Smart garbage bin: Garbage growth behavior prediction
Eunice Likotiko1,a)Yuki Matsuda1,b)

Keiichi Yasumoto1,c)Abstract: Presently in the heart of many major cities globally, there exist several smart garbage systems based on theInternet of things (IoT). Likewise, there are different traditional garbage management systems at levels of cities andmunicipals with fixed collection schedules. However, to learn garbage growth behavior in a single house level at different times and predicting future amounts for the next collection schedule is paramount. Therefore, in this study, we
design and develop a smart garbage bin embedded with ToF (time of flight), DHT22 (temperature and humidity), andload cell sensors and Wi-Fi interface. With the prototype of the garbage bin, we conducted a preliminary deploymentof a customized smart garbage bin in a student laboratory. Using a Wi-Fi gateway data were sent into a ThingSpeakcloud platform, thus enhancing the smart bin system’s ubiquity. A machine learning method for continuous-time series forecasting ARIMA with a fixed forecast slide window size of 2-4-7 days on the split training size was appliedin learning the garbage growth and predicting future garbage behavior. The ARIMA model was evaluated based on
the performance measurement values of average mean absolute error and standard deviation. Training the model with
N=20 number of observations given with the 4-window size provided the average mean absolute of 5.17 cm and thestandard deviation of 0.33 cm, thus was considered the best accuracy on the garbage growth prediction. The ARIMA
model found to be suitable for predicting future garbage growth behavior; therefore, enhancing flexibility in the garbage
collection schedule and the frequency of changing garbage bags in the smart bin.

1.

Introduction

In recent times, many studies on Internet of Things (IoT),“smart cities” and “smart homes” have been conducted. Over
decades, the main goal has remained the same, to apply ubiquitous sensing to enhance the human experience [1]. Owing to
the characteristics and merits of IoT services, garbage management has also become a significant domain attracting a numberof IoT studies. The current IoT applications for improving domestic garbage management services such as Radio Frequency
Identification (RFID) [2], [3] as well as proximity sensors [4], [5]have been helping users to take data-driven actions ahead of timethrough real time monitoring, transfer of data into specified storage as well as sending notifications to the relevant authorities using communication technologies such as GSM and Wi-Fi. On theother hand, cities and municipals have also introduced long term
garbage generation analysis and predictions models [6], [7], [8]for best route plan of garbage collection and future data predictions.
Apart from this technological revolution of IoT smart garbage
management systems as shown in Fig. 1, different countries havedifferent traditional garbage management systems at various levels such as cities and municipals levels. Japan as a case basefor this study is known as an exceedingly clean and eco-friendly
country [24]. The cities, towns, and districts have completely different garbage management systems that are said to be efficientand effective. Taking a walk in a Japanese city, one would rarely
1

a)
b)
c)

Nara Institute of Science and Technology,
Takayamacho, Ikoma, Nara 630-0192, Japanlikotiko.eunice david.ld4@is.naist.jp
yukimat@is.naist.jp
yasumoto@is.naist.jp

find a public garbage bin. In most cases, distinct types of bins
are installed in an area comprising of burnable, plastic, pet bottles and cans garbage bins. The disposal and sorting is strict andcomplex.
Likewise, at the household level, every household is responsible of proper management and disposal of own garbage. This is
by sorting and disposing garbage in a specific colored bag thatare then put outside the house on the right collection day basedon the city’s fixed garbage collection schedule as is set by a particular city (Fig. 1). For example, in Ikoma city found in Naraprefecture [25] burnable garbage are scheduled to be collected
twice a week on Mondays and Thursdays, while plastic garbage
is collected once a week whereas the rest garbage such as glass,cans, pet bottles, fragile items and toxic waste are collected twicea month. Nonetheless, such existing systems are city based andunfriendly in learning garbage growth behavior for a single houseat different times in a day, a week, or a month. This results in unnecessary garbage collection trips, thus adding up to the cost ofoperation and lack of proper household garbage growth records
that are essential for appropriate city planning and management.

