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Citation: Saher, R.; Saleh, M.; Anjum,
M. Holistic Trash Collection System
Integrating Human Collaboration
with Technology. Appl. Sci. 2023,13,
11263. https://doi.org/10.3390/
app132011263
Academic Editors: Tomohiro Tabata,
Maria Cristina Collivignarelli,
Alessandro Abbà and Marco
Carnevale Miino
Received: 16 August 2023
Revised: 27 September 2023
Accepted: 11 October 2023
Published: 13 October 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
applied
sciences
Article
Holistic Trash Collection System Integrating Human
Collaboration with Technology
Raazia Saher 1,* , Matasem Saleh 2,* and Madiha Anjum 1
1Computer Engineering Department, College of Computer Science and Information Technology (CCSIT),
King Faisal University, P.O. Box 400, Hofouf 31982, Al-Ahsa, Saudi Arabia; mshahzad@kfu.edu.sa
2Telecom Division, Emaar Altelal, P.O. Box 7239, Hofouf 31982, Al-Ahsa, Saudi Arabia
*Correspondence: razsaher@kfu.edu.sa (R.S.); matasem@emaaraltelal.com (M.S.)
Abstract:
Effective waste management is of paramount importance as it contributes significantly
to environmental preservation, mitigates health hazards, and aids in the preservation of precious
resources. Conversely, mishandling waste not only presents severe environmental risks but can
also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon
dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression
of global warming and climate change, consequently giving rise to atmospheric pollution. This
pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of
cardiovascular disorders, and negatively impact overall public health. Hence, efficient management
of trash is extremely crucial in any society. It requires integrating technology and innovative so-
lutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary
communication paradigm with significant contributions to remote monitoring and control. IoT-based
trash management aids remote garbage level monitoring but entails drawbacks like high installation
and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and
substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate
a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes
the need for global commitment by policymakers, stakeholders, and civil society, working together
to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and
time, our proposed method leverages quick response (QR) codes to enable the remote monitoring
of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT
devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively
to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at
the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any
technology is its ability to interact and collaborate with humans. Within our framework, citizens will
employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant
garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these
teams will plan for optimized garbage collection procedures, considering parameters such as garbage
volume and the most efficient collection routes aimed at minimizing both time and fuel consumption.
Keywords:
trash collection system; internet of things; remote garbage level monitoring; QR
code-based
waste collection; integrating technology in the waste collection; human collaboration; waste collection
teams; optimized garbage collection procedures
1. Introduction
In the present era, waste production has significantly increased, leading to overflowing
garbage bins throughout the city. This creates unhygienic conditions, affecting the aesthetics
of the surroundings and posing risks of disease transmission [
1
]. Wastes accumulated over
long periods of time in the dump yards can be even more devastating. It can cause the
contamination of land and water bodies. Harmful chemicals released from these wastes can
Appl. Sci. 2023,13, 11263. https://doi.org/10.3390/app132011263 https://www.mdpi.com/journal/applsci
Appl. Sci. 2023,13, 11263 2 of 20
seep into the land and adversely affect the groundwater [
2
]. Effective waste management
can readily mitigate land and water pollution while also curbing the proliferation of
diseases [
3
]. In urban areas, daily waste collection is a labor-intensive task with significant
implications for the environment and society. This necessitates the adept management
of waste truck routes, coupled with a diligent assessment of environmental, economic,
and social factors [4].
A comprehensive trash collection system holds significant relevance and value in
society, particularly with the growing urban populations and the accompanying rise in
daily trash quantities [
5
,
6
]. This presents a significant dilemma for many nations that
lack the financial means to engage sufficient manpower to tackle this problem. Hence,
to address this issue, a smart and intelligent system is needed to minimize the need for
manual labor while maximizing efficiency and delivering superior results. In order to
develop such a system, data plays a pivotal role. Firstly, it encompasses information about
the types of locations requiring garbage collection, such as residential areas, industrial sites,
or marketplaces. Secondly, it includes data on the quantity of waste generated by these
locations, enabling the system to target areas with greater precision. Lastly, it considers
whether the waste is recyclable or non-recyclable [7].
Our innovative holistic trash collection system (HTCS) represents a paradigm shift
in waste management. It amalgamates human participation with cutting-edge technology
to establish a highly efficient and productive framework for trash collection, sorting,
and disposal. The system leverages the versatility of quick response (QR) codes for labeling
garbage bins. QR codes are two-dimensional barcodes that can be easily scanned by
smartphones. They possess the capacity to store up to 7089 alphanumeric characters,
making them ideal for storing various data types, such as website URLs, contact details,
geographical information, and product specifications [
8
]. Through the integration of
QR code-equipped bins and a user-friendly mobile app involving the community, our
proposed system strives to enhance waste reduction, elevate recycling rates, and enhance
the cleanliness and sustainability of communities. Our approach markedly diminishes
the extensive dependency on IoT sensors, a prevalent practice in some of the previous
research on smart bins, where these sensors were the exclusive means of collecting data
related to waste levels [9]. We are not advocating for the complete removal of IoT sensors.
Instead, we propose to utilize them primarily in the central garbage collection facilities,
given that the number of central collection units is significantly lower compared to the
extensively deployed QR-enabled garbage bins installed in the neighborhoods, ensuring a
more resource-efficient approach.
The primary aim of the proposed model is to establish a more organized waste
management system that minimizes the environmental impact of waste disposal and
promotes responsible waste handling. Through the utilization of advanced technology
for waste monitoring and tracking, the system strives to decrease landfill waste, boost
recycling rates, and enhance the overall cleanliness and sustainability of communities.
Our innovative approach employs technology, including QR codes and IoT technology,
alongside community participation to streamline and enhance the waste management
process. In our research, we intend to make the following contributions:
•
Propose an efficient waste collection system utilizing QR codes affixed to trash bins
for real-time data on bin contents when scanned.
•
Optimize waste management team efficiency by streamlining collection routes, result-
ing in time and fuel savings while ensuring that waste collection trucks do not attend
to bins that are not completely filled.
•
Enhance recycling through our framework by employing QR codes to identify food
waste and recyclable/non-recyclable materials and then guide these materials to the
designated processing facilities. This segregation guarantees that recyclable materials
remain unadulterated, simplifying the recycling process, while food waste is directed
for proper processing and reuse.
Appl. Sci. 2023,13, 11263 3 of 20
•
Empower waste management teams with the capability to monitor the volume and
composition of waste gathered from diverse locations, enhancing the transparency
of the entire system. These data can serve to pinpoint areas for enhancing waste
reduction and recycling endeavors and quantifying the effectiveness of waste reduc-
tion initiatives.
•
Engage communities in waste management efforts by employing waste bins equipped
with QR codes. We enhance community involvement by implementing rewards
programs that incentivize recycling when participants provide information about
recyclable and non-recyclable items.
•
Lower the waste management cost with our proposed system by minimizing the
utilization of IoT sensors for each bin, optimizing collection routes, and diminishing
the requirement for manual waste sorting.
• Boost environmental sustainability by limiting IoT sensor use, significantly reducing
e-waste and energy consumption. This lessens toxic landfill waste, lowers resource-
intensive electronics production, cuts greenhouse gas emissions, and promotes a more
sustainable future.
