40% weight for one of them, the other

one necessarily correspond to 60%.

The communication ﬂow of this functionality cov-

Integrating a Multi-Agent Smart Parking System using Cloud Technologies

685

ers the services provided in the cloud architecture,

ﬁrstly recording of data in the database, then activat-

ing an event-based function in the cloud, and ﬁnally

publishing the message in a Publish-Subscribe topic.

Enter and Leave Spot: The application must in-

clude the possibility to request to enter or exit the pre-

reserved spot, requiring the interaction with the MAS

system, and particularly with the physical hardware

of a parking spot. Once the spot reservation’s dura-

tion reaches the end, the application must remove the

user’s access to this functionality, so the user can no

longer interact and use it.

4 EXPERIMENTAL

IMPLEMENTATION

The designed cloud-based MAS architecture for

smart parking systems was experimentally imple-

mented and tested in a case study that considers the

bicycle parking system of the Polytechnic Institute of

Braganc¸a (IPB). The IPB parking system comprises

several sectors, where each one constitutes a set of bi-

cycle parking spots. The vacancies are available to

be used with mechanisms that guarantee the bicycle’s

safety and that enables veriﬁcation for available spots.

4.1 Development of the MAS

The MAS-based smart parking system was developed

using the FIPA-compliant JAVA Agent DEvelopment

Framework (JADE) (Bellifemine et al., 2007), and

deployed in different computing layers, namely edge

and cloud layers. For this purpose, the spot agents

were deployed in Raspberry Pis at the edge level, and

the driver agents were deployed in the cloud, commu-

nicating with each other by using the TCP/IP protocol

encoded for the FIPA-ACL (Foundation of Intelligent

Physical Agents-Agent Communication Language).

The communication between the spot agents and the

physical assets was established using the MQTT pro-

tocol, where an exclusive Raspberry Pi was used as

an MQTT broker to support the cyber-physical in-

teractions. Furthermore, each physical asset com-

prises a logical control running in an ESP8266 mi-

crocontroller responsible for managing the access to

the parking spot, e.g., through a latch that will lock

the bicycle in the parking spot and release it after use.

4.2 Cloud Implementation

The cloud provider used in this work was the Google

Cloud Platform (GCP) and was conﬁgured based on

the West Europe location. The Cloud works through

the communication between modules such as the

backend, which is responsible for the communication

between the Client and the Cloud, event-based func-

tions for managing events and ﬁnally, the Publish-

Subscribe service being the bridge between the MAS

and Cloud. The Publish-Subscribe service was imple-

mented through the MQTT protocol.

The protocols used in this communication also in-

clude HTTP, where the client-side does not maintain

an active connection with server-side and data is ex-

changed by sending requests and receiving responses.

This type of protocol is used in most functionalities

when requesting to write or read a document from

the database, such as requesting a reservation, search

for spots, and search for history. On the other side,

a WebSocket is used for real-time message exchange

for situations where the client-side must wait for the

MAS response, such as spot availability, reservation

response, and arriving and departing conﬁrmation.

4.3 Development of the User Interface

The React Native framework was used with the help

of Expo tools to build a mobile application that can

be natively compiled on Android and iOS platforms.

This application is focused on the driver, who must be

able to use the functionalities provided by the smart

parking system in an intuitive and effective way.

After the user authentication, the application re-

quests the available parking areas. Therefore, selec-

tive loading of only data close to the requested loca-

tion occurs, keeping the application light, responsive

and without making requiring signiﬁcant bandwidth,

even with a large number of smart parking places reg-

istered in the system. Based on this, the user can

choose where to park, and when clicking on the mark-

ers at the home page (Figure 5A), a card is shown with

the name and image representing the parking (Fig-

ure 5B). The Check spots button allows to show the

spots that are currently available (Figure 5C).

To request the reservation allocation, the user se-

lects the search parameters, this request is recorded

in the database, activating a function in the cloud that

sends the request to the MAS through the Publish-

Subscribe service and the negotiation and decision

will take place between the agents. After the response

from the MAS, a card is displayed informing the pro-

posal and the user is able to accept or refuse it.

