The 13th International Conference on Mobile Computing and Ubiquitous Network (ICMU2021)

SGBS: A novel smart garbage bin system forunderstanding household garbage disposal behaviour
Eunice Likotiko1 , Shinya Misaki1 , Yuki Matsuda1,2 , and Keiichi Yasumoto1 ,1

Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan
Email: {likotiko.eunice david.ld4, misaki.shinya.mq9, yukimat, yasumoto}@is.naist.jp.
2
JST Presto, Tokyo, Japan.

Abstract—Recently, there is a growing use of smart garbage
management systems. Previous work on IoT garbage management systems presents an opportunity to provide a dynamic
garbage collection and estimate future garbage growth. However,
little attention has been given to understanding users’ garbage
disposal behaviour and the type of garbage contents produced
and disposed by the households. To examine, we designed anddeveloped a smart garbage bin system “SGBS”, capable oftracking garbage amounts and identifying the contents of garbage
disposed. The smart garbage bin was fastened with a ToF (timeof flight), DHT22 (temperature and humidity), load cell and air
quality sensors for measuring the amount of garbage. Households
users used smartphones installed with a garbage annotation
application to input the type of garbage contents they disposed.
A sigfox antenna module was used as a gateway to send data
into a cloud server. The developed system uses energy-saving
algorithms with sleep and wake-up cycles to reduce energy usage
and increase the lifetime of sensor devices on daily use. To
evaluate our approach, we conducted an initial experiment on
the smart garbage bin system in three households. Our findings
show that households’ garbage disposal behaviour depends on
the amount and contents of garbage as well as the routineof disposing such garbage content. We discuss the potential of
our system to be scaled in a smart city to influence behaviourchange, provide healthier life, and improve garbage management
operational efficiency.
Index Terms—Smart garbage bin, households behaviour, Sigfox, IoT, garbage contents.

I. I NTRODUCTION
Recently, there is a growing use of smart garbage management systems. Such systems are vital as it is criticallysignificant in providing dynamic collection and estimation of
future garbage growth [1], [2]. Daily in homes, there will
always be a lot of garbage produced. The type of garbage
contents and amount varies due to family lifestyle, family type,family size, and other home daily activities, including eating
and cooking behaviours. However, little attention has been
given to the existing IoT-based smart garbage management
system to understand households’ garbage disposal behaviours
and the type of contents disposed based on the households’
heterogeneous characteristics.
To fill this gap, this research contributes to the use of a contemporary methodology able to establish garbage log service
for households that can automatically track information aboutgarbage amounts such as garbage level, weight, moisture, air
quality, and identifying type of garbage contents disposed in©2021 IPSJ