The rest of this paper is structured as follows: Section 2reviews the literature on the
role of QR technology, IoT devices, people, and data in waste management. Section 3
provides an overview of the proposed system. Section 4introduces our framework for
trash collection and discusses several factors for successfully implementing the proposed
framework. Finally, Section 5concludes the paper and discusses future work.
2. Literature Survey
This section discusses the previously published literature on waste collection and
management. The previous efforts mostly discuss IoT-assisted waste collection, IoT-
enabled smart bins [
10
,
11
], or the specific issues related to the waste-management cycle.
Sheng et al. [
12
] proposed an IoT-based system utilizing deep learning models and LoRa
(long-range) communication technology. Rogoff et al. [
13
] discussed automated waste
collection systems that utilize underground vacuum pipes and automated collection vehi-
cles for efficient and hygienic waste disposal. Glouche et al. [
14
,
15
] explored smart waste
management using self-describing complex objects, where waste items are equipped with
RFID tags and other sensors to enable automated identification, sorting, and disposal
processes. When compared to previous efforts, our work proposes an end-to-end holistic
trash collection system with all the implementation details, aspects of human collaboration
with technology, and data analysis algorithms for waste collection trends.
Aparna et al. [
3
] discussed an IoT-assisted waste collection and management system
using QR codes. This is a thoughtful and innovative study that explores the potential of
using internet of things (IoT) technology and QR codes to improve waste collection and
management. The authors propose a system in which waste bins are equipped with IoT
devices and QR codes, allowing for the real-time monitoring of waste levels and efficient
collection. The study provides a detailed description of the system’s architecture and
demonstrates its potential through simulation results. Using QR codes for waste classifica-
tion also adds an interesting feature to the system. Overall, this study presents a promising
solution for addressing the challenges of waste management, particularly in urban areas.
The system’s ability to provide real-time data on waste levels and efficient collection can
greatly improve the effectiveness and efficiency of waste management. Additionally, in-
tegrating QR codes for waste classification enhances the system’s capabilities and makes
it more user-friendly. This study is a valuable resource for researchers and practitioners
working in the field of waste management and IoT.
Taelman et al. [
7
] presented a literature review of waste management practices in
European cities by analyzing current practices, identifying their strengths and weaknesses,
and considering economic, environmental, and social sustainability factors. The authors
also provide examples of best practices and recommendations for city planners and policy-
Appl. Sci. 2023,13, 11263 4 of 20
makers to improve waste management practices in their cities. Unlike our work, this paper
does not provide the technical details of the proposed waste management system.
Pelonero et al. [
16
] proposed a data-centric approach to design an IoT-based garbage
collection system that aims to incentivize citizens to recycle waste and improve their waste
disposal habits. The proposed system utilizes IoT sensors and a mobile app to collect data
on garbage collection and recycling rates. The collected data are then used to incentivize
citizens to recycle more and reduce their waste generation. The system allows citizens to
receive rewards in the form of discounts, vouchers, or loyalty points for their recycling
efforts, and an incentivization mechanism can encourage citizens to recycle more and
contribute to the overall sustainability of the city. The proposed system also includes
features such as the real-time monitoring of waste levels, dynamic route planning, and the
predictive maintenance of garbage collection trucks for efficient and cost-effective waste
management. The paper highlights the importance of citizen participation and engagement
in waste management and proposes a data-driven solution to improve waste management
practices that has the potential to transform the traditional garbage collection service into a
more data-centric and citizen-focused one.
Lee et al. [
17
] propose the creation of an integrated system for managing radioactive
waste, incorporating a QR code-based approach with real-time monitoring capabilities. This
innovative system offers a streamlined and systematic method for handling small-packaged
radioactive waste, covering the entire process from generation to treatment. By doing so, it
aims to reduce the volume of radioactive waste and subsequently lower disposal costs.The
QR code system can store essential information, including the waste generation year,
generator details, container specifications, total radioactivity levels, visual representations
of contents, and physical characteristics. This system can seamlessly connect with on-site
operations through the radioactive waste cycle history management system, significantly
enhancing operational efficiency, minimizing the risk of human error, and bolstering
reliability in radioactive waste management. Additionally, this safety-focused radioactive
waste management system harnesses cutting-edge ICT technologies to enhance monitoring,
tracking, and decision-making processes [18].
Wang et al. [
19
] proposed a municipal waste management system that utilizes deep
learning-based classification and cloud computing to reduce the cost of waste classification,
monitoring, and collection. The system subdivides recyclable waste into six categories
and uses deep-learning convolution neural networks to achieve high accuracy in garbage
classification. The envisioned system incorporates IoT devices equipped with sensors to
monitor both the total waste generation and the operational status of waste containers. This
data is harnessed by the waste management center for real-time decision-making, encom-
passing adjustments to equipment deployment, maintenance schedules, waste collection,
and optimizing vehicle routing plans. MobileNetV3 has been singled out as the optimal
and precise classifier for this particular system.
Vishnu et al. [
20
] examined various technological strategies for solid waste manage-
ment within smart city contexts. The literature is classified into three key categories: RFID,
WSN, and IoT-based methodologies, with IoT-based systems emerging as the most effective
among them. In the realm of automating solid waste management in urban environments,
LoRaWAN stands out as the preferred communication protocol. The research also pinpoints
several areas where further investigation is warranted, including the design of ultra-low-
power nodes, the optimization of energy harvesting techniques for self-sustaining nodes,
bin-based waste segregation, and the utilization of hybrid network architectures.
The paper [
21
] proposes a smart waste management system called the internet of
garbage bins (IoGBs), which uses IoT technology to monitor waste bin fill levels and
optimize waste collection schedules, reducing operational costs and improving the sustain-
ability of waste management.
Smart cities are being introduced globally, with examples in cities like
Mangaluru [22,23]
,
Seoul [
24
], Greater Noida [
25
], and Kerala [
26
] where innovative QR code-based systems
have been successfully implemented for household waste collection. In this system, primary
Appl. Sci. 2023,13, 11263 5 of 20
waste collection vehicles, responsible for collecting refuse from door to door, employ QR
code scanning technology placed at household entrances during the collection process.
Subsequently, the gathered refuse is transferred to secondary collection vehicles, which then
transport it to designated disposal sites. Furthermore, beyond waste management, the QR
codes installed at residences can serve as versatile tools, facilitating various public services,
including the payment of utility bills, property taxes, and telephone bills, among others.
Panainte-Lehadus et al. [
27
] have presented a case study conducted in a specific local-
ity to evaluate the efficiency of different household waste collection methods and statistics
about the community involved in the waste collection. The authors used a combination
of data analysis, surveys, and interviews to collect and analyze data on waste generation,
collection, and disposal. The results showed that the current waste collection system in
the locality was inefficient, with high contamination levels and illegal dumping. The data
analysis also presented that community characteristics such as education level, locality, age,
and gender have a substantial effect on the household waste collection mechanism. The au-
thors recommend several improvements, such as the implementation of separate collection
for organic waste, the use of recycling bins, and education and awareness campaigns to
promote sustainable waste management practices. The study provides valuable insights
into the challenges and opportunities of household waste collection, highlighting the im-
portance of effective waste management strategies in promoting sustainable development.
The analysis of this study holds considerable significance, as it reveals that community
involvement is very important for an effective household trash collection system.