Fig. 1

Overview of traditional and smart city garbage management systems

Interestingly, there is yet inadequate studies on IoT based

1

garbage management systems that can learn growth behavior of
garbage in a smart bin and predict its future growth at a single
house, in realization of garbage generation in a season such as
day of a week for example a weekend; time of the day such as
morning time or evening time, rather than solely monitoring thelevel of garbage to notify the authority or owners based on theirconfigurations. For instance, garbage disposal at a university laboratory would fluctuate based on many factors such as a day of
the week. It is obvious that while there would be so much garbage
on weekdays, there is less on weekends.
This paper aims to introduce a customized smart garbage bin capable of Big data collection of user’s garbage disposal routine using some sensors and Wi-Fi communication technology. Further,to establish and predict a house based garbage growth behavior
patterns and collection schedule. The understanding of garbage
disposal growth rates and predictions of growth behavior is vitalfor practical and efficient garbage management.

2.

Related work

This section presents a thorough discussion on existing IoT
based smart garbage management solutions.
IoT based smart garbage solutions have been implemented at
the heart of major cities in the world such as Seoul-Republic ofKorea, Varese- Italy, Hong Kong, Barcelona- Spain, Singapore,and Stockholm- Sweden [5], [9], [10]. In these cities, smart bins
are equipped with sensors that provide users with ability to know
the fill-level (volume) of each waste container in real time. Thesebins are equipped with a live monitoring platform which helps
the waste collection staff to plan ahead on how collections shouldbe implemented, targeting only the locations of full garbage bins
[10].
Mostly, there are different technological approaches for implementing such application solutions. For instance, studies in[4],[11] developed a smart and wireless waste management system using a load cell, ultrasonic sensors, and GSM module, whichused to notify either the bin is full or emptied. Besides, the workin [12] developed a cloud-integrated and wireless waste management system for smart cities involving a combination of infrared(IR), ultrasonic sensor, temperature sensor, (MQ2) gas sensors
and load cell in monitoring and storing the information about
waste status in a bin. Some approaches focus on RFID technology where the smart bin embedded with RFID tags, and the collection vehicle is installed with the RFID reader to detect smart
bins during waste collection in the city [13],[3],[14].
Likewise, to implement measures on reduction and recovery
of waste, [2] introduced iBags using RFID. Further, other studies
used RFID technology to build smart garbage management system such as in Seoul, Korea [10] and The Hong Kong Polytechnic
University [9] where NFC card was launched, for users to open
the bin a verification process is done by pairing up with the user’s
card and charged based on weight measured.
Broadening to the approaches of IoT based garbage management, machine learning techniques have also been involved.
Whereby a study [15] applied a linear regression model on prediction future garbage generation using level values from the ultrasonic sensor and population as main variables. Also, some studies[16],[6] applied decision tree and Time-series algorithms respectively in predicting and modeling future garbage generation andplan for the best route for garbage collection using sensor fillinglevel as an influential variable for predictions. Moreover, efforts
are devoted at municipal and city levels on predictions and modeling of future garbage generation and collection at their levelsin overall [7],[8],[17]. This provides benefits to the authorities in
estimating and allocating essential resources needed in the futurefor garbage management and formulating alternative strategies toinfluence the attainability of sustainable goals [6].
However, these approaches have been exclusively helping
users conduct real-time monitoring, taking data-driven action
ahead of time, sending a notification for full waste bins, predicting, and planning for the best garbage collection route. Yet, it
is indeed vital to understand and learn the growth behavior of
garbage in a smart bin and predict future growth at the single
house level in a season. This remains significant for future decision making in ensuring effective as well as efficient garbage
management. Hence, we developed a customized smart garbage
bin, suitable for Big garbage data collection, learn garbage
growth behavior, and predict future garbage growth amounts.

3.

Methods and tools

3.1 System requirements
In this section, we describe system requirements for the proposed customized smart garbage bin. Based on the discussions in
Sections 1 and 2, there are the following two main requirements
for a smart bin system:
Req 1: It should detect a particular garbage variable andupload it to the specified cloud storage at a defined programming time interval via a wireless gateway. Also, ensure realtime data visualization.
Req 2: It should be able to conduct data analysis and predictions of future garbage growth.
3.2 Proposed smart garbage system
The proposed system’s block diagram comprises three main
layers, as shown in Fig. 2: Hardware layer, Cloud service layer,
and Processing and control layer. In particular, the smart bin collects bin’s status data and then instantly sends it to the cloud platform via a gateway. Later, machine learning methods are applied
for data analysis and prediction. The following is the explanationof each part of the proposed system. On the other hand, Fig. 3
provides an overview of the proposed smart garbage bin system.