order to learn household behaviour on the garbage disposal.
The garbage log service for households users involves learningand analysing patterns, peaks, and trends of the garbage
growth amount and identifying the type of contents of garbage
disposed daily in their own homes. Therefore, by leveraging the Internet of Things (IoT) technologies, we propose
a smart garbage bin system (SGBS) that can capture andprovide such garbage disposal information from households.
The proposed SGBS is capable of low energy usage and longdata range. This study considered the sustainability of an
IoT system deployment that relies significantly on prolonged
efficient power consumption and reliable data rate transmission
technologies of sensing devices. Meanwhile, the deployment
of IoT-based smart garbage solutions is still limited by powerhungry devices and long data transmission. Most smart bin
systems are usually installed in places where power outlets areunavailable [3] and far away from other network devices, thus
facing energy constraints and data transmission obstructions.
Using a battery could be an option, but a high frequency of
replacing and charging a battery could be complex for theusers and consistently impractical [3]. Besides, the use of
conventional energy is more expensive. Contemplating thesechallenges could greatly benefit from the IoT technologies.
To this end, this paper presents a designed and developed
SGBS with solar energy harvest capabilities that aims toprovide garbage log service for household users. The SGBS
has low energy usage, low data rate with enabling technology to sustain the system. The SGBS was deployed inthree households of heterogeneous characteristics where theimpact of a system can be easily measured and subsequently
reproduced on a larger scale. It is vital to understand thehouseholds behaviour on garbage disposal and identify thetype of content disposed. This is potentially significant to influence behaviour change to person and family on the garbage
disposal, thus minimising garbage generation. Further, thesystem can help smart cities improve garbage-related services,
provide more efficient garbage management, and encourage
responsible citizens through data-driven evidence to live incleaner, healthier, and safer environments. The remainder ofthe paper is organised into five Sections. Section 1 provides abrief introduction and background of the research. Section 2
lists some of the related work and details the state of theart of the existing systems. Section 3 provides methods andtools used in the study. Section 4 details the initial experiment978-4-907626-48-8/21/$31.00
©2021
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conducted whereas section 5 concludes the study.
II. R ELATED WORK
Recently, several IoT-based smart garbage management systems have been developed. Most of the existing work invests
effort in using the amount of garbage to estimate its future
growth and provide dynamic garbage collection. We discussed
our related work by considering both aspects found in theexisting work.
Dynamic polling algorithm for low energy garbage level[3] was proposed where the smart trash bin consists of an
ultrasonic range finder to measure the amount of garbage
level. The study used a polling algorithm to measure themaximum height of garbage based on historical information
about the fluctuation in garbage height gathered in advance.
The dynamic polling algorithm was used to reduce the power
consumption of the device. The study mainly focused on
dynamic garbage collection to eliminate the high cost andinefficiency of the existing static garbage collection systems.
A novel smart waste management approach for businessIoT “SWAM” was suggested [1], the system was elaborated
in the city of Luxembourg targeting businesses and large
entities. The system used ultrasonic sensors to measure theamount of garbage level in smart bins. Driver mobile dataand customer profile were combined and advise the driver on
the best times to visit a customer and collect garbage. Also,
the study proposed a multi-objective optimization layer, which
compiles the collection routes that minimize the impact on
the environment and maximize the service quality. Likewise,
Hossain et al. [4] demonstrated an optimal route planningmodel based on Dijkstra’s algorithm as one of the mostimportant factors in the smart waste management system inthe city. The authors considered the status of the amount ofgarbage level in a bin as one of the real-life parameters incalculating optimal distance link cost and other parameterssuch as road congestion status and distance travelled by thedriver.
Idwan et al. [5] advances the use of IoT technology todetermine the schedule and pathways of waste collection
trucks. The study simulated multiple route trucks using aheuristic algorithm and developed a smart dumpster equippedwith an ultrasonic sensor and GSM module to measure thelevel of waste and send updates into the central management
system using a wireless network. The author asserts that data
regarding the status of garbage in the developed dumpsters
used to determine the most effective route of the truck,
reducing the cost and time taken.
The dynamic features of IoT on human innovation withthe remedy of an ever-increasing amount of garbage wasdemonstrated [6]. The author developed a smart garbage bin
system able to record the status of the amount of garbage
level periodically in a cloud server and send a report to waste
management authorities and truck drivers for the automationof garbage collection.
Moreover, a prediction of fill-level containers using a BINCT (BIN for the city) software system was presented [2]. The

study pivoted on paper waste containers due to their variability
in collection frequency rather than organic waste. The system
combined two main algorithms, one for next day prediction of
the fill level of the containers based on the historical fill level
data of each container using Machine Learning algorithms.
Second, it computes the best routes to visit them; BIN-CTprioritized containers with fill level greater than 80 % for
the collection schedule. The predictive system is designed to
improve municipal waste collection planning. Similarly, Faye
et al. [1] also argued the future system to include the predictivemodels in order to obtain an estimation of the fill level at least
48 hours in advance and plan for collection.
Previous studies have shown that IoT-based smart garbage
management systems can use garbage amounts to predictgarbage growth and provide a dynamic and optimal route for
garbage collection to reduce cost and improve garbage operational services. However, our approach addresses a greater
understanding of household garbage disposal behaviour andidentification of the type of garbage contents disposed. Theinterpretation of garbage log data can help to influence behaviour change to users on the garbage disposal, thus minimising garbage generation. Consequently, our approach offers
healthier living for users and ensure more efficient garbage
management in the smart city.
III. M ETHODS AND TOOLS
A. System requirements
In this section, we identified and described the requirements
for the proposed SGBS as follows;• It should detect the amount of garbage in the smart bin.
• It should detect the moisture condition and air quality of
the garbage in the smart bin.
• It should be able to provide desirable low energy usage
on devices• It should be capable of transfer the detected data into a
cloud server at a long-range.
• It should identify the type of garbage contents and guideusers on how to dispose of garbage.
• It should allow household users to clean their smart bins
for healthier living.
• It should allow safe installation and replacement of smartphone.
TABLE IS ENSOR PROPERTIES AND PURPOSES
Sensor device
1.