Some trash collection systems have high development and maintenance costs. The au-
thors in [
28
] present a new approach to trash collection that involves an automated system
that can move around autonomously, detect trash, and collect it. The system consists of
a mobile base, a manipulator arm, and a set of sensors, including a camera and a lidar.
The paper describes the design and construction of the system and evaluates its perfor-
mance in simulated and real-world environments. The results demonstrate the feasibility
and effectiveness of the proposed approach, which can potentially improve the efficiency
and sustainability of trash collection.
Vishnu et al. [
29
] proposed a waste management system using IoT devices to track
waste levels in public and residential bins. The proposed system measures the level of
unfilled bins using an ultrasonic sensor, which calculates the ground distance from the
bin’s surface to get the capacity. It is also used to send location, store data for statistics
measurements, and send all of the processed data to the central monitoring station, which
give stats about the level of bins. Further, the decision on waste collection will be made
based on the bin level.
Table 1provides a concise overview of the various studies in the field of trash man-
agement, shedding light on the diversity of approaches used to tackle the different tasks
associated with trash collection systems. QR codes have been used in different contexts
in previous studies. For example, QR codes have been used for tracking domestic waste
segregation [30] or wastebank sorting [31].
Prior studies have primarily focused on the integration of technology and IoT in
various aspects of waste management, including garbage collection, waste segregation,
and recycling. In contrast, our holistic approach not only leverages technology but also
fosters community participation, resulting in elevated recycling rates, cost reductions,
and the implementation of more sustainable waste management practices. Furthermore,
the utilization of QR codes enhances community engagement in waste management and
provides real-time data for informed decision-making.
Appl. Sci. 2023,13, 11263 6 of 20
Table 1. A brief tabular outline of the related works with the proposed trash management tasks.
Trash Management Tasks
Used Techniques QR Code RFID IoT Sensors Data Collection Route Planing Bin Overflow Control
Zhang et al. [32]X X X X
Hannan et al. [33]X X X X X
Anjum et al. [34]X X X X
Anjum et al. [35]X X X X
Nguyen et al. [36]X X X X
Metagar et al. [37]X X X
Pardini et al. [38]X X
Jagannathan et al. [30]X X
Aleyadeh and Taha [39]X X X X
Wandee et al. [40]X X X
Sosunova and Porras [41]X X X X X
Widaningsih and Suheri [
31
]
X X
3. Proposed QR-Based Systems: An Overview
Our system employs a fusion of bins featuring QR codes, central sensor technology,
and a mobile app for community waste tracking and monitoring. Users will categorize
the type of waste within their homes using the mobile app. Bins are equipped with a QR
code that must be scanned by the user to specify the amount of waste they are disposing
of. The data are then transmitted to a central database. The mobile app allows users to
track their waste and recycling habits, set reminders for trash pick-up days, and access
information about local waste management services. The app also includes a rewards
program that incentivizes users to recycle and reduce their waste. The bins at the central
garbage collection facility are equipped with sensors that detect the volume of waste being
dumped and transmit the data to a central database via the IoT. The sensors in these
smart bins are also used to track their location and facilitate maintenance and repair by
the administration.
By combining technology and community engagement, our approach presents a holis-
tic waste management solution. It harnesses machine learning and artificial intelligence
algorithms to analyze and predict waste generation patterns, enabling the optimization
of collection schedules and routes. This optimization yields benefits for both the waste
collection agency and the community, enhancing efficiency and lowering costs. Further-
more, our system promotes community-driven waste management initiatives, including
the establishment of composting and recycling facilities, as well as the implementation of
recycling and composting incentives to encourage resident involvement. In summary, our
proposed approach integrates technology and community participation to develop a more
sustainable and cost-effective waste management system.
Table 2analyzes various aspects related to trash management using a QR code-based
system and an IoT device-based system, outlining the pros and cons associated with each
aspect. The table provides a comparison of monitoring methods, cost considerations,
environmental impacts, human participation, and approaches for monitoring garbage
levels. By assessing these diverse aspects, our proposed approach makes well-informed
decisions regarding the most appropriate method, taking into account factors like cost,
environmental impact, human involvement, and monitoring techniques.
Appl. Sci. 2023,13, 11263 7 of 20
Table 2.
A comparison of the pros and cons of a QR code-based system vs. an IoT device-based
system.
Aspect Quick Response (QR) Code-Based System Internet of Things (IoT) Device-Based System
Cost Economical, minimal upfront investment
Potentially high installation and maintenance costs
for IoT devices
Environmental Impact Low electronic waste production
Contribution to electronic waste through
IoT Devices
Human Involvement Citizen participation in scanning QR codes Reliance on IoT devices for monitoring
Garbage Level
QR codes scanned by citizens for garbage
level reporting
IoT devices installed in garbage bins for
remote monitoring
In the following section, we discuss each entity of the proposed system in detail.
4. Holistic Trash Collection System Architecture
A sustainable society must handle its waste effectively. Given the rising daily waste
volume, developing a more effective and sustainable garbage collection and disposal
strategy is imperative. This idea offers a comprehensive waste disposal system that uses
QR code technology. Figure 1shows the overall flow of information in our proposed holistic
trash collection system.
Figure 1. Flowchart of proposed holistic trash collection system.
4.1. System Components
The system will comprise six main entities, i.e., (1) Mobile App, (2) QR-based Trash
Bin, (3) Sensors at Central Location, (4) Cloud-Based Analytics, (5) Waste Collection Teams,
Appl. Sci. 2023,13, 11263 8 of 20
and (6) Rewards. Figure 2illustrates the system components and the role of each system
entity in terms of the information stored and processed within them.
Figure 2. System Components.
We will explain these entities as follows:
4.1.1. Mobile App
The mobile application will facilitate waste disposal by scanning the QR code on the
trash bin. Figure 3shows the comprehensive flow of the mobile app. The application will
present the user with a menu that includes options for trash classification, bin scanning,
checking awarded rewards, and contacting the authorities in case of any system-related
issues, as depicted in Figure 3A. Within the trash classification screen, users have the choice
to categorize the waste they intend to dispose of according to its type, including options
such as paper, plastic, glass, or food waste, as exemplified in Figure 3B. Once the user has
finished classifying the garbage at home, the next step is to scan the QR code affixed to the
bin located outside their home for waste disposal, as depicted in Figure 3C. Subsequently,
the user is prompted to select the current bin level, allowing them to actively engage in the
community’s collective efforts for effective waste management facilitated by our proposed
holistic trash collection system (HTCS), as illustrated in Figure 3D. The mobile application
will serve as a user-friendly guide, aiding individuals in the process of waste classification
within their homes and its subsequent placement into the bin. This ensures the software we
develop caters to users across diverse age groups. The mobile app will encompass a wide
array of functions, including educating users on waste classification with the assistance of a
robust AI facility. This feature will encourage users to employ their smartphone cameras for
identifying and classifying waste items within their homes. Additionally, consumers have
the capability to report issues related to garbage bins, such as overflow or damage, using
the “Contact Us” feature within the same application. In order to further aid users in the
event that their nearest bin is full, the application will furnish real-time data regarding the
fill levels of the nearest alternative bins. App users will receive notifications and messages
pertaining to waste collection within their proximity, owing to the application’s integration
with the cloud-based analytics system. Furthermore, the app will offer information to users
regarding the nearest landfill, recycling facility, or waste pickup location.