Fig. 2 Block diagram for proposed smart garbage system

2

Table 1 A summary of studies used different technologies in the development of IoT-based smart garbage
systems
Reference
Wi-Fi
[18]
O
[19]
X
[3]
X
[2]
X
[20]
X
[10]
X
[4]
X
[9]
X
[12]
X
[21]
O
[14]
X
[22]
X
[11]
X
[23]
O
our system ∗O∗ O = YES, X = NO

GSM
X
O
O
X
O
X
O
X
O
X
X
O
O
X
X

RFID
X
X
O
O
X
O
X
O
X
X
O
X
X
X
X

ZigBee
X
X
X
O
O
X
X
X
X
X
X
X
X
X
X

Ultrasonic sensor
X
X
O
X
O
X
O
O
O
O
O
O
O
O
X

Infrared sensor
O
X
X
X
X
X
X
X
X
X
X
X
X
X
X

Load cell
O
O
X
X
O
X
O
O
X
X
X
X
O
X
O

DHT11/22
X
X
X
X
X
X
X
X
O
X
X
O
X
X
O

MQ2/135
X
X
X
O
X
X
X
X
O
X
X
X
X
X
X

Camera
X
O
X
O
X
X
X
X
X
X
X
X
X
X
X

ToF
X
X
X
X
X
X
X
X
X
X
X
X
X
X
O

the ToF, DHT22 (temperature, and humidity) sensors Fig. 3. The
ToF sensors measure the increase of garbage fill levels in a smart
garbage bin in the centimeter unit of measurements plus DHT22,
which measures the inside temperature and humidity conditionof garbage in the bin. The temperature and humidity need to bemonitored because the garbage may decompose and may produce
a pungent smell. Further, the bottom part of the smart garbage
bin Fig. 2 comprises the load cell, which is responsible for detecting the increase of the garbage weight in the bin. Power Core
26800mAh Anker external battery supply sufficient power to thesystem. Arduino IDE software was used as the programming environment for the sensors. Table 2 shows the type of sensor usedand purpose.
Fig. 3

Smart garbage bin overview
Table 23.2.1 Hardware layer
The Hardware layer comprises the hardware used in developing the proposed smart garbage bin system with two levels; bottom and top shown in Fig. 2. By which Adafruit feather m0 WiFi atsamd21 + atwinc1500 Microcontroller placed in the heart ofthe system connected to the sensors. The Adafruit feather m0 has
a built-in Wi-Fi module hence providing ubiquity, ease of setupof the system. We considered exploring other related studies
used different technologies in the development of IoT-based smart
garbage systems see Table 1, where communication technologies
such as RFID, GSM, Wi-Fi, and ZigBee enhanced garbage datatransfer during real-time monitoring. Besides, sensor technologies such as ultrasonic, infrared, load cell, and DHT22, provided
the measurements of the garbage status. The review from otherrelated studies found that the ToF sensor has not yet commonly
considered in developing an IoT-based smart garbage system. In
contrast, ultrasonic and infrared sensors are found to be popular. Thus, in the present study, we ultimately chose the time offlight (ToF) sensor. The ToF sensor is not affected by the colourof the target object compared to the infrared sensor. Also, compared to the ultrasonic sensor ToF sensor does not critically depend on the angle of incidence, and not disturbed by environmental noise; thus, it has greater readings and accuracy. Therefore,
in the proposed system, the smart bin cover is embedded with

Sensor used in development of smart garbage bin

Sensor
1. AE-VL53L0X(ToF)
2. DHT22
3. Load cell

PurposeMeasure fill level of garbage in a bin
Measure temperature and humidity in a bin
Measure increase of weight of garbage in a bin