DHT22

2.
3.

ToF
Load celland HX711
CCS811 Air
quality sensor

4.

Max current
(mA)
2.5

Voltage
(V)
3∼5

10
1.6

2.8 ∼ 5
2.6 ∼ 5.5

26

3∼5

PurposeTemperatureand humidity
Filling level
Weight
TVOC and
CO2

In order to achieve the above requirements for the SGBS
we selected hardware devices, where in the heart of the

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Fig. 1. High level architecture of smart garbage bin system

proposed SGBS, an ATmega328-Arduino Pro Mini microcontroller used. The ATmega328 uses a 3.3V, 8 MHz and consumes 16mA before the sleep mode state; this is suitable for
implementing a low energy smart garbage bin. In addition, we
selected the Low Power Wide Area Network, sigfox Antenna
module programmed with the ATmega328 to transfer small
chunks of measured garbage data into a specified cloud dataservice. Google Pixel 3a 64 GB simple smartphone with Wi-Fi
capability used for installing garbage annotation application.
Furthermore, we selected small, low power and lightweight
sensors for determining the state of garbage amount (fill level
and weight), moisture and air quality (Total Volatile Organic
Compounds (TVOCs) and Equivalent CO2 (eCO2) ). Table I
provides the purpose and power consumption properties of thechosen sensors used in the development of smart garbage bin.
B. Architecture design
Fig. 1 demonstrates the high-level architecture of the SGBS
which consists of three primary services. First, the smart
garbage bin operation service, where the smart garbage bin
is embedded with ToF (time of flight), DHT22 (temperatureand humidity), HX711-load cell, CCS811 air quality sensor
and a solar panel battery. Using a sigfox antenna module as agateway garbage data are sent to the cloud data server throughthe API calls. In addition, the smart garbage bin operation
service consists of the garbage annotation application installedon smartphones that guide users in disposing their garbage
in the smart garbage bin appropriately. Secondly, the Clouddata service collects, stores and processes all sensor data fromthe smart garbage bin. Thirdly, the garbage log service “GLS”
established by using the garbage amount and garbage contentsfrom households to learn the garbage disposal behaviour ofhouseholds which is the focus of this study.

Fig. 2. Smart garbage bin system design

C. System design
This subsection describes the design of the SGBS. Fig. 2
illustrates the design of the Smart garbage bin system. The
SGBS design consists of four components:• Smart garbage bin: Where sensors, solar panel battery,
sigfox antenna, and smartphones are attached for the
purpose of garbage data collection.
• Sigfox antenna module: That uses low power ultra
narrowband sends data to the backend of the sigfox cloud.
• Sigfox cloud: That uses a custom callback service typeto integrate with google cloud API through the createdgoogle sheet URL to receive the bytes of data via thesigfox antenna module.
• Google sheet cloud service: That receives data from
sigfox cloud using the doGet parameter function, storingand processing all sensor data from the smart garbage binthat are sent in a defined system time interval.
D. Sigfox as the enabling data communication infrastructure
for SGBS
Sigfox network is part of the Low Power Wide Area
Network( LPWAN) with an ultra-narrowband technology that