Appl. Sci. 2023,13, 11263 9 of 20
Figure 3. The flow of the Mobile App.
4.1.2. QR-Based Trash Bin
The QR-based trash bins will be crafted to be reliable, efficient, and simple to operate.
The trash bin will include a QR code that can be read by a mobile device running our
mobile app, as shown in Figure 4.
Figure 4. QR-based Trash Bin.
QR codes will serve as tools for monitoring trash bin usage and fill levels. When
users scan the QR code using the mobile app, they can inspect the current trash level
Appl. Sci. 2023,13, 11263 10 of 20
and verify the accuracy of the information provided by the previous user. Additionally,
as a fail-safe measure in case the QR code is damaged, each bin comes equipped with a
unique ID number already stored in the collection team’s database. This ID number serves
as an additional means of identification, enabling users to access and modify the bin’s
information, even if the QR code is unreadable or compromised. Users can adjust the
recorded garbage fill level information if they find discrepancies with the previously saved
data. Furthermore, the application allows users to update the garbage level after disposing
of their waste.
Utilizing the QR code for monitoring trash bin fill levels can significantly enhance route
management in several ways. For instance, it enables the provision of real-time updates on
those bins in need of immediate attention, facilitating the optimization of collection routes
and bolstering waste management efficiency. By directing collection trucks to locations
where they are most needed, this approach reduces travel time and minimizes fuel con-
sumption. Moreover, monitoring trash bin fill levels serves as a preventive measure against
garbage overflow. When bins reach capacity, individuals tend to leave their waste around
the bins, which adversely impacts the environment and contributes to littering. Therefore,
this comprehensive and immediate trash collection system has the potential to enhance
waste management efficiency, leading to a cleaner and more sustainable environment.
QR-Coded Bin Route Optimization Algorithm
For route optimization, Algorithm 1takes a set of QR-coded bins and a set of routes as
the input, outputting an optimized route based on the shortest distance and weight of each
route. It first calculates the total distance and weight of each route and then sorts the routes
in increasing order of distance. It sets the initial route to be the shortest route, and then
iteratively adds bins to the route if the distance and weight constraints are met. Finally, it
returns the optimized route.
Algorithm 1 QR-Coded Bin Route Optimization Algorithm
Input: A set of QR-coded bins B, a set of routes R
Output: An optimized route r∗
foreach route r ∈Rdo
Calculate the total distance drof route r; Calculate the total weight wrof route r;
end
Sort the routes in increasing order of dr; Set the initial route r∗=R1;
while there exists a route r that has not been added to r∗do
foreach bin b ∈Bdo
if bin b is not in r∗then
Calculate the distance
dnew
from the end of
r∗
to
b
; Calculate the weight
wnew
of
r∗
plus
b
;
if dnew ≤
maximum route distance and
wnew ≤
maximum route weight
then
Add bin bto the end of r∗;
end
end
end
end
return Optimized route r∗;
Unreadable QR Code Resolution Algorithm
In order to address issues related to the QR codes affixed to the bins, Algorithm 2
concentrates on rectifying the problems stemming from unreadable or unscannable QR
codes. It systematically attempts to read each QR code, and if any issues are detected, it
prompts the user for the bin’s ID and any details of the encountered problems. The algo-
rithm then assesses whether the problem is recurring or unique. If the problem is found
to be recurring, the specific bin may be removed from the list. However, if the problem
Appl. Sci. 2023,13, 11263 11 of 20
is identified as unique, it will be added to the list of problematic bins requiring manual
inspection or retagging.
Algorithm 2 Unreadable QR Code Resolution Algorithm
Input: A set of QR-coded bins B
Output: A list of problematic QR codes P
foreach bin b ∈Bdo
Scan the QR code on bin b;if QR code is readable then
Record bin ID and its associated data;
else
Add bin ID to the list of problematic QR codes P;
end
end
foreach problematic QR code p ∈Pdo
if problematic QR code p is a repeat then
Remove bin associated with pfrom set of bins B;
end
else if problematic QR code p is unique then
Assign bin to a temporary location for manual inspection and re-tagging;
end
end
In instances where a bin cannot be identified due to a damaged QR code, we have
contingency measures in place. One approach involves the manual entry of the bin’s
unique ID number using the mobile app, which still retains information regarding the bin’s
location and fill level. Additionally, the bin’s GPS co-ordinates can be accessed via the
mobile app for identification in such scenarios. Hence, while the QR code plays a pivotal
role in the proposed process, alternative methods exist for locating and monitoring the fill
levels of the trash bins if the code is absent or unreadable. Irrespective of the method used
for bin identification, it is imperative to establish a system for real-time data collection on
bin fill levels.
4.1.3. Sensors at Central Garbage Collection Facility
Sensors will exclusively be installed within those trash bins located at the central
garbage collection center, serving the sole purpose of gauging garbage levels. These sensors
will leverage IoT technology to promptly transmit data to the cloud. They are expected to
exhibit a prolonged operational lifespan, resilience to environmental factors, and minimal
maintenance requisites. The sensors will employ either infrared or ultrasonic technologies
for ascertaining the fill levels of the bins. Infrared sensors, in particular, harness infrared
light—electromagnetic radiation with a wavelength exceeding that of visible light—to
detect and quantify the bin’s fill level. These sensors function by emitting infrared light
into the trash bin and subsequently measuring the intensity of reflected light. As the trash
accumulates, the reflected light diminishes, facilitating the sensor’s determination of the
fill level. Ultrasonic sensors, on the other hand, use high-frequency sound waves to detect
and measure the fill level of the trash bin. These sensors work by emitting high-frequency
sound waves into the trash bin and measuring the time it takes for the sound waves to
bounce back. As the trash level rises, the time it takes for the sound waves to bounce back
increases, allowing the sensor to determine the fill level.
These sensors will transmit observed data to the cloud-based analytics system to
predict bin fill levels. Upon detecting that a container is either full or in need of atten-
tion, the sensors will promptly alert the garbage collection staff in real time, ensuring
efficient communication without unnecessary repetition. Both infrared and ultrasonic
sensors are widely employed in applications necessitating non-contact detection and mea-
surement of physical attributes, including fill-level assessment in trash bins, distance
measurement, and object detection, among others. The selection of sensor technology is
Appl. Sci. 2023,13, 11263 12 of 20
contingent upon multiple factors, including cost considerations, performance prerequisites,
and prevailing environmental conditions. Consequently, we have the flexibility to employ
both sensor types in our application based on the specific requirements and conditions of
the implementation.
4.1.4. Cloud-Based Analytics
The optimization of garbage collection routes will be facilitated by the cloud-based
analytics system, which utilizes data sourced from users through the mobile app and the
sensors situated at the central garbage collection facility, as shown in Figure 5. Furthermore,
leveraging the historical data on trash levels for each bin maintained by the cloud-based sys-
tems, the system will employ machine learning techniques to predict future waste levels in
these bins and offer optimized collection route recommendations. Additionally, the system
will factor in variables such as traffic conditions, road closures, and potential obstruc-
tions that could affect the collection path. It will also encompass attributes like scalability,
dependability, and security. Ensuring data security, particularly safeguarding the physi-
cal integrity of IoT sensors and employing robust encryption methods, is crucial
[42,43]
.