3.2.2 Cloud service layer
In attaining the first requirement, a ThingSpeak cloud platform was used. ThingSpeak is an open-source cloud platformthat provides cloud space for IoT projects. Using the Wi-Fi gateway connection from the hardware layer, all of the sensed datafrom the smart garbage bin were continually uploaded, stored,
and visualized into the Thingspeak cloud space via the provided
Write-Application Programming Interface (W-API). Besides, aThingView mobile application linked to Thingspeak via (W-API)enhanced easy data visualization and real-time garbage monitoring.
3.2.3 Processing and control layer
The processing and control layer backs the achievement of thesecond requirement of the smart garbage bin system. A Timeseries machine learning algorithm for continuous data named an
autoregressive integrated moving average (ARIMA) was applied.
Fig. 4 is a flow chart of the predictive model for future garbage
growth behavior patterns of a single house. The flow chart includes data preparation, machine learning model building, performance measurement, and the model’s deployment.

3

Fig. 4 The flow chart of modelling steps used in this study

4.

Deployment and data collection experiment

In evaluating the performance of the developed smart garbage
bin in Fig. 3, we explored a way of successfully utilizing the smart
bin system in terms of the number of days (times), type of user,and garbage growth. In addition to the smart garbage bin’s validation and feasibility, some key questions studied foremost. These
are: how a big data collection of garbage growth of a single house
being conducted?; how to learn the trends/patterns of garbage
growth?; and how to estimate the future amount of garbage generation for the next schedule and changing of garbage bags?
To respond to the questions above, we conducted a preliminary
deployment of the smart garbage bin in a university laboratory
consisting of 42 research students who use the laboratory daily.
As a routine, students visit the laboratory from Mondays to Fridays, on or after morning to night hours, while few visit the lab
on Saturdays and Sundays. At these times, they do different activities, including eating, drinking, and cleaning, which in the end,
produce garbage.
The lab is placed with three types of garbage bins: burnable,cans, and plastics. However, for this study, the focus stood on
burnable garbage only. Burnable garbage includes: food waste,
paper waste fruit/vegetable peel, eggshells, old clothes, and anyother food item that may leave an unpleasant odor if left in thebin for too long.

5.

The interpretations of the results shown in Fig. 5 based on using the p-value from the test. The time series dataset is considered stationary if the p-value is p≤0.05, and the critical values
at 1%, 5%, 10% confidence intervals are as close as possible to
the ADF statistics. In our test, the ADF test gave the p-value of
0.006, and the test statistic was less than the 1% critical value.
Thus, it suggests that we can reject the null hypothesis with asignificance level of less than 1%; therefore, the dataset is stationary. Consequently, we used the ARIMA model to predict future garbage growth using a fixed-sized slide forecast window on
training and to test the model. ARIMA is a popular model andwidely used in the statistical method for continuous-time series
forecasting [26]. The ARIMA model consists of the three components: autoregression (AR), integrated (I), and moving average(MA), which is explicitly specified in the model as a parameter
like ARIMA(p, d, q). An auto correlation function (ACF) provides the MA value, and partial auto correlation function (PACF)
provides the AR. The akaike information criterion (AIC) value
allows us to compare how well the model fits the data. The lower
the value, the better the model. Therefore, we built our modelwith the ARIMA (2, 1, 0) with the AIC value of 205.

Model building

The foremost way to model continuous time series data using
an autoregressive integrated moving average (ARIMA) model is
to check the stationarity of the observation, which can be used infeature engineering and feature selection on time series problem
when using the machine learning method. We applied an augmented statistical dickey-fuller (ADF) test in checking the stationarity of our time series dataset with a 2-day rolling window.

Fig. 5

6.

Dickey-Fuller test statistic results

Results and discussion

6.1 Cloud data visualization
In our preliminary deployment of a smart garbage bin in a university laboratory, data from three sensors, ToF, DTH22, and loadcell, were continuously collected and stored in the ThingSpeakcloud storage over 30 days at a 1-minute interval. For smooth
live streaming and garbage data monitoring, a ThingView mobile
application shown in Fig. 6 linked to the ThingSpeak cloud storage via W-API, provided good data visualization.
6.2 Daily garbage growth
During the deployment, we studied the garbage’s growth using the fill-level values sensed by the ToF sensor from the top
level of the smart garbage bin in a time series interval of eachday. As a reference, Fig. 7 reveals both slow and high variationsof the garbage growth behavior, which depended on the use ofsmart garbage bin on a particular day by the students in the laboratory. Further, we determined the trended frequency of changing the garbage bags on different peak values as Fig. 7 illustrates,
whereas, the small green boxes indicate the lowest peak value(5.6 cm) from the bottom when the smart bin was empty. The
dark red small boxes indicate the highest peak values of garbage