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uses a standard radio transmission method called binary phaseshift keying (BPSK). Sigfox operates in unlicensed bands
worldwide, with radio frequencies of 868 to 869 MHz and902 to 928 MHz using a data rate of 100bps to 600 bps [7]depending on the region. Sigfox network is employed mainly
for developing IoT more reliable than Wi-Fi because it canhandle a data transmission without object obstruction to 6km/h
from the installed location. Conversely to Wi-Fi technology
standards 802.11abgn which involve short-range, high costand high power consumption. As mentioned in the above
sections, we wanted to achieve low energy usage on the
proposed SGBS. Therefore, in this study, we installed a Kit
breakout board sigfox BRKWS01 (RC3+915Mhz) antenna.
The registered antenna contains a low-cost one-year contract
for the network provision and subscription. With the sigfox
module, the devices can send a maximum of 6 messages perhour (36/6) in a 10-minute duty cycle, which means a total of
144 messages per day. So we used this scenario to define the
SGBS time interval for sleep and active mode. This way, theSGBS sends new garbage data into the cloud service using thedesignated 10-minute break continuously in a day. To receive
data, we used the custom call back service at the back end of
sigfox cloud to integrate with the google sheet cloud server
through google API, whereas data are received with the helpof the doGet function query.
E. Energy saving algorithm
In this section, an energy-saving algorithm is proposed anddescribed to reduce power consumption on the smart garbage
bin. We assume the contents of the garbage differs from one
household to another in a day. Thus, the proposed system has
to record such patterns without missing an important garbage
disposal behaviour of the user in specified time intervals.The
energy-saving algorithm operates in active and sleep modes tocollect information about garbage contents;
1) Active mode: During the active mode is where themeasurement of garbage contents occurs using the sensors.
The smart garbage bin starts by waking up for two seconds,
all the sensors to measure the available garbage contents.
Because the frequency of measurement directly affects thepower consumption, it is important to reduce the system active
time as much as possible. The measured garbage values are
uploaded as bytes of a message into the cloud server via thesigfox antenna module. This process requires a delay time ofabout 1.5 seconds before the system enters sleep mode.
2) Sleep mode: In sleep mode, we achieved a low energy
usage of the smart garbage bin. During this state, the microcontroller enters sleep for 8 minutes. We used a Low Power
Mode (LPM) that puts inactive functions that consume power
to run in a microcontroller, including; Timer 0, 1, 2, SPI
and UART Communication, External Oscillator through an
Arduino lower power library. This method results in low usageof power in the smart bin devices. To avoid confusion on thesolar battery and consistently power the system devices using
this algorithm, we introduced a 1-minute wake delay in theenergy-saving duty cycle. Therefore, using this energy-saving

TABLE IIP OWER CONSUMPTION CHARACTERISTICS
Active
mode
Sleep
mode

Mode
Wakeup current
Wakeup time
Sleep current
Sleep time

Current consuption
50.1 mA
3.5 sec
38 mA
9 min

algorithm is expected to be an efficient power consumption on
the proposed SGBS.
F. Evaluation of power consumption
In this section, a method for measuring the power consumption by the smart garbage bin is introduced and in order toconfirm its low energy attributes, as discussed in the previous
section. The proposed SGBS aims to track garbage amounts,
including; garbage level, weight, moisture, and air quality.
Thus, the energy-saving algorithm was used to increase lifetime sensor devices. Using Cen-Tech digital multimeters, wemeasured the total current consumption by the smart garbage
bin during the active mode, when it performs measurements
and communicates the data to the cloud server. Also, wemeasured the power when the smart garbage bin is in sleepmode.
1) Power Measurement method: To calculate the power
measurement by the smart garbage bin, we measured the total
current consumption by the devices over time (3,600 seconds
in one hour). As mentioned above we divided the measurement
into two ways; during measurements (Active mode) and at
sleep mode. Table II recorded the power consumption characteristics of the smart garbage bin during active mode andsleep mode.
2) Results: By referring to Table II in active mode, thesmart garbage bin consumes 50.1 mA as a wakeup current
in a total of 3.5 seconds, six times in every one hour. If
it is expressed into milliamp-hours (mAh) units, the power
consumption of the smart garbage bin can be calculated asfollows;
50.1 mA × (3.5 sec ×6 /3600 sec) = 2.9 × 10−1 mAh (1)Again from Table II during the system sleep mode, the smart
garbage bin consumes 38 mA as a sleep current in total of 9
minutes which is equal to 540 seconds. If it is expressed into
milliamp-hours (mAh) units, the power consumption of thesmart garbage bin can be calculated as follows;
38 mA × (540 sec ×6 /3600 sec) = 34.2 mAh(2)3) Estimate Battery life: To confirm the lifetime of smart
bin devices using our energy-saving algorithm, we estimatedthe battery life of the solar panel battery in powering thesmart garbage bin. We conducted our experiment using a
26,800 mAh solar panel battery to power the smart garbage
bin. Therefore, we used power consumption characteristics as
demonstrated in Table II and assumed the percentage wasted

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TABLE IIII NFORMATION OF THE PARTICIPANTS
Household one

Household two
Fig. 3. Smart garbage bin system deployed in a household

capacity of the solar battery is less than 5%. Thus, the battery
life of the smart garbage bin was calculated as follows;
Average current =(38 mA × 540 sec) + (50.1 mA × 3 sec)
(540 sec +3.5 sec)