The system will be hosted on a cloud platform, such as Microsoft Azure or Amazon Web
Services, and will continually enhance the garbage collection and management process
through the utilization of real-time data.
Figure 5. Cloud-based data analytics using app data.
Figure 6illustrates the route optimization algorithm employed within our proposed
system. When the waste bins reach their capacity threshold, users are required to scan the
QR code, transmitting the fill level data to the cloud for subsequent bin emptying. In sce-
narios where multiple bins necessitate servicing concurrently, the cloud-based analytics
system performs calculations to determine the most efficient route, considering factors
such as minimizing distance and avoiding traffic congestion. Subsequently, this optimized
route is relayed to the small garbage truck responsible for collecting waste from the bins
and depositing it at the central waste disposal facility.
Appl. Sci. 2023,13, 11263 13 of 20
Figure 6. Illustration of route optimization using QR-based trash bin and cloud-based analytics.
4.1.5. Waste Collection Teams
The waste collection team will assume responsibility for gathering garbage from the
designated trash bins. Equipped with mobile devices, each team member will have the
holistic waste collection system’s mobile app installed. This integration will empower
mobile phones to receive instantaneous notifications generated by the cloud-based analytics
system. They will receive real-time updates regarding the fill levels of the bins, allowing
them to optimize their collection routes, thereby reducing both collection time and costs.
In order to confirm the completion of a collection task, the teams will utilize their mobile
devices to scan the QR codes affixed to the garbage bins. Furthermore, the team will reset
the garbage level indicator to zero once the contents have been successfully collected from
a specific bin. In order to proficiently operate the technology, the garbage collection team
will undergo comprehensive training.
4.1.6. Rewards
Our system incorporates a reward mechanism designed to incentivize user engage-
ment. In order to fund these incentives, we plan to establish strategic partnerships with
various organizations, offering them access to recyclable materials sourced from the col-
lected waste and through advertisements displayed within our app. These rewards could
be distributed to users in the form of vouchers or additional calling minutes. Additionally,
users may receive increased rewards based on the validation of their provided information
by other users, thus encouraging active participation and the sharing of reliable data.
4.2. Benefits of Using QR-Based Waste Management Systems
A practical, environmentally responsible, and user-friendly garbage collection and
disposal method is provided by the holistic trash collecting system that is being developed
employing QR code technology. It may completely alter how we handle garbage, creating
a cleaner, more sustainable world. Waste will be managed effectively and sustainably
by adopting the recommended approach, which includes sensors, a mobile app, cloud-
based analytics, and garbage collection teams. Moreover, the system will offer alarms and
real-time monitoring, allowing prompt and practical garbage collection.
Appl. Sci. 2023,13, 11263 14 of 20
4.2.1. Efficient Waste Collection
By harnessing QR code-based trash bins that provide real-time updates on garbage
fill levels, waste collection personnel can meticulously strategize their routes, minimizing
the need to travel to bins that don’t require emptying. This approach not only boosts
operational efficiency but also yields substantial cost reductions while drastically reducing
the carbon footprint generated by collection trucks.
4.2.2. Sustainability
Through at-home garbage classification and dedicated sections within the bins for
various waste types, our system ensures that the appropriate waste is deposited in the
correct container. This eliminates the burden of sorting recyclable and non-recyclable
materials at recycling facilities, reducing contamination. Segregation of food waste in
separate containers allows the proper sorting and collection of food waste for effective
food waste handling. Furthermore, real-time monitoring of garbage fill levels reduces
the necessity for frequent collections of partially filled bins, resulting in fuel savings and
reduced emissions. These measures collectively contribute to the establishment of a more
sustainable waste management system.
4.2.3. Recycling and Food Waste Treatment for a Circular Economy
In our proposed holistic waste management system, there is a diversity of waste
streams, including recyclable and non-recyclable materials. Recyclable materials, such as
plastics, paper, glass, and metals, present opportunities for resource recovery and sustain-
able practices, emphasizing the importance of efficient sorting and recycling processes.
In parallel, non-recyclable materials, such as styrofoam, ceramics, and composite materials,
necessitate proper disposal methods to mitigate environmental impacts. Recyclable waste
should be separated at the source, collected, and sent to recycling facilities, while non-
recyclable waste should be disposed of in a landfill or incinerated using environmentally
responsible methods. HTCS allows recyclable waste to be separated at the source, collected,
and sent to recycling facilities, while non-recyclable waste is disposed of in a landfill or
incinerated using environmentally responsible methods, considering the importance of a
circular economy [44].
Food waste is characterized by its unique composition, including biodegradable
materials, chemicals, and organic matter, making it a critical component of the overall
waste stream. The impact of food waste on the environment, greenhouse gas emissions,
and resource depletion is undeniable [
45
]. Our proposed HTCS enables more sustainable
disposal methods and reduces contamination in landfills by separating food waste from
other types of waste at the source (e.g., households and businesses) using dedicated food
waste containers within bins for at-home garbage classification.
Once food waste has been collected by a garbage truck, our system allows several ways
to deal with it, depending on the local waste management practices and infrastructure.
Landfill Disposal: In many areas, food waste is still sent to landfills along with other
trash. However, this is often considered the least sustainable option as it contributes to
methane gas emissions and takes up valuable landfill space [46].
Waste-to-Energy (WTE) Facilities: Some communities have waste-to-energy facilities
that burn food waste and other organic materials to generate electricity or heat. This can
help reduce the environmental impact of food waste disposal while producing energy.
Composting: Food waste can be composted to create nutrient-rich soil amendments.
Depending on the scale, this can be done at home in small compost bins or through larger
municipal or commercial composting facilities. The resulting compost can be used in
agriculture and landscaping [47].
Anaerobic Digestion: Anaerobic digestion is a biological process that breaks down
organic waste, including food waste, in the absence of oxygen. It produces biogas, which
can be used for energy production, and digestate, a nutrient-rich material that can be used
as a soil conditioner.
Appl. Sci. 2023,13, 11263 15 of 20
Industrial Processing: Some food waste, especially from commercial or industrial
sources, may undergo specialized processing to extract valuable components or convert
them into products like animal feed, biofuels, or chemicals.
Animal Feed: In some cases, food waste can be processed and used as animal feed,
provided it meets safety and regulatory requirements. This approach helps reduce waste
while providing a source of nutrition for animals [48].
Food Waste Reduction: As a long-term strategy, reducing food waste at the source
is the most effective way to minimize the need for food waste disposal. Encourage food
producers, businesses, and consumers to adopt practices that reduce food waste generation.
Bioconversion: Emerging technologies, such as black soldier fly larvae or other insects,
can be used to convert food waste into protein-rich insect biomass, which can be used as
animal feed or for other purposes [49].
The specific method chosen for dealing with collected food waste will depend on
local regulations, available infrastructure, environmental considerations, and economic
factors. Many communities are shifting toward more sustainable options like composting
and anaerobic digestion to divert food waste from landfills and reduce its environmental
impact [50].
4.2.4. Cost-Effective
Cost efficiency will be significantly improved through real-time monitoring of fill
levels and optimized collection routes, coupled with a reduction in the number of IoT
sensors. This sensor reduction will lead to reduced installation and maintenance expenses
for the proposed system, ultimately enhancing the financial viability of waste management
for municipalities and waste management entities.
