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Use of Gamification Techniques to Encourage Garbage Recycling. A Smart City Approach: 13th International Conference, KMO 2018, Žilina, Slovakia, August 6–10, 2018, Proceedings


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One of the main problems in urban environments is the accumulation of waste. A large percentage of waste can be recycled, preventing it from being deposited at landfills or uncontrolled sites, contributing to air, soil and water pollution. Although today’s systems collect and separate different types of waste, a large part of waste is not disposed at recycling centres. In order to increase the amount of waste that is recycled, it is necessary to motivate our society to become involved in recycling activities. For this reason, this paper proposes a system that encourages citizen participation by obtaining reductions in the waste rate applied by their local government, so that the amount of waste collected to be recycled is increased. This system employs a virtual organization of agents that obtains and manages the knowledge of each city and that through gamification techniques produces a change in the habits of citizens, motivating and increasing citizen participation in recycling regardless of the urban environment in which it is implemented. A case study was carried out in order to evaluate the performance of the proposed system, the results show that citizen participation would increase by 32.2% and the amount of recycled waste would increase by 17.2%.
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Use of gamification techniques to encourage garbage
recycling. A Smart City approach.
Alfonso González Briones1, Pablo Chamoso1, Alberto Rivas1, Sara Rodríguez1, Fernando De
La Prieta1, Javier Prieto1 and Juan M. Corchado1,2,3
1BISITE Digital Innovation Hub, University of Salamanca. Edificio Multiusos I+D+i, 37007,
Salamanca, Spain
{alfonsogb, chamoso, rivis, srg, fer, javierp, corchado}
2Osaka Institute of Technology, Osaka, Japan
3 Universiti Malaysia Kelantan, Kelantan, Malaysia
Abstract. One of the main problems in urban environments is the accumulation
of waste. A large percentage of waste can be recycled, preventing it from being
deposited at landfills or uncontrolled sites, contributing to air, soil and water pol-
lution. Although today's systems collect and separate different types of waste, a
large part of waste is not disposed at recycling centres. In order to increase the
amount of waste that is recycled, it is necessary to motivate our society to become
involved in recycling activities. For this reason, this paper proposes a system that
encourages citizen participation by obtaining reductions in the waste rate applied
by their local government, so that the amount of waste collected to be recycled is
increased. This system employs a virtual organization of agents that obtains and
manages the knowledge of each city and that through gamification techniques
produces a change in the habits of citizens, motivating and increasing citizen par-
ticipation in recycling regardless of the urban environment in which it is imple-
mented. A case study was carried out in order to evaluate the performance of the
proposed system, the results show that citizen participation would increase by
32.2% and the amount of recycled waste would increase by 17.2%.
Keywords: Behavioral change; Serious games; Context-awareness; Social
computing; Multi-agent system
1 Introduction
Unusable waste is a problem for many societies due to the large amount of waste that
is produced and the absence of a destination to maintain it sustainably. It is a major
problem that affects not only those living in large cities but the entire population of the
planet. The increase in population, the development of modern human activities and
high consumerism have all greatly increased the amount of waste produced. This in-
crease in the growth of waste production together with the inefficient handling of such
waste (open burning or storage in garbage dumps, among others) causes problems such
as water, soil and air pollution, this triggers health problems and damages to the envi-
ronment, including social and political conflicts.
One of the solutions that were implemented to reduce pollution was to adopt
measures that would allow the recycling of this waste. In this sense, many countries
began to build urban waste treatment plants to manage the garbage generated in metro-
politan areas. At the beginning of their implantation, these plants only allowed to recy-
cle some residues such as paper and cardboard, glass or some plastic components, al-
lowing the recycling of new elements (oil, tyres, electronic products, etc.) as the
knowledge and techno-logy in this field has evolved. However, in order for these plants
to perform their recycling function, it was necessary to develop a waste collection net-
work. There is no common method of waste management in the European Union. Some
countries have deployed a series of colorful containers in which the user introduces
waste according to its typology, in other countries it is in the supermarkets where the
consumer is allowed to deliver a series of waste and packaging for recycling. The use
of these measures has made it possible to recover many tons of garbage that were pre-
viously disposed at landfills, obtaining numerous benefits for both man and the envi-
ronment.Although these measures have been widely accepted, there are still many tons
of recyclable materials that end up at landfills without any reuse. The rate of glass re-
cycling in the European Union reached 73% in 2013 according to The European Con-
tainer Glass Federation (FEVE) [0]. The recycling rate for paper/cardboard is around
83%, plastic around 34.3% and wood around 37.7% [5][25]. This shows that the glass
and paper/cardboard recycling model works well, but a higher recycling rate for the
remaining materials needs to be achieved.