Household three

Particulars- Age group:(23)- Type of family: Single- Size of family: 1- Life style: 1 absence on day on weekdays- Cooking & Eating habits: often evening
Particulars- Age group:(29)- Type of family: Young married couple- Size of family: 2 adults, 1 child- Life style: 1 absence on day on weekdays- Cooking & Eating habits: often all the days
Particulars- Age group:(55-50)- Type of family: Married Couple- Size of family: 2 adults- Life style: 1 absence on day on weekdays- Cooking & Eating habits: often all the days

= 38.0779 mA
26, 800 mAh × 0.95
38.0779 mA
668.63 h=
24 h≈ 29 days

Battery life time =(3)

IV. D EPLOYMENT AND DATA COLLECTION EXPERIMENT
We conducted an initial experiment to verify the feasibilityof the SGBS for 11–21 days in three households to understandhouseholds’ behaviours on the garbage disposal and identify
the type of garbage contents disposed. We used age group,
family size, family type and cooking and eating habits ascriteria to select the participants for the experiment. Table III
outlines the information of the participants of the initial study.
We ensured safe data collection and storage to users by protecting users’ anonymity and confidentiality, and the collected
data was used only for the intended purposes of this study.
We did not sample for specific experience with smart hometechnology, but we assumed all participants have experience
using smartphones and general knowledge of sensors Fig. 3
shows the deployed SGBS in the household. The subsequent
section illustrates how households use a mobile applicationthat guides during the garbage disposal.
A. Garbage annotation mobile application
In order to collect information about the type of garbage
contents disposed by household users, we designed and developed a garbage annotation mobile application to guide users
in the garbage disposal. The developed mobile applicationconsists of four categories with ten different types of burnable garbage contents; kitchen garbage; (all food garbage),plastic/unclean products; (storage containers, toys, unclean
packages, unclean containers), paper/textile; (tissues, mixed
papers, milk/juice box, unclean cloth) and other related typesof garbage contents as shown on Fig. 4. The classification of
burnable garbage based on the catalogue as instructed by the
Ikoma city in Japan thus creates the menu for garbage annotation mobile application. The garbage annotation application

Fig. 4. Garbage annotation mobile application

allows individual households to select the type of garbage
content each time they dispose garbage in the SGB from ahandy smartphone installed outside on top of the SGB cover,then data about the garbage content are sent into the clouddata server using a Wi-Fi network.
B. Result and discussion
The discussion below is on the general aspects of understanding behaviour of household users on garbage disposal
from the established garbage log service “GLS” of each family.
First we discuss the tracking of garbage growth amount fromthe users of the three households, then identified important
types of garbage content for each household and lastly wedescribe the routine behaviuor of disposing such garbage
contents. This discussion is more on general households’
adoption, experience and limitation in using the SGBS.
1) Tracking garbage growth amount: The objective of theinitial experiment was to verify the feasibility of the SGBS
in understanding the behaviour of households on garbage
disposal as mentioned in the previous chapters. Fig. 5 toFig. 13 depicts the results of tracking the garbage amountinto all three households users when using the smart garbage

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Fig. 5. Garbage filling level in household one

Fig. 8. Garbage filling level in household two

Fig. 11. Garbage filling level in household threeFig. 6. Garbage weight in household one

Fig. 9. Garbage weight in household two

Fig. 12. Garbage weight in household threeFig. 7. Garbage moisture in household one

Fig. 10. Garbage moisture in household two

Fig. 13. Garbage moisture in household threebin. The size of the smart garbage bin used in the experiment
was Width 31 × Depth 39 × Height 57.5 cm and 45 L
Capacity. From the Fig. 5 to Fig. 13 the Y-axis of garbage
level shows the fill level in cm unit, and Y-axis of garbage
weight graphs shows the weight of garbage in gram units
respectively and the X-axis of both shows the date when thedata was collected. The actual fill level was measured between
the lid of the smart garbage bin and the garbage disposal bag
inside the smart garbage bin. So a narrow distance shows thatthe garbage is reaching the maximum fill level and a long
distance shows that the smart garbage bin is almost empty.
The actual behaviour of garbage fill level amount is directly
proportional to the growth amount of weight of the garbage
however the fill level was much affected by compression
behaviour causing huge fluctuations of the fill level fromthe steady state. The behaviour of changing garbage disposal
bags inside the smart garbage bin differs from household tohousehold. In addition, the tracked garbage growth amountfrom the households is helpful in predictions of future growth
behaviour as proposed in our previous work [8] where atime series machine learning algorithm was used to forecast
future garbage growth experimented at a single house level.
Furthermore, predicting future garbage growth behaviour canbe scaled from the households to a larger scale of the city ina different season of operation, thus providing more efficientgarbage management. The subsequent section provides a brief
understanding of household behaviour on garbage disposal
using the tracked amount of garbage.
• Household one: Was a single family occupied by one
individual. During the first seven days (27-May to 2June) of the experiment in this household, the garbage
amount as illustrated in Fig. 5 and Fig. 6 remained at
the lowest steady-state with slight sensor fluctuations.
This behaviour validated the assumption that either theparticipant was absent from home or the participant did