4.2.5. Scalable
The cloud-based analytics system is designed to be scalable, providing the flexibility
to expand the system’s coverage as necessary. Furthermore, it exhibits adaptability to
accommodate changes in population density and variations in trash generation rates and
types. This adaptability allows for seamless adjustments to garbage collection routes,
ensuring optimal waste management efficiency.
Therefore, the proposed methodology provides a holistic approach to waste manage-
ment. It achieves this by incorporating technology, educating citizens about their crucial
role in waste management, and offering incentives to promote community engagement.
This approach aims to optimize the garbage collection process and enhance sustainability.
4.3. Enhancing QR Code by Integrating Additional Information
Garbage bins are equipped with QR codes containing extensive information, including
(1) bin identification, (2) bin location, (3) bin size, and (4) waste type. The incorporation
of supplementary data into these QR codes holds the potential to greatly boost the sys-
tem’s efficiency and effectiveness. This enhancement not only fosters accurate garbage
disposal practices but also guarantees the upkeep and cleanliness of the bins. Moreover, it
encourages increased public engagement in the waste management process.
Table 3provides a descriptive overview of bin statuses based on bin-level measure-
ments. It outlines different levels of bin fill, associated color codes, and corresponding bin
statuses. The table categorizes bin statuses as empty (less than 20% filled, represented by
the color blue), lightly filled (between 20% to 40% filled, represented by the color cyan),
medium filled (between 40% to 60% filled, represented by the color green), partially filled
(between 60% to 80% filled, represented by the color yellow), and full (80% or greater
filled, represented by the color red). Table 4showcases the bin-level and other relevant
statistics encoded within a QR code. The table presents two sample instances of QR codes,
each with a unique QR Code ID. For each QR code, the table includes features such as
Bin ID, Location (latitude and longitude coordinates), Filled Bin Level (expressed as a
percentage), Bin Status (descriptive status of the bin), Bin’s Status by Color (associated
Appl. Sci. 2023,13, 11263 16 of 20
color code), and an optional Description field for additional observations or issues. This
table highlights the encoded information within each QR code, allowing the system to
encode comprehensive data about the bin, including its location, fill level, and status, in the
QR code.
Table 3. Description of bin status according to bin-level measurements.
Filled Bin-Level Color Code for Bin-Level Bin Status
Less than 20% Blue Empty
Between 20% and 40% Cyan Lightly filled
Between 40% and 60% Green Medium filled
Between 60% and 80% Yellow Partially filled
80% or greater Red Full
Table 4. Bin-level and other stats merged in the QR code.
QR Code ID Features Encoded Information
QR_Code1
Bin ID XXXX
Location (Latitude_XXXX,Longitude_XXXX)
Filled Bin Level 55%
Bin Status Medium filled
Bin’s Status by Color Green
Description (Optional) Describe any other observation
QR_Code2
BIN ID YYYY
Location (Latitude_YYYY,Longitude_YYYY)
Filled Bin Level 95%
Bin Status Full
Bin’s Status by Color Red
Description (Optional) Any other issues, e.g., QR unreadable
4.3.1. QR-Embedded GPS Data to Optimize Waste Collection and Cloud-Based Analytics
Our system additionally suggests incorporating both the GPS coordinates and a
unique ID number into each garbage bin’s QR code. This dual inclusion serves as a
fail-safe measure, ensuring that even if the QR code becomes damaged or unreadable,
the bin’s precise location can still be determined using the user’s mobile app coordinates.
For instance, in the event that the QR code on a garbage bin is compromised, users can
resort to their mobile app to access the bin’s location by utilizing the GPS coordinates from
their own mobile device when in proximity to the bin.
Waste management personnel can optimize garbage collection routes and schedules by
leveraging GPS data acquired from both the bins and sensors. Through a thorough analysis
of this GPS data, they can make informed decisions about the most efficient collection
routes, resulting in fuel savings and a reduction in greenhouse gas emissions. Furthermore,
the data collected by the cloud-based analytics system can be seamlessly integrated with
the GPS information obtained from the bins and sensors. This integration offers a holistic
view of the waste management system, encompassing vital details such as the precise
location and fill level of each bin, the efficiency of garbage collection routes, and the overall
impact of waste reduction initiatives.
4.3.2. QR-Embedded Trash Bin History for Efficient Maintenance
Incorporating a bin maintenance and cleaning history within the QR code offers
valuable insights for waste management staff. This data facilitates strategic planning of
routine bin maintenance, ensuring bins are well-maintained and clean. Consequently, this
contributes to a cleaner and more efficient waste management system, enhancing overall
hygiene and aesthetics within the community.
Appl. Sci. 2023,13, 11263 17 of 20
4.3.3. QR-Embedded Contact Information
The QR code can be extended to include contact details for the waste management
team, such as phone numbers or email addresses. This addition allows the public to easily
report issues with the bin or seek additional information, fostering greater user engagement
and participation in maintaining cleanliness. It also contributes to the overall effectiveness
of the proposed waste management system HTCS.
4.4. Sustainability Considerations
While we have predominantly discussed waste collection optimization, we recognize
the interconnectedness of various stages in waste management. Sustainability considera-
tions, including feasibility, circular economy integration, and environmentally responsible
practices, are integral to achieving a more holistic and eco-friendly waste management
system. Our proposed HTCS is financially viable, environmentally responsible, and socially
acceptable, making it a sustainable waste management system.
Financial Feasibility: Our proposed system takes into account the potential cost
savings achieved through optimized waste collection routes, reduced fuel consumption,
and minimized electronic waste from IoT devices. These financial considerations make our
approach economically feasible for municipalities and waste management authorities.
Environmental Impact: By minimizing the deployment of energy-intensive IoT de-
vices and leveraging human collaboration, our system reduces energy consumption and
electronic waste generation. Additionally, optimizing collection routes reduces carbon
emissions, contributing to a lower carbon footprint.
Social Acceptance: Encouraging citizen engagement and participation in waste man-
agement fosters a sense of community responsibility. This social aspect aligns with
sustainable practices by promoting a sense of shared ownership and responsibility for
waste reduction.
5. Conclusions and Future Work
In this research, we present an innovative approach to revolutionizing trash collection,
integrating human collaboration with advanced technology to enhance the efficiency of
waste collection, sorting, and disposal. Our holistic trash collection system (HCTS) incor-
porates QR-coded bins, a mobile app, and IoT sensors to promote waste reduction, boost
recycling rates, and elevate community cleanliness and sustainability. With the implemen-
tation of the holistic trash collection system, the potential for substantial long-term cost
savings in waste management becomes evident. This system not only reduces startup
costs but also minimizes ongoing expenses by effectively crowd-sourcing waste level de-
tection through QR codes, thereby limiting the utilization of IoT devices. Additionally,
optimized garbage collection routes contribute to substantial cost reductions over time.
The environmental benefits of our system are equally significant. By encouraging recycling
and promoting efficient waste disposal practices, it can significantly reduce the volume of
waste destined for landfills. Furthermore, the system’s efficient use of technology reduces
e-waste and minimizes greenhouse gas emissions associated with waste collection vehicles,
thanks to improved garbage collection routes. We have incorporated a user-friendly inter-
face design to enhance the overall usability and efficiency of the holistic trash collection
system. Additionally, we recommend the introduction of a reward system to incentivize
user participation and active engagement with the application. Our system’s architecture
is incredibly adaptable and can be customized to meet the unique requirements of var-
ious communities. Moreover, it seamlessly integrates with existing waste management
infrastructures, such as garbage trucks, waste transfer stations, and recycling facilities,
reducing the necessity for new infrastructure. Thus, our proposed system has the potential
to revolutionize waste disposal practices and offer a promising solution to the escalating
challenges of waste management.