At the end of the reservation process, the applica-

tion schedules notiﬁcations, based on the 30 minutes

prior to the start and end of the reservation, to alert

the user about the reservation. As these are local no-

tiﬁcations, the user will be alerted even if the applica-

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686

Figure 5: Process to search available spots.

Figure 6: Fragments of the UI to perform a reservation.

tion do not have access to the Internet. As shown in

Figure 6A, it is possible to observe more information

about the reservation, as well as open GPS applica-

tions with routes directly to the spot’s location. When

the current time is between the beginning and end of

the reservation, the action of entering the spot is made

available (Figure 6B), being communicated to MAS

in the same way as the reservation request.

5 ANALYSIS OF EXPERIMENTAL

RESULTS

The experimental tests were carried out using the

parking hosted in IPB, focusing the response time and

communication effectiveness for three functionalities

related to MAS, namely the search for available spots,

request for reservations, and entry or exit of spots.

There was no data communication loss reported

in any of the tests, and in all of them the informa-

tion from the mobile applications arrive in the MAS

system, and returned the expected result. This is due

to the Quality of Service (QoS) guaranteed by the

Publish-Subscribe service, which uses delivery recog-

nition methods for each subscriber.

Figure 7: Execution time for searching available spots and

requesting reservations.

In general, the application’s communication with

the back-end module was fast, proving efﬁciency in

cases with local and mobile network connection, and

ﬂexible in case of connection loss. Fig. 7 shows the

average execution time of some system’s functionali-

ties, namely the search for available spots and the re-

quest for reservations. This execution time comprises

the time elapsed from the function call by the user ac-

tivation until the reception of the MAS response and

the visual presentation to the user. The tests were car-

ried out for scenarios comprising 10, 100 and 200

available parking spots. The execution time for the

reservation request functionality was constant, as it

does not depend on the number of spots available at

the moment. On the other hand, the execution time for

the search for spots increases with the raise of avail-

able spots, since the communication also increases.

Summing, the experimental tests clearly showed

that cloud technology is suitable to integrate such sys-

tems, particularly based on distributed components,

as MAS systems are, and provides a technological

solution to support scalability and robustness in such

systems. The used cloud platform approach also con-

tributes to increase authentication and security issues,

as well to integrate databases and UIs.

6 CONCLUSIONS AND FUTURE

WORK

This paper presented a cloud-based MAS architecture

for smart parking systems using Cloud computing

technologies to integrate the MAS system responsi-

ble for the spot allocation and the client-side modules.

Unlike other approaches, this option does not depend

on just one physical server, with Cloud computing al-

lowing an unlimited number of servers that act as an

uniﬁed system. The scalability and decentralization

of the system is guaranteed through the horizontal

scaling, which entails separating a sequential portion

Integrating a Multi-Agent Smart Parking System using Cloud Technologies

687

of the logic into smaller parts so that they can be per-

formed on several machines in parallel, increasing the

efﬁciency of large-scale processes. The security fac-

tor is satisﬁed by the Cloud service provider, which

abstract issues such as implementation of security

protocols, authentication tokens, and possible mali-

cious attacks. The communication between the MAS

and the Cloud was carried out through a Publish-

Subscribe service, which provided a simple integra-

tion between the system logic and physical compo-

nents. Event-based functions were used to provide

asynchronous and stable communication between the

various components of the Cloud. Finally, web and

mobile applications were developed to validate the

user’s interactivity with the system. The mobile app

for drivers was tested in a case study regarding a Uni-

versity campus, being promising, mainly considering

the successful communication between the users and

the MAS, as well as w.r.t the response time, scalabil-

ity and security issues. The implemented cloud based

MAS architecture proved to be ﬂexible, capable of in-

corporating new smart parking modules and integrat-

ing different MAS systems.

Future work will be integrate intelligence algo-

rithms to support the negotiation process during the

allocation of parking spots to drivers. Regarding the

integration with the MAS, it can be explored features

that enable the agent management via a web interface.

ACKNOWLEDGMENTS

This work has been supported by FCT - Fundac¸

para a Ci

encia e Tecnologia within the Project Scope:

UIDB/05757/2020.

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