cook or eat at home at all thus, there was no any
garbage disposal behaviour observed. On the followingdays of the experiment, the garbage growth started to beobserved using the collected sensor data and annotation
app. Surprisingly, the garbage amount grew faster than
expected and remained at high peaks for four days beforechanging the garbage disposal bag in the smart garbage
bin, whereas the sensor values began from their initialvalue on around 4-June. Further on the experiment, we
also observed that the garbage amount continued to
grow for six days around 6-June to 11-June with somefluctuation due to garbage compression behaviour, thus
verifying that the household did not change the garbage
disposal bag for all six days. This was also revealed as
the sensor values did not begin from their initial values
but continued. Such a pattern found to be both strange
and interesting compared to the observed behaviours ofhousehold two and three as described below.
• Household two: While the household one had an unpredictable garbage growth behavior, household two which
consisted of a family size of two adults with one child
had a constant behavior on garbage disposal. As shown
on Fig. 8 and Fig. 9 the participants used the smart
garbage bin every day and changed the garbage disposal
bag mostly after every two days, This occurrence wasalso realized by using the collected sensor values as
they changed and began from its initial values every
time the disposal bag was changed. However the garbage
compression behavior was also highly perceived, which
explains that probably participants always want to keep
the garbage bin from reaching its maximum thresholds
or heavier garbage came after lighter garbage.
• Household three: The household consisted of two married couples. The behaviour of garbage growth as shownin Fig. 11 and Fig. 12 looked similar to household

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two, although the peak, trends and patterns of garbage
growth were different. In this household, the behaviourof changing garbage disposal bags varied from one day( 31-May to 1-June) to a maximum of four (4-June to
9-June) as depicted in both Fig. 11 and Fig. 12. This
trend was also realized using the collected sensor values
as they changed and began from their initial values.
2) Identification of garbage content: Further, this study
aimed to identify the type of garbage contents disposed by
the households. Households used the garbage annotation app
to select and input the type of garbage contents each time they
disposed garbage in smart garbage bin. The type of garbage
contents was first identified through the data from the garbage
annotation app, which had four categories of burnable garbage
with ten different types of garbage contents, as described in
the above sections. Also, the garbage contents were realised
through the moisture conditions and the air quality foundin the smart garbage bin since the type of garbage contents
affects the moisture and air quality in the smart garbage bin.
Therefore, using data from the garbage annotation applicationand moisture inside the smart garbage bin, we identified andranked the type of garbage contents that were more importantto the households (high produced and disposed) than the other(low produced and disposed ).
Household one: As shown in Fig. 14 we found thatfood garbage contents ranked as the most important
garbage produced and disposed of by the participant.
In this household, the participant often disposed thefood garbage content causing the moisture to rise in
the smart garbage bin. Therefore, the moisture condition
was observed to be consistently high, as depicted inFig. 7. Furthermore, Tissue contents were second in rank,followed by other type of burnable garbage content. The
unclean package was the fourth in high ranking. The
mixed paper was the lowest in the rank of garbage
content produced and disposed only three times by thehousehold. The result has shown that mixed paper was
only disposed three times between 3-June and 5-Juneand once on 13-June. An unclean container disposed six
times, whereas storage containers appeared seven times
during the experiment.
• Household two: Like in household one ( Fig. 14), it
also observed that tissue garbage content ranked secondin household two as illustrated in Fig. 15. The unclean
package garbage content ranked first, followed by unclean
container. Lastly, the food garbage content ranked fourthamong the important garbage produced and disposed by
the participant, only five times that the storage containers
were disposed made it the lowest in rank. Although toysgarbage seemed to be not important in other households,
it was exciting to find it few times in household two.
Moreover, in this household, the moisture inside thegarbage bin was observed with a shifting tendency, as
shown in Fig. 10 , because participant used to collect andpark garbage in a small disposal bag before disposing it in•