4

garbage growth on training the ARIMA model with the ten, fifteen, and twenty N-number of observations. The result shows thatthe predicted garbage values follow the actual values of the initialobservations. Additionally, Fig. 11 demonstrates prediction outcomes of garbage growth on each number of training size; N=10,
N=15, N=20 with given forecast window size. We have observedthat the ARIMA model is suitable for predicting future garbage
growth behavior in the single house because the prediction follows the actual observations and able to provide predictions withfew amounts of data; also, the model was capable of gathering
the fluctuations on observed data, and the averaged accuracy error decreases with respect to the simultaneous increasing forecast
window size and training size. Moreover, the ARIMA model is a
flexible method, which uses past data to predict the future where
its application does not require much data. Thus, in the present
study, the ARIMA model using the slide forecast window wasused, provided flexibility and functional result.

Fig. 6 Daily garbage growth data visualization using ThingView app

where the change of garbage bag occurred, such trend frequencyof changing the garbage bag learned as irregular behavior in thesmart bin system. The change of the garbage bag can also be
due to bad smell resulting from decomposed garbage in the smart
garbage bin.
60

High peak

Garbage_Increase_Level

50

Low peak

55.5

53.9

53

Fill level

51

47

46.9

40

39.9
36.5
35.2

32.4

30 27.3

Fig. 820

Garbage growth prediction with ten number of observations

10
5.6

5.6

5.6

5.6

5.6

5.6

5.6

5.6

5.6

0

Day_Time

Fig. 7

Garbage growth and frequency of change of a bag in the smart bin

6.3 Garbage growth prediction
To predict the future amount of garbage growth for the nextcollection schedule and the change of garbage bag. We applied
the ARIMA model using a sliding window forecast method totrain the model on the fill-level dataset, which consisted of a
total of thirty number of the observations provided by the ToF
level sensor. Initially, we started by splitting the whole number of observations into different training size, and each training
size takes a given forecast window size as an input for testingand predicting (Forecast) future garbage growth behavior. As
shown in Table 3, the number of observations (N) was split intoten days, fifteen days, and twenty days as training size. Therefore, a fixed forecast slide window size of 2, 4 or 7 days was applied to each N-number of observations to forecast the garbage
growth. Fig. 8, Fig. 9, and Fig. 10 illustrate the prediction ofFig. 9 Garbage growth prediction with fifteen number of observations

6.4 Performance measurements
To analyze the prediction’s relevant facts, we calculated theerror of the model on each training size (N-number of observations) given the slide forecast window. There are many standard methods that can be applied to evaluate the performance of5

level, which was best achieved by the model, can impact the timing of garbage bag change during garbage disposal and garbage
collection schedule.
Table 3

ARIMA model performance measurement

Training size (N)Forecast window size
Average MAE
(day)
(day)
(cm)
10
2
3.67
10
4
4.63
10
7
5.34
15
2
7.54
15
4
6.11
15
7
5.56
20
2
5.08
20∗
4
5.17
7
5.34
20
∗ is considered as the best performance accuracy

SD
(cm)
0.75
0.76
0.62
0.18
1.42
1.19
3.24
0.33
0.55

Fig. 10 Garbage growth prediction with twenty number of observations
9
8

Average_MAE_ Error

7
6
5
4
3
2
1
0

10 -2

15-2

20-2

10-4

15 - 4

20- 4

10-7

15-7

20-7

Number_of_train and Forecast_window_size

Fig. 12 An error bar graph during model performance measurement
Fig. 11 Prediction outcomes of garbage growth on different N-number ofobservation