Future research should focus on extensive real-world implementations to gauge the
system’s practicality and user acceptance while harnessing behavioral insights to develop
Appl. Sci. 2023,13, 11263 18 of 20
strategies that positively influence individuals and communities, encouraging active partic-
ipation in sustainable waste management practices. This can lead to refined data analytics
and optimization algorithms for dynamic waste collection routes, alongside exploring
environmental impact assessments and integration with broader smart city initiatives,
ensuring the system’s long-term sustainability and its contribution to urban development.
While our research primarily addresses advanced holistic trash collection, it lays the
foundation for a more sustainable waste management system by efficiently gathering
valuable data on waste generation. In future research and development, there is a need to
further explore the treatment and disposal aspects of waste management. The integration
of innovative recycling and treatment technologies is essential to close the loop and further
enhance the sustainability of waste management practices. Future work also needs to
delve into the environmental and economic assessments of these technologies to provide a
more comprehensive view of their sustainability, including a reduction in environmental
impacts, the promotion of recycling and reusing materials, and minimizing the overall
carbon footprint of the entire waste management system.
Author Contributions:
Conceptualization, R.S. and M.S.; methodology, R.S. and M.S.; investiga-
tion, R.S. and M.S.; resources, R.S. and M.A.; writing—original draft preparation, R.S. and M.S.;
writing—review
and editing, R.S., M.S. and M.A.; visualization, M.S. and R.S.; supervision, R.S.;
project administration, R.S. and M.S.; funding acquisition, R.S. All authors have read and agreed to
the published version of the manuscript.
Funding:
The authors extend their appreciation to the Deputyship for Research and Innovation,
Ministry of Education in Saudi Arabia, for funding this research work (Project number INST040).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Argulwar, P.; Borse, S.; Argulwar, K.N.; Gurunathappa, U.S. IoT-Based Smart Garbage Management System. In Proceedings of
the International Conference on Intelligent Computing and Applications (ICICA), Chennai, India, 10–11 March 202; Springer:
Berlin/Heidelberg, Germany, 2018; pp. 237–245.
2.
Podlasek, A.; Jakimiuk, A.; Vaverková, M.D.; Koda, E. Monitoring and assessment of groundwater quality at landfill sites:
selected case studies of Poland and the Czech Republic. Sustainability 2021,13, 7769. [CrossRef]
3.
Aparna, H.; Bhumijaa, B.; Avila, J.; Thenmozhi, K.; Amirtharaja, R.; Praveenkumar, P.; Umamaheswari, B. IoT Assisted
Waste Collection and Management System Using QR Codes. In Proceedings of the International Conference on Computer
Communication and Informatics (ICCCI), Coimbatore, India, 27–29 January 2021; pp. 1–4.
4.
Anh Khoa, T.; Phuc, C.H.; Lam, P.D.; Nhu, L.M.B.; Trong, N.M.; Phuong, N.T.H.; Dung, N.V.; Tan-Y, N.; Nguyen, H.N.; Duc,
D.N.M. Waste Management System Using IoT-Based Machine Learning in University. Wirel. Commun. Mob. Comput.
2020
,2020,
6138637. [CrossRef]
5.
Knickmeyer, D. Social factors influencing household waste separation: A literature review on good practices to improve the
recycling performance of urban areas. J. Clean. Prod. 2020,245, 118605. [CrossRef]
6.
Fatimah, Y.A.; Govindan, K.; Murniningsih, R.; Setiawan, A. Industry 4.0 based sustainable circular economy approach for
smart waste management system to achieve sustainable development goals: A case study of Indonesia. J. Clean. Prod.
2020
,
269, 122263. [CrossRef]
7.
Taelman, S.E.; Tonini, D.; Wandl, A.; Dewulf, J. A Holistic Sustainability Framework for Waste Management in European Cities
Concept Development. Sustainability 2018,10, 2184. [CrossRef]
8.
Rehman, S.U.; Rasool, R.U.; Ayub, M.S.; Ullah, S.; Kamal, A.; Rajpoot, Q.M.; Anwar, Z. Reliable Identification of Counterfeit
Medicine Using Camera Equipped Mobile Phones. In Proceedings of the International Conference on High-capacity Optical
Networks and Emerging Technologies (HONET), Riyadh, Saudi Arabia, 19–21 December 2011; pp. 273–279.
9. Ramson, S.J.; Moni, D.J. Wireless sensor networks based smart bin. Comput. Electr. Eng. 2017,64, 337–353. [CrossRef]
10.
Gaikwad, M.; Gayke, A.; Kamble, P.; Pisal, S.; Tilekar, S. Smart Waste Management System Using IoT Based Dustbin and QR
System. Int. J. Adv. Sci. Res. Eng. Trends 2021,6, 18–21.
11.
Pardini, K.; Rodrigues, J.J.; Diallo, O.; Das, A.K.; de Albuquerque, V.H.C.; Kozlov, S.A. A Smart Waste Management Solution
Geared Towards Citizens. Sensors 2020,20, 2380. [CrossRef]
Appl. Sci. 2023,13, 11263 19 of 20
12.
Sheng, T.J.; Islam, M.S.; Misran, N.; Baharuddin, M.H.; Arshad, H.; Islam, M.R.; Chowdhury, M.E.; Rmili, H.; Islam, M.T. An
Internet of Things Based Smart WasTe Management System Using LoRa and Tensorflow Deep Learning Model. IEEE Access
2020
,
8, 148793–148811. [CrossRef]
13.
Rogoff, M.J.; Ross, D.; Wood, J.L. Automated Waste Collection–How to Make Sure It Makes Sense for Your Community. Am.
Public Work. Assoc. Report. 2010,77, 44–48.
14.
Glouche, Y.; Sinha, A.; Couderc, P. A smart waste management with self-describing complex objects. Int. J. Adv. Intell. Syst.
2015
,
8, 1–16.
15.
Glouche, Y.; Couderc, P. A Smart Waste Management with Self-Describing Objects. In Proceedings of the International Conference
on Smart Systems, Devices and Technologies (SMART), Rome, Italy, 23–28 June 2013.
16.
Pelonero, L.; Fornaia, A.; Tramontana, E. From Smart City to Smart Citizen: Rewarding Waste Recycle by Designing a Data-Centric
IOT Based Garbage Collection Service. In Proceedings of the International Conference on Smart Computing (SMARTCOMP),
Bologna, Italy, 14–17 September 2020; pp. 380–385.
17.
Lee, J.W.; Jeong, J.Y.; Kim, J.J.; Park, H.S.; Chae, S. A Note on the Design of Waste Management System Using QR Code for
Radioactive Waste. Sustainability 2022,14, 9265. [CrossRef]
18.
Park, H.S.; Jang, S.C.; Kang, I.S.; Lee, D.J.; Kim, J.G.; Lee, J.W. A Detailed Design for A Radioactive Waste Safety Management
System Using ICT Technologies. Prog. Nucl. Energy 2022,149, 104251. [CrossRef]
19.