the smart garbage bin. This justifies the observation thatthe unclean package was identified as the first garbage
content produced.
• Household three: On the contrary to the observation tohousehold one and two where tissues garbage content
was the second in rank, in household three, as shownin Fig. 16 tissue was ranked first as the most important
garbage content, followed by other types of burnablegarbage content. Food garbage contents ranked third,
whereas unclean packages ranked fourth. The unclean
cloth was the lowest in the rank as it was observed only
once on 9-June throughout the experiment. Even thoughtissue contents are frequently disposed, the moistureinside the smart garbage bin was high as observed inFig. 13; this proves the assumption that the tissues wereslightly wet and also the food contents contributed to therise of the moisture in a smart garbage bin3) Routine behaviuor of garbage disposal: In addition,
we have also learned the routine of garbage disposal by
the households. In general, the study found that households
can dispose of different types of garbage simultaneously andannotate all types of garbage content at the exact incidence.
The study further observed that certain types of garbage
content were frequently disposed of and annotated daily by thehouseholds. For instance, food garbage contents in household
one, Unclean packages garbage content in household two
and tissues garbage content in household three Fig. 14 toFig. 16 thus, were identified as the most important type of
garbage contents disposed by the household every day in theexperiment.
C. Limitation of using SGBS
Apart from the attained objectives in this initial experiment,
however, the study had some limitation and technical challenges in using the SGBS as follows;• An unimportant garbage content identification: The
study followed the burnable garbage manual to categorise
the garbage content in the annotation app, yet somegarbage contents were not important to be identified byhousehold users, e.g. toys.
• Wrong and forgotten garbage annotation: Althoughfrom the observed data, households users were able toannotate the different types of garbage each time they
dispose garbage. However, it was also possible for thehousehold user to wrongly annotate the garbage content
or forget to annotate the garbage because it was hard toknow if the household annotated all types of garbage.
• Garbage compression behaviour: The compression of
garbage in the smart garbage bin by the participant’s
hand during the disposal as one way of keeping the
garbage bin from reaching its maximum thresholds shifts
the values from its steady-state and disturbs both fill level
and weight sensor readings.
• Use several garbage bins: Households often use several
bins for different types of garbage; however, this initialstudy is based on burnable garbage only.

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The 13th International Conference on Mobile Computing and Ubiquitous Network (ICMU2021)

Fig. 14. Identified garbage contents in household one

Fig. 15. Identified garbage contents in household two

Fig. 16. Identified garbage contents in household three

D. Technical challenges
Power problem: During the experiment, we faced apower supply problem. The used solar panel battery could
not achieve the power supply for 29 days as it wasapproximated by our energy saving algorithm, unsteady it
only supplied power to the smart garbage bin half of the
expected only 13 days. Additionally, we also experienced
some short circuits caused by the design of the circuit ona breadboard.
• Network connectivity: Although the used sigfox antenna
showed good area coverage. Still, we experienced amessage delay problem to our server, which persisted
for some hours and sometimes changed the transmissioninterval from 10 minutes as pre-defined in the SGBS to 20
minutes time interval minutes. These technical challenges
led to missing some data points in the households andalso disturbed sensor readings.
•

V. C ONCLUSION
This study aims to understand households’ garbage disposal
behaviour and further identify the type of garbage contents disposed. It is vital to learn the household behaviour on garbage
disposal and identify the type of content and routine of disposing such garbage contents to influence behaviour change,
provide healthier life, and improve garbage management operational efficiency. This study designed and developed the
SGBS to tracking garbage amounts and identify the contents
of garbage disposed. The observations from our initial study
reveal that the amount of garbage has a strong relationship withhuman activities, including households eating and cooking
habits. The study showed that households’ garbage disposal
behaviour depends on the amount and contents of garbage andthe routine of disposing of such garbage content. Moreover, thecategorization of garbage content helped identify an important
type of garbage content for each household. Further, the study
realized the household disposes a specific type of garbage
contents more often than other garbage. In future, we willbuild a garbage content estimation model for households andfurther predict future garbage growth behaviour.
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