the ARIMA model. In that regard, we conducted a performancemeasurement of our model using mean absolute error (MAE) andstandard deviation (SD) in the centimeter unit of measurements(cm). As shown in Table 3, the forecast window started with thesize of two, then slid to four and, finally, seven window sizes on
the same training size; N=10, N=15, N=20, therefore, the performance was observed and compared from few to higher numbersof observations (N). We obtained the model performance accuracy by using three different training iterations; thus, MAE wasrecorded and averaged. Fig. 12 is an error bar graph achieved during model performance measurements, training the model withten number of observations using the window size of two indicates satisfactory performance, but the number of observation islower. Thus, training the model with twenty number of observations given with the window size of 4 days, as shown on Table 3,
provided the best accuracy on the garbage growth prediction with
(N=20, Average MAE=5.17 cm, SD=0.33 cm). The results showthat the simultaneous increase of both the training and forecast
window sizes provides fewer errors. With the less number of prediction errors during the ARIMA model’s performance measurement indicates the predicted values are closer to the actual observation that offers high efficiency in predicting the future amountof garbage growth behavior on daily practical use. In contrast,
the higher error value above 10% of the smart bin’s maximum fill

7.

Conclusion

This study aims to learn garbage growth behavior in a single
house using IoT sensors and to store the data in a cloud platform
for further processing with the machine learning model. The prediction of garbage growth’s future behavior will enhance a flexible collection schedule; thus, authorities will reduce operationalcosts by eliminating unnecessary garbage collection trips. Therefore, we have designed and developed a customized smart bin capable of storing the garbage data in a cloud platform that was preliminarily deployed in a student laboratory. Data from ToF (timeof flight), DHT22 (temperature and humidity), load cell sensors
via Wi-Fi gateway were collected over thirty days and stored into
the ThingSpeak cloud platform. Further, we have applied a timeseries model ARIMA with a fixed slide forecast window size usedon each N-number of observations in predicting the future amountof garbage growth for the collection schedule and changing of
the garbage bag during the disposal. The result found that the
ARIMA model is suitable for predicting future garbage growth
behavior in a single house because the predicted garbage values follow the actual values of the initial observations. Also, themodel was capable of gathering the fluctuations on observed data,and its application did not require much data.Moreover, the model
performance accuracy error decreased with respect to the simultaneous increasing window size and training size. As future work,6

we will build a model for supporting behavior change of users on
garbage disposal by providing control on throwing garbage andconsidering seasonality fluctuation factors of an area. Also, wewill add other sensors such as an accelerometer for counting thesmart bin’s opening during the disposal of garbage and route optimization for garbage collection in a city as well as increasing
devices lifetime by optimizing energy.

[20]

[21]

[22]

References
[1]
[2]
[3]
[4]

[5]

[6]
[7]

[8]
[9]

[10]
[11]

[12]

[13]
[14]

[15]

[16]

[17]
[18]

[19]