Wang, C.; Qin, J.; Qu, C.; Ran, X.; Liu, C.; Chen, B. A smart municipal waste management system based on deep-learning and
Internet of Things. Waste Manag. 2021,135, 20–29. [CrossRef]
20.
Vishnu, S.; Ramson, S.J.; Rukmini, M.S.S.; Abu-Mahfouz, A.M. Sensor-based solid waste handling systems: A survey. Sensors
2022,22, 2340. [CrossRef] [PubMed]
21.
Jaid Jim, A.A.; Kadir, R.; Mamun, M.A.A.; Nahid, A.A.; Ali, M.Y. A noble proposal for Internet of garbage bins (IoGB). Smart
Cities 2019,2, 214–229. [CrossRef]
22.
Smart Cities Council. Mangaluru Smart City Begins QR Code Based House-to-House Garbage Collection. Available online: https:
//www.smartcitiescouncil.com/article/mangaluru-smart-city-begins-qr-code-based-house-house-garbage- collection (accessed
on 15 September 2023).
23.
Mangalore City. Smart Way to Collect Waste in Smart City Mangalore by Using QR Codes. Available online: https://
mangalorecity.in/smart-way-to-collect-waste-in-smart-city-mangalore-by-using- qr-codes- check-out-how/ (accessed on 15
September 2023).
24.
SmartCitiesWorld News. Seoul to Use QR Codes for Smarter Management of Trash Bins. Available online: https://www.
smartcitiesworld.net/news/news/seoul-to-use-qr-codes-for-smarter-management-of-trash-bins-6967 (accessed on 15 September 2023).
25.
NDTV. Greater Noida to Go Hi-Tech for Door-to-Door Waste Collection with GPS, CCTVs. Available online: https://www.ndtv.
com/india-news/qr-code-gps-and-cctv-greater-noida-authority-to-go-hi- tech-for-door-to- door-waste-collection-2539684 (ac-
cessed on 15 September 2023).
26.
News18 Telugu. Garbage Collection with QR Code Scanning-Ideal Standing Municipality. Available online: https://pipanews.
com/garbage-collection-with-qr-code-scanning-ideal-standing-municipality-news18-telugu/ (accessed on 15 September 2023).
27.
Panainte-Lehadus, M.; Vulpe, M.; Nedeff, V.; Mosnegutu, E.; Przydatek, G.; Tomozei, C.; Chitimus, D. Study on the Method of
Household Waste Collection: Case Study. Appl. Sci. 2022,12, 7490. [CrossRef]
28.
Othman, H.; Petra, M.I.; De Silva, L.C.; Caesarendra, W. Automated trash collector design. J. Phys. Conf. Ser.
2020
,
1444, 012040. [CrossRef]
29.
Vishnu, S.; Ramson, S.J.; Senith, S.; Anagnostopoulos, T.; Abu-Mahfouz, A.M.; Fan, X.; Srinivasan, S.; Kirubaraj, A.A. IoT-Enabled
solid waste management in smart cities. Smart Cities 2021,4, 1004–1017. [CrossRef]
30.
Jagannathan, U.K.; Balaji, N.; Brunda, A.; Gagan, M. QR Code Enabled Domestic Waste Segregation Tracking. In Proceedings of
the International Conference on Industrial Engineering and Operations Management, Istanbul, Turkey, 7–10 March 2022.
31.
Widaningsih, S.; Suheri, A. Design of Waste Management System Using QR Code for Effective Management in Wastebank.
J. Phys. Conf. Ser. 2021,1764, 012066. [CrossRef]
32.
Zhang, Q.; Li, H.; Wan, X.; Skitmore, M.; Sun, H. An intelligent waste removal system for smarter communities. Sustainability
2020,12, 6829. [CrossRef]
33.
Hannan, M.; Al Mamun, M.A.; Hussain, A.; Basri, H.; Begum, R.A. A review on technologies and their usage in solid waste
monitoring and management systems: Issues and challenges. Waste Manag. 2015,43, 509–523. [CrossRef]
34.
Anjum, M.; Sarosh Umar, M.; Shahab, S. Analysis of IoT and Communication Technologies to Develop Waste Management
Service Framework for Smart City. In Proceedings of the International Conference on Sustainable Expert Systems (ICSES),
Lalitpur, Nepal, 9–10 September 2022; Springer: Singapore, 2022; pp. 691–707.
35.
Anjum, M.; Shahab, S.; Umar, M.S. Smart Waste Management Paradigm in Perspective of IoT and Forecasting Models. Int. J.
Environ. Waste Manag. 2022,29, 34–79. [CrossRef]
36.
Nguyen, S.T.; Le, B.N.; Dao, Q.X. AI and IoT-powered Smart University Campus: Design of Autonomous Waste Management. In
Proceedings of the International Symposium on Electrical and Electronics Engineering (ISEE), Ho Chi Minh, Vietnam, 15–16
April 2021; pp. 139–144.
Appl. Sci. 2023,13, 11263 20 of 20
37.
Metagar, S.M.; Sayyed, F.R.; Singhvi, P. Garbage Management System Using Arduino Device to support Swachh Bharath Mission.
In Proceedings of the OPJU International Technology Conference on Emerging Technologies for Sustainable Development
(OTCON), Raigarh, India, 8–10 February 2023; pp. 1–6.
38.
Pardini, K.; Rodrigues, J.J.; Hassan, S.A.; Kumar, N.; Furtado, V. Smart Waste Bin: A New Approach for Waste Management
in Large Urban Centers. In Proceedings of the Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA, 27–30 August
2018; pp. 1–8.
39.
Aleyadeh, S.; Taha, A.E.M. An IoT-Based architecture for waste management. In Proceedings of the International Conference on
Communications Workshops (ICC Workshops), Kansas City, MO, USA, 20–24 May 2018; pp. 1–4.
40.
Wandee, P.; Bussabong, Z.; Duangkum, S. Application for a Waste Management via the QR-Code System. Int. J. Adv. Comput. Sci.
Appl. 2022,13, 659–664. [CrossRef]
41.
Sosunova, I.; Porras, J. IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review. IEEE Access
2022
,
10, 73326–73363. [CrossRef]
42.
Saleh, M.; Zaman, N.; Abdullah, A.; Saher, R. Message Security Level Integration with IoTES: A Design Dependent Encryption
Selection Model for IoT Devices. Int. J. Comput. Sci. Netw. Secur. (IJCSNS) 2022,22, 328.
43.
Saleh, M.; Jhanjhi, N.; Abdullah, A.; Saher, R. Proposing Encryption Selection Model for IoT Devices Based on IoT Device Design.
In Proceedings of the International Conference on Advanced Communication Technology (ICACT), PyeongChang, Republic of
Korea, 13–16 February 2022; pp. 210–219.
44.
Kassab, A.; Al Nabhani, D.; Mohanty, P.; Pannier, C.; Ayoub, G.Y. Advancing Plastic Recycling: Challenges and Opportunities in
the Integration of 3D Printing and Distributed Recycling for a Circular Economy. Polymers 2023,15, 3881. [CrossRef]
45.
Liu, X.; Li, S.; Chen, W.; Yuan, H.; Ma, Y.; Siddiqui, M.