One of the ways that would help to increase the recycling rate would be a more active
participation of citizens in the recycling chain. One of the ways in which greater citizen
participation can be achieved is by providing benefits to citizens who participate more
actively in the recycling chain. The concept of gamification is a technique that allows
the recycling process to be stimulated and carried out in a more dynamic way so that
certain results are achieved. For this reason, it is necessary to develop a system that
increases the recycling rate of all materials through greater citizen participation. The
objective of this paper is to provide a new multi-agent context-aware which grants eco-
nomic benefits, promoting citizen participation. In this way, the amount of waste col-
lected in the recycling chain increases.
CAFCLA (Context-Aware Framework for Collaborative Learning Applications)
was used to develop the proposed system, this framework provides a basis for the im-
plementation of its technical and social features [7]. The system will learn from the
actions taken by citizens to adapt to them and provide new solutions that will increase
recycling rates and citizen participation. To this end, the system is developed using the
multiagent systems paradigm (MAS) for learning user actions and decision making.
This article is organized as follows: section 2 describes the state of the art of multi-
agent systems and gamification, Section 3 describes the proposal, Section 4 presents
the results and Section 5 conclusions.
2 Background
This section reviews the different methods that are currently implemented for waste
recycling in the European Union, detailing their characteristics, advantages and disad-
vantages. It also explains the current state of the techniques used by the proposed sys-
tem for its adoption within the problem of waste recycling, and how these techniques
will help us to increase citizen participation.
2.1 Recycling methods in the European Union
To prevent many tons of recyclable materials from ending up at landfill sites, differ-
ent ways of collecting waste were developed for subsequent recycling. In 1982, the first
glass container was installed in Spain. That year, collaboration began between the Au-
tonomous Communities, local authorities and manufacturers of glass containers for the
recycling of this material. In 1994, Directive 94/62/EC sets out the framework within
which all legislation in each of the European Community countries will be developed.
Within the European Community there are mainly two models in the recycling chain.
The first method consists of various colored containers deployed in cities and urban
environments for each citizen to deposit their waste, each colored container indicates
the type of waste to be introduced. In Spain, this method consists of three containers:
blue for paper and cardboard, yellow for plastic waste and green for glass. There is also
a container for organic waste as well as clean points for collecting oil, tyres and elec-
tronic waste. In France there are five containers with different colours and symbols to
distinguish them at a glance. In addition, there is an annual schedule to follow, which
sets the days for littering, usually two days a week. Citizens have to follow this schedule
to know on what day the garbage truck passes, this allows them to take out their rubbish
the night before. A citizen who does not recycle receives a fine. In Italy, the recycling
system is also based on colours, although the colour of the containers varies in compar-
ison to Spain and France. The citizen who does not recycle can also receive a fine and
the collection of waste also follows a plan. In Norway the system is similar, with one
container for organic waste and another for paper and cardboard. The plastic is placed
in a special bag placed next to the paper and cardboard container. The glass is left in
special containers next to the supermarkets. The collection is carried out following a
schedule set by the town council of each locality. The second method is Deposit Refund
System (DRS) which is used in Germany, Sweden and Denmark. In this method, citi-
zens pay a tax when they buy a container, which is returned once the package is depos-
ited in perfect condition in a series of machines located in the supermarkets where they
bought it. Customers receive a voucher that can be exchanged for cash, receiving 0.25€
for each bottle. This method in some countries such as Germany is found along with
the method of color containers.
Although the two main models are widely accepted, these methods are not as effi-
cient as our cities want to become a real intelligent city of the future. Both the colour
container model and the DRS model have several drawbacks. The main one is that it
can only be used to recycle water, soft drinks and beer containers, whether plastic or
metal containers. Any other type of packaging is not accepted. In total, it handles 8%
of the packaging, while in the colour container model, almost 80% of the waste can be
managed through yellow and blue containers. This model normally works through su-
permarkets, rather than containers, so that citizens can only come to deposit their pack-
aging when the stores are open. Another major drawback of DRS is that, when buying
a product, the citizen has to pay for the packaging. This packaging will only be refunded
if the packaging is returned in perfect condition (in case of dent, for example, the ma-
chine rejects it). Thus, the citizen is not assured that their money will be returned.
2.2 Gamification as a technique for behaviour change in recycling
Games have always been associated with training and a playful way of spending
time, although they have also been used in educational tasks. These games employ the
typical mechanisms of a training game with a purpose related to learning, understand-
ing or social impact, addressing both cognitive and affective dimensions. Dynamics
and concepts that stimulate and make the player's interaction with the learning process
more attractive.
The term employed to describe the use of a game for an educational purpose as
opposed to entertainment, is called gamification. This technique allows city
dwellers to feel that by recycling they are participating in a game, in which
they receive a reward when they do it well and receive penalties if they do not.
This allows for higher levels of societal commitment in this area and stimu-
lates the development of habits that are environmentally friendly. Although
this technique has been used in other fields such as energy efficiency with very
favorable results [6], [7], currently in the field of recycling no system has been
developed that would use this technique to encourage citizen participation and
with it the increase in the amount of waste deposited for recycling, so it is
interesting to evaluate whether this technique produces satisfactory results in
this area [22].
2.3 Multi-agent systems for data collection, action learning and decision
Multi-agent systems (MAS) have been used in a variety of contexts because of their
ability to model behaviors, simulate situations or solve problems that are difficult or
impossible to solve for an individual agent or a monolithic system [11][10]. Due to the
multiple characteristics of this type of systems that allow agents to communicate, co-
ordinate, interact and cooperate for the realization of different activities, they have been
applied in works with varied objectives, such as the obtaining of genes whose behavior
patterns signify of particular diseases [12][9][16][20], detection of drivers under the
influence of drugs [1], facial image classification according to gender and age [13], and
all of them performed well. In addition, the autonomy of agents in virtual agent organ-
izations to interact with each other without any need for user action or the ability to
perceive and react to changes in the environment makes it an ideal approach to data
collection, learning behavior patterns and decision making for certain actions that can
occur. Characteristics such as extensibility, flexibility imply the possibility of adding
new functionalities such as the inclusion of new algorithms or new infrastructure that
is managed by MAS. This is why multi-agent systems have been used in different pro-
posals within the field of recycling, from simulation of behaviour, learning of recycling
habits, efficient waste management within the supply chain or learning of user behav-
iour actions.
Meng et al. proposed a system that simulates the separation and recycling of solid
household waste along with social surveys [14]. The system is based on a multi-agent
system in which three agents simulate behavior and decision making under two set
condition scenarios. In this work, a multi-agent system is used that simulates behavior
under various conditions but does not encourage citizen participation in recycling tasks.
Another work employing agent-based technology is that proposed by Yang et al. in
which the economic sustainability of an agent-based simulation-based waste recovery
system is assessed [26]. In this work, in addition to agents, sustainability metrics were
used to evaluate and compare two recovery processes to determine their economic sus-
tainability, recycling rate in the process and economic efficiency. In the work con-
ducted by Mishra et al. a multi-agent system makes autonomous judgments for the ef-
fective recycling of waste. System agents coordinate effectively to perform different
tasks such as waste categorization, transport, waste recycling, waste management and
allocation of reusable products [15]. As many of the tasks involved in the supply chain
are very complex, the use of a multi-agent system allows them to manage them effec-
tively by being able to cooperate and communicate between the different agents that
make up the supply chain. In addition, agents continually learn from past experiences
to make effective judgments in the future. The proposed framework will therefore con-
tribute to effective decision making, from the collection of scrap to the distribution of
recycled components and manufacturing using the environment of the green supply
One of the great advantages of using virtual agent organizations to model this type
of problems is the possibility of programming agents with the capacity to learn action.
This ability allows them to recognize behaviors through the repetition of certain actions
[3][4][24]. In addition, within the existing multi-agent architectures there are a series
of shortcomings in learning and decision-making aspects. Therefore, it is necessary to
develop a multi-agent architecture that adapts to the needs posed in our work (integra-
tion of different devices, distributed services, applications). For this reason, the adop-
tion of virtual agent organizations makes it possible to develop distributed systems de-
ployed in different environments for common management. This is necessary because
an agent-based system will manage the waste collection services distributed within a
Smart City. In addition, this architecture allows for the use of applications and services
that can communicate in a distributed way, even from mobile devices, regardless of the
programming language or operating system they incorporate. The above typology will
be used together with the model proposed by Rodríguez et al. [18], whose main novelty
lies in its dynamic and adaptive planning abilities which allow for the effective distri-
bution of tasks among the organization's member agents. This will make it possible to
distribute the tasks among the agents in charge of managing the data of each container.
3 Proposed Architecture
This section details the technical side of the architecture. It describes aspects related
to the required infrastructure to install the system in a smart city (data collection by
sensors, data transmission) and the gamification system of citizen participation in the
recycling process.
3.1 Required Infrastructure
In order for our system to know when each user recycles, it is necessary to derive an
infrastructure that allows us to determine the quantity, the type of waste deposited, the
user that recycles, the state of filling of the city's containers, and the occupancy rate of
the nearest waste treatment plant. The necessary infrastructure does not imply a change
in the current model of coloured containers that is implemented in several countries
(France, Italy, Norway, Spain, etc.). However, it will be necessary to incorporate in the
containers (i) a QR code reader to identify the user, this code will be provided by its
urban environment. (ii) GPS Locator, indicates the coordinates in which the container
is located [17]. (iii) Weight sensor, weighs the material introduced by the citizen. (iv)
Volumetric sensor shall measure the filling status of the container. (v) NarrowBand
IOT (NB-IoT) comunication technology, for data transmission to MAS. The choice of
this communication technology is due to the need to communicate information over
long distances. This type of LPWAN network has been chosen using ICM bands to
reach long distances, up to 50 km in rural areas and between five and ten km in urban
areas. (vi) Solar panel, which feeds the various sensors deployed in the container so
that the system is energy independent.
When a citizen inserts a waste bag into the corresponding container, a data structure
is generated with the following information: user id, type of waste, amount of waste,
container id, location of the container, container filling status. This data structure is sent
via the MQTT protocol using the NB-IoT network to a local station. The local station
will act as a MQTT Broker sending the data in JSON format through REST services
[19]. Communication also exists between the deployed container infrastructure and the
urban waste treatment plant so that once a container is full, the waste recycling plant
sends a truck to collect and transport the waste to that plant.
3.2 Virtual agent organization Architecture
As the proposed system requires the deployment of an associated infrastructure
which may undergo changes. These changes may include an increase in the number of
containers or their location, etc., it is necessary for the management system to be a
highly dynamic platform employing self-adapting capabilities at runtime. Thus, each
agent's behavior is determined by the goals it wishes to achieve (amount of waste to be
recycled, participation percentage, etc.), however it also considers the goals of other
agents and any changes that may arise in the environment. The core of the architecture
is a group of deliberative agents that act as controllers and administrators of all appli-
cations and services. The functionalities of the agents are not within their structure,
instead they are modeled as services. This approach provides greater error-recovery
capability and greater flexibility to change agent behavior at runtime [21].
The architecture has been developed for the incorporation of multiple heterogeneous
sensors, so that functionality can grow in the future for the incorporation of new ser-
vices, as explained in the methodology developed by Dante et al. [23]. Pangea has been
chosen for the development of virtual agent organizations [27]. PANGEA is a better
option than other agent framework technologies because it provides a number of agents
that encapsulate elementary functionality, such as data access, service discovery [2] or
rule control, and supports standard inter-agent communication protocols. Pangea incor-
porates agents that manage security at the system level, unlike other types of systems
in which it is necessary to develop this type of measures so that the information col-
lected is really the one that is transmitted and analyzed [8]. In this way, the developer
can no longer worry about these modules and it is PANGEA that is responsible for
defining their function. Thus, the developer's efforts are directed primarily at the func-
tional part of the system they want to implement. The system is specifically designed
to analyze sensor data and include data from external in-formation sources.
Fig. 1. Proposed architecture based on virtual agent organizations.
The virtual agent organization system distributes the operation of the agents that
make up the system and their roles are assigned; grouped in three layers according to
the activities performed by each one as shown in Fig. 1.
Infrastructure VO. This virtual organization is made up of basic agents that act as
middleware between users and the system. Each container is associated with an
agent that generates a data structure (cannon, deposited type of waste, user that re-
cycles, filling status of the container, occupancy rate of the nearest waste treatment
plant). LPWAN agents send the information to Data collection and transmission VOI
Data collection and transmission VO. The agents in this layer are responsible for
collecting data from the infrastructure deployed in a Smart City. IoT agents com-
municate with agents deployed in the VO Infrastructure to build the data structure
that collects the data on waste deposit in a container, which is sent in JSON format
through REST services. IoT Broker performs the NGSI-to-NGSI (Next Generation
Services Interface) conversion between IoT agents and the Context Broker agents,
and the data is collected by Data Communication agents which transmits it for anal-
ysis to Smart City Intelligence VO.
Smart City Intelligence VO. Once the data structure of each recycling process has
been received, it is checked whether the container is full and the decision is made to
send a truck to collect the waste. In the next stage Big Data Engine Agents analyze
the data using Complex Event Processing (CEP) for pattern recognition. The system
has Open Data Agents that incorporate mechanisms for the incorporation of data that
may be relevant for analysis, such as information on temperature or the weather
forecast so that more information is available for more effective decision making.
Smart Govermenet VO. This VO manages the results obtained from the analysis
performed in the Smart City Intelligence VO. On the basis of this analysis, contain-
ers that are full are collected, more containers are moved to areas where a greater
amount of garbage is produced or garbage trucks go to these areas more frequently
to collect the waste. This layer is in charge of rewarding or penalizing the user by
decreasing or increasing the garbage fee that must be paid to the local government.
4 Results
The experiment was divided into two phases and took place between August and
November 2017. The first phase of the experiment was carried out during July and
August, this phase measured the number of people that recycle and the amount of waste
collected in the different recycling containers. The second phase took place during the
months of September and October, at this stage the data was acquired exclusively
through the developed virtual organization-based system.
Fig. 2. Screenshot of a user profile involved in the experiment
The evaluation of the prototype was carried out in an urbanization outside the urban
nucleus of Zaragoza with an estimated population of 2200 inhabitants, in which the
sound system kit was deployed in thirty containers (ten blue paper and cardboard con-
tainers, ten yellow plastic waste containers and ten green glass containers), with the
collaboration of the city's urban waste collection company.
In the second phase, users had to download the Android mobile application, Fig. 2,
to log in into the system when they were going to deposit waste in the recycling bins.
The process consists of the user once the application is downloaded to the mobile ter-
minal, the user can read the QR code that identifies the container and identifies the user
in such a way that the opening of the waste deposit and weighing container is enabled.
Once the waste is introduced in the container, the generated data structure is sent (waste
quantity, type of waste deposited, user recycling, container filling status, occupancy
rate of the nearest waste treatment plant) through the local relay antenna deployed using
LPWAN. The information reaches the system and the agents of the different virtual
organizations are responsible for making decisions such as sending a truck to collect
the waste from a specific container if it is full or updating the user's profile. The mobile
application allows to visualize the achievements that the user has accumulated during
the month (every month the profile is restarted, achievements are not cumulative for
the next month). If the user reaches the goals proposed by the city council, in the case
study it was proposed to increase the amount of waste by 18%. The users who achieved
this obtained a reduction of 5€ on the monthly waste rate (48€). At the end of the ex-
periment, the amount of waste deposited in the containers has been measured in such a
way that the efficiency of the system is measured, as can be seen in Table 1. In which
an increase of 17.2% is observed.
Table 1. Quantity (Kg) of waste collected from each container
Before System
After System
Blue Container
Yellow Container
Green Container
The system also allowed us to detect the ages of the users who have participated in
the experiment, so that the local government can obtain data on the age ranges of the
inhabitants who least recycle and to promote social awareness campaigns. Table 2
shows the sample of participants in the second phase. The system's functionality al-
lowed the system to be used as a tool for social measurements and learning about users'
behavioural patterns (average amount of waste deposited, frequency with which it goes
to containers, timetables, days of the week, etc.).
Table 2. Distribution of the users who participated in the case study distributed by age range
Blue Container
Yellow Container
Green Container
5 Conclusions
This work presented an innovative approach based on Virtual Organizations (VO)
of agents to increase citizen participation in recycling tasks and thus increase the
amount of recycled waste. To this end, our proposal consists of gamifying the recycling
process in such a way that rewards are offered to users according to the amount of waste
deposited in the container. For this purpose, it was necessary to deploy a small infra-
structure in each recycling container to obtain data every time a user deposited waste
in a container (amount of waste, type of waste deposited, user that recycles, state of
filling of the container, occupancy rate of the nearest waste treatment plant).
In the conducted case study, it was demonstrated how our proposal offers an inno-
vative method in the field of recycling that has made people aware of recycling. The
results of the case study confirms this statement, since citizen participation increased
by 32.2% and the amount of waste recycled increased by 17.2% in comparison to the
data obtained before the implementation of our system. At the social level, the system
has allowed us to identify that people between the ages of 43 and 56 have been more
involved in the experiment. It is proposed in the next version of this work to make
partial achievements, so that the benefit for each user depends on the amount of waste
This work has been supported by project ``MOVIURBAN: Máquina social para la
gestión sostenible de ciudades inteligentes: movilidad urbana, datos abiertos, sensores
móviles". SA070U 16. Project co-financed with Junta Castilla y León, Consejería de
Educación and FEDER funds
The research of Alfonso González-Briones has been co-financed by the European
Social Fund (Operational Programme 2014-2020 for Castilla y León, EDU/310/2015
1. Briones, A. G., González, J. R., & de Paz Santana, J. F. (2015). A drug identification system
for intoxicated drivers based on a systematic review. ADCAIJ: Advances in Distributed
Computing and Artificial Intelligence Journal, 4(4), 83-101.
2. Chamoso, P., De la Prieta, F., De Paz, F., & Corchado, J. M. (2015). Swarm agent-based
architecture suitable for internet of things and smartcities. In Distributed Computing and
Artificial Intelligence, 12th International Conference (pp. 21-29). Springer, Cham.
3. Costa, Â., Barberá, S. M. H., Cámara, J. P., Novais, P., & Inglada, V. J. J. (2016). Persuasion
and recommendation system applied to a cognitive assistant. ADCAIJ: Advances in Distrib-
uted Computing and Artificial Intelligence Journal, 5(2), 89-99.
4. De La Prieta, F., Navarro, M., García, J. A., González, R., & Rodríguez, S. (2013). Multi-
agent system for controlling a cloud computing environment. Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics) (Vol. 8154 LNAI).
5. Eurostat, Statistics Explained. (2018). Waste statistics. Available at:
rostat/statistics-explained/index.php/Waste_statistics [Accessed 19 Dec. 2017].
6. García, Ó., Alonso, R. S., Prieto, J., & Corchado, J. M. (2017). Energy Efficiency in Public
Buildings through Context-Aware Social Computing. Sensors, 17(4), 826.
7. García, O., Chamoso, P., Prieto, J., Rodríguez, S., & de la Prieta, F. (2017, June). A Serious
Game to Reduce Consumption in Smart Buildings. In International Conference on Practical
Applications of Agents and Multi-Agent Systems (pp. 481-493). Springer, Cham.
8. González Briones, A., Chamoso, P., & Barriuso, A. L. (2016). Review of the Main Security
Problems with Multi-Agent Systems used in E-commerce Applications.
9. González, A., Ramos, J., De Paz, J. F., & Corchado, J. M. (2015). Obtaining relevant genes
by analysis of expression arrays with a multi-agent system. In 9th International Conference
on Practical Applications of Computational Biology and Bioinformatics (pp. 137-146).
Springer, Cham.
10. González-Briones, A., Chamoso, P., Yoe, H., & Corchado, J. M. (2018). GreenVMAS: Vir-
tual Organization Based Platform for Heating Greenhouses Using Waste Energy from Power
Plants. Sensors, 18(3), 861.
11. González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., & Corchado, J. M.
(2018). Energy Optimization Using a Case-Based Reasoning Strategy. Sensors, 18(3), 865.
12. González-Briones, A., Ramos, J., De Paz, J. F., & Corchado, J. M. (2015). Multi-agent Sys-
tem for Obtaining Relevant Genes in Expression Analysis between Young and Older
Women with Triple Negative Breast Cancer. Journal of integrative bioinformatics, 12(4), 1-
13. González-Briones, A., Villarrubia, G., De Paz, J. F., & Corchado, J. M. (2018). A multi-
agent system for the classification of gender and age from images, Computer Vision and
Image Understanding,
14. Meng, X., Wen, Z., & Qian, Y. (2018). Multi-agent based simulation for household solid
waste recycling behavior. Resources, Conservation and Recycling, 128, 535-545.
15. Mishra, N., Kumar, V., & Chan, F. T. (2012). A multi-agent architecture for reverse logistics
in a green supply chain. International Journal of Production Research, 50(9), 2396-2406.
16. Oliveira, T., NEVES, J., & Novais, P. (2012). Guideline formalization and knowledge rep-
resentation for clinical decision support. ADCAIJ: Advances in Distributed Computing and
Artificial Intelligence Journal, 1(2), 1-11.
17. Prieto, J., Mazuelas, S., Bahillo, A., Fernández, P., Lorenzo, R. M., & Abril, E. J. (2010).
On the minimization of different sources of error for an RTT-based indoor localization sys-
tem without any calibration stage. In Indoor Positioning and Indoor Navigation (IPIN), 2010
International Conference on (pp. 16).
18. Rodríguez, S., de Paz, Y., Bajo, J., & Corchado, J. M. (2011). Social-based planning model
for multiagent systems. Expert Systems with Applications, 38(10), 13005-13023.
19. Román, J. A., Rodríguez, S., & de da Prieta, F. (2016). Improving the distribution of services
in MAS. Communications in Computer and Information Science (Vol. 616).
20. Silva, A., Oliveira, T., Neves, J., & Novais, P. (2016). Treating Colon Cancer Survivability
Prediction as a Classification Problem. ADCAIJ: Advances in Distributed Computing and
Artificial Intelligence Journal, 5(1), 37-50.
21. Silveira, R. A., Comarella, R. L., Campos, R. L. R., Vian, J., & de la Prieta, F. (2015).
Learning objects recommendation system: issues and approaches for retrieving, indexing
and recomend learning objects. ADCAIJ: Advances in Distributed Computing and Artificial
Intelligence Journal, 4(4), 69-82.
22. Silveira, R. A., da Silva Bitencourt, G. K., Gelaim, T. Â., Marchi, J., & de la Prieta, F.
(2015). Towards a model of open and reliable cognitive multiagent systems: dealing with
trust and emotions. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence
Journal, 4(3), 57-86.
23. Tapia, D. I., Alonso, R. S., De Paz, J. F., & Corchado, J. M. (2009, June). Introducing a
distributed architecture for heterogeneous wireless sensor networks. In International Work-
Conference on Artificial Neural Networks (pp. 116-123). Springer, Berlin, Heidelberg.
24. Tapia, D. I., Fraile, J. A., Rodríguez, S., Alonso, R. S., & Corchado, J. M. (2013). Integrating
hardware agents into an enhanced multi-agent architecture for Ambient Intelligence sys-
tems. Information Sciences, 222, 4765.
The European Container Glass Federation. (2017). FEVE PR recycling 2013. Available at: [Accessed 19 Dec.
25. Wood woRking INdustry RecycliNG (2017). Waste statistics. Available at:
cling [Accessed 19 Dec. 2017].
26. Yang, Q. Z., Sheng, Y. Z., & Shen, Z. Q. (2011, December). Agent-based simulation of
economic sustainability in waste-to-material recovery. In Industrial Engineering and Engi-
neering Management (IEEM), 2011 IEEE International Conference on (pp. 1150-1154).
27. Zato, C., Villarrubia, G., Sánchez, A., Bajo, J., & Corchado, J. M. (2013). PANGEA: a new
platform for developing virtual organizations of agents. International Journal of Artificial
Intelligence, 11(A13), 93-102.
... For meet this need, but also the increasingly push towards a Smart City model, the research moved over the years towards prototypes and applications on smart bin system [15], [8] to manage the waste collection. One of the ways to achieve greater involvement of citizens consist in granting benefits to whom is participating more actively in the recycling chain [2]. These reward systems typically involves citizens through dedicated smartphone app only when they interacts with the smart bins. ...
... Despite gamification approaches leveraging the smart bin model have already been largely studied [15], [8], [2], over the years the municipalities' trend is becoming increasingly oriented on the door-to-door waste management model. Imposed by European restrictions and regulations whose aim is to reduce the environmental and health impacts of waste and improve Europe's resource efficiency. 2 Moreover, among all the strategies and solutions applied, studies suggest that the door-to-door collection model works better than the collection using disposal bins [3]. ...
... Imposed by European restrictions and regulations whose aim is to reduce the environmental and health impacts of waste and improve Europe's resource efficiency. 2 Moreover, among all the strategies and solutions applied, studies suggest that the door-to-door collection model works better than the collection using disposal bins [3]. We created a system that rewards the effort of people based on the amount of recycling they produce. ...
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Engagement is a common goal pursued by most social and technical systems, because of its widely acknowledged effects on enhancing user acceptance and performance. Previous research has shown that a system’s ability to engage users involves two known aspects: the technology foundation that determines the interactive paths for engaging users and the design methodology that determines the atop user experience to be conveyed through those paths. In recent years, an emerging and promising engagement approach that integrates both an advanced technology stack and novel design methodology, i.e. IoT-enabled Gamification (IeG), has attracted wide interest from both public and private sectors. This article aims to conduct a systematic review to answer some fundamental questions. 75 papers were reviewed under a 3-axis analysis framework of user engagement, the majority of which indicated that IeG is linked to increased engagement in a variety of application domains, stages, and population scales.
MaaS, with its user-centric vision aiming at more flexible, personalized, and on-demand mobility services, has a perspective to address the key sustainability challenges of the mobility of the future: transport solutions that are integrated, inclusive, and time-efficient, while containing the economic, social, and environmental costs. Civic engagement is a key success factor towards this objective: to fully embrace MaaS transformation, citizens need to feel part of the process. Gamification proved to be effective to raise citizens' awareness, encourage their participation, and promote a gradual but profound behavior change. These characteristics can be exploited by MaaS solutions to attract users and encourage the acceptance of tailored mobility plans. The chapter presents two successful cases of gamified systems aiming at promoting a more sustainable mobility, Play&Go and Kids Go Green; investigates the potential of gamified systems, in combination with MaaS solutions, in terms of citizens' engagement and behavior change; and discusses current limitations and future challenges.
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At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.
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The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user's energy bill is greatly reduced with the implemented system.
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Triple negative breast cancer is an aggressive form of breast cancer. Despite treatment with chemotherapy, relapses are frequent and response to these treatments is not the same in younger women as in older women. Therefore, the identification of genes that cause this difference is required. The identification of therapeutic targets is one of the sought after goals to develop new drugs. Within the range of different hybridization techniques, the developed system uses expression array analysis to measure the expression of the signal levels of thousands of genes in a given sample. Probesets of Gene 1.0 ST GeneChip arrays provide categorical genome transcript coverage, providing a measurement of the expression level of the sample. This paper proposes a multi-agent system to manage information of expression arrays, with the goal of providing an intuitive system that is also extensible to analyze and interpret the results. The roles of agent integrate different types of techniques, statistical and data mining methods that select a set of genes, searching techniques that find pathways in which such genes participate, and an information extraction procedure that applies a CBR system to check if these genes are involved in the disease.
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The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.
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The ability to connect to the Internet from a wide variety of devices such as smart phones, IoT devices and desktops at anytime and anywhere, produces a large number of e-commerce transactions, such as purchases of clothes, ticket entrances for performances, or banking operations. The increasing number of these transactions has also created an increase in the number of threats and attacks by third parties to access user data banks. It is important to control the access procedure to user data so that the number of threats does not continue to grow. To do so, it is necessary to prevent unauthorized access, theft and fraud in electronic commerce, which is required to ensure the safety of these transactions. Many e-commerce platforms are developed through multi-agent-systems because they include certain advantages to control the product, resource management, task distribution, etc. However, there are a number of threats that can jeopardize the safety of the agents that make up the system. These issues must be taken into account in the development of these multi-agent systems. However, existing methods of development do not cover in depth the issue of security. It is necessary to present and classify the potential security flaws of multi-agent systems. Therefore, the present research presents a review of the main vulnerabilities that occur in multi-agent systems responsible for managing e-commerce applications, as well as the proposed solutions to the major security problems on these platform systems. The main conclusions provided by this research is the need to optimize security measures and enhance the different security solutions applied in e-commerce applications in order to prevent identity theft, access to private data, access control, etc. It is therefore essential to continue to develop the security methods employed in applications such as e-commerce as different types of attacks and threats continue to evolve.
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Drug use; Signs of drug use; Sobriety checkpoint, Review Tests to detect the use of illegal substances among drivers are becoming more common. During these tests, a saliva test is performed and agents observe the driver to determine whether or not they are driving under the influence of psychoactive substances. During the joint control of alcohol and drugs, a breath test is performed followed by a saliva test. In addition, agents use a previously established observation questionnaire to evaluate external signs that the driver may present. This review aims to help expand and improve the questionnaire administered by the traffic officer to the driver, so that upon completing the questionnaire as indicated , it is possible to determine which drug corresponds to the symptoms displayed by the driver. The diagnosis will be facilitated by a software tool that employs the use of decision trees whose gain function has been modified to give different weights to signs and methods. A study was conducted on the symptoms and observable and/or easily detectable signs of drugs detected at sobriety checkpoints. This has enabled the creation of a test that determines the substance consumed by the driver. The proposal facilitates the detection of drugs with the data gathered from the test.
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This work presents a survivability prediction model for colon cancer developed with machine learning techniques. Survivability was viewed as a classification task where it was necessary to determine if a patient would survive each of the five years following treatment. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Six features were extracted from a feature selection process in order to construct the model. This model was compared with another one with 18 features indicated by a physician. The results show that the performance of the six-feature model is close to that of the model using 18 features, which indicates that the first may be a good compromise between usability and performance.
The automatic classification of human images on the basis of age range and gender can be used in audiovisual content adaptation for Smart TVs or marquee advertising. Knowledge about users is used by publishing agencies and departments regulating TV content; on the basis of this information (age, gender) they are able to provide content that suits the interests of users. To this end, the creation of a highly precise image pattern recognition system is necessary, this may be one of the greatest challenges faced by computer technology in the last decades. These recognition systems must apply different pattern recognition techniques, in order to distinct gender and age in the images. In this work, we propose a multi-agent system that integrates different techniques for the acquisition, preprocessing and processing of images for the classification of age and gender. The system has been tested in an office building. Thanks to the use of a multi-agent system which allows to apply different workflows simultaneously, the performance of different methods could be compared (each flow with a different configuration). Experimental results have confirmed that a good preprocessing stage is necessary if we want the classification methods to perform well (Fisherfaces, Eigenfaces, Local Binary Patterns, Multilayer perceptron). The Fisherfaces method has proved to be more effective than MLP and the training time was shorter. In terms of the classification of age, Fisherfaces offers the best results in comparison to the rest of the system’s classifiers. The use of filters has allowed to reduce dimensionality, as a result the workload was reduced, a great advantage in a system that performs classification in real time.
Conference Paper
The research described in this paper presents a social system where intelligent management helps control energy consumption in buildings. In this work we used the CAFCLA framework, which makes it possible to combine various technologies that simplify the creation of context-awareness and social computing systems. The created system is capable of influencing user behavior in a way that favors the efficient management of energy resources in the workplace. This is achieved by merging a number of techniques; Wireless Sensor Networks and Real-Time Locating Systems, along with the use of Collaborative Learning and Virtual Organizations of Agents. These artificial intelligence (AI) techniques provide a great potential for the development of serious games that foster the acquisition of good energy saving habits among workers and users in public buildings.
In this paper, we present a persuasive recommendation module included in the iGenda framework. iGenda is a cognitive assistant that helps care-receivers and caregivers in the management of their activities of daily living, by resolving scheduling conflicts and promoting active aging activities. The proposed new module will allow the system to select and recommend to the users an event that potentially best suits to his/her interests (likes or medical condition). The multi-agent approach followed by the iGenda framework facilitates an easy integration of these new features. The social objective is to promote social activities and engaging the users in physical or psychological activities that improve their medical condition.