G. Laput, Y. Zhang, and C. Harrison, “Synthetic sensors: Towards
general-purpose sensing,” in Proceedings of the 2017 CHI Conferenceon Human Factors in Computing Systems, 2017, pp. 3986–3999.
P. Reis, R. Pitarma, C. Goncalves, and F. Caetano, “Intelligent systemfor valorizing solid urban waste,” in 2014 9th Iberian Conference on
Information Systems and Technologies (CISTI). IEEE, 2014, pp. 1–4.
N. S. Kumar, B. Vuayalakshmi, R. J. Prarthana, and A. Shankar, “Iotbased smart garbage alert system using arduino uno,” in 2016 IEEE
Region 10 Conference (TENCON). IEEE, 2016, pp. 1028–1034.
S. Thakker and R. Narayanamoorthi, “Smart and wireless waste management,” in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE,
2015, pp. 1–4.
S. Longhi, D. Marzioni, E. Alidori, G. Di Buo, M. Prist, M. Grisostomi, and M. Pirro, “Solid waste management architecture using wireless sensor network technology,” in 2012 5th International Conference
on New Technologies, Mobility and Security (NTMS). IEEE, 2012,
pp. 1–5.
J. Ferrer and E. Alba, “Bin-ct: Urban waste collection based on predicting the container fill level,” Biosystems, vol. 186, p. 103962, 2019.
M. Kannangara, R. Dua, L. Ahmadi, and F. Bensebaa, “Modeling andprediction of regional municipal solid waste generation and diversion
in canada using machine learning approaches,” Waste Management,
vol. 74, pp. 3–15, 2018.
S. A. Ali and A. Ahmad, “Forecasting msw generation using artificial
neural network time series model: a study from metropolitan city,” SN
Applied Sciences, vol. 1, no. 11, p. 1338, 2019.
C. K. M. Lee and T. Wu, “Design and development waste management
system in hong kong,” in 2014 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2014, pp.
798–802.
I. Hong, S. Park, B. Lee, J. Lee, D. Jeong, and S. Park, “Iot-based
smart garbage system for efficient food waste management,” The Scientific World Journal, vol. 2014, 2014.
S. V. Kumar, T. S. Kumaran, A. K. Kumar, and M. Mathapati, “Smartgarbage monitoring and clearance system using internet of things,”
in 2017 IEEE International Conference on Smart Technologies andManagement for Computing, Communication, Controls, Energy andMaterials (ICSTM). IEEE, 2017, pp. 184–189.
M. Talha, A. Upadhyay, R. Shamim, and M. S. Beg, “A cloud integrated wireless garbage management system for smart cities,” in 2017
International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2017, pp. 175–179.
B. Chowdhury and M. U. Chowdhury, “Rfid-based real-time smart
waste management system,” in 2007 Australasian Telecommunication
Networks and Applications Conference. IEEE, 2007, pp. 175–180.
A. Papalambrou, D. Karadimas, J. Gialelis, and A. G. Voyiatzis, “Aversatile scalable smart waste-bin system based on resource-limited
embedded devices,” in 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA). IEEE, 2015, pp. 1–8.
C. J. Baby, H. Singh, A. Srivastava, R. Dhawan, and P. Mahalakshmi,“Smart bin: An intelligent waste alert and prediction system using machine learning approach,” in 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).
IEEE, 2017, pp. 771–774.
T. Bakhshi and M. Ahmed, “Iot-enabled smart city waste management
using machine learning analytics,” in 2018 2nd International Conference on Energy Conservation and Efficiency (ICECE). IEEE, 2018,
pp. 66–71.
N. Sun and S. Chungpaibulpatana, “Development of an appropriate
model for forecasting municipal solid waste generation in bangkok,”
Energy Procedia, vol. 138, pp. 907–912, 2017.
S. Navghane, M. Killedar, and V. Rohokale, “Iot based smart garbage
and waste collection bin,” International Journal of Advanced Researchin Electronics and Communication Engineering (IJARECE), vol. 5,
no. 5, pp. 1576–1578, 2016.
G. Prajakta, J. Kalyani, and M. Snehal, “Smart garbage collection system in residential area,” IJRET: International Journal of Research in

[23]

[24]
[25]
[26]

Engineering and Technology, vol. 4, no. 3, pp. 122–124, 2015.
M. A. Al Mamun, M. Hannan, A. Hussain, and H. Basri, “Wirelesssensor network prototype for solid waste bin monitoring with energy
efficient sensing algorithm,” in 2013 IEEE 16th International Conference on Computational Science and Engineering. IEEE, 2013, pp.
382–387.
M. Adam, M. E. Okasha, O. M. Tawfeeq, M. A. Margan, and B. Nasreldeen, “Waste management system using iot,” in 2018 International
Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, 2018, pp. 1–4.
H. N. Saha, S. Gon, A. Nayak, S. Moitra et al., “Iot based garbage
monitoring and clearance alert system,” in 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2018, pp. 204–208.
P. J. Ayaskanta Mishra, Nisha Ghosh, “Internet of things based waste
management system for smart cities: A real time route optimization
for waste collection vehicles,” International Journal of Computer Sciences and Engineering, vol. 7, pp. 496–503, 4 2019. [Online]. Available: https://www.ijcseonline.org/full p aperv iew.php?paperi d = 4064
available from hhttps://www.tofugu.com/japan/garbage-in-japan/i (accessed 2020-06-29)
available from hhttps://www.city.ikoma.lg.jp/cmsfiles/contents/i (accessed 2020-06-29)
availablefromhhttps://towardsdatascience.com/time-seriesforecasting-using-auto-arima-in-python-bb83e49210cdi(accessed
2020-06-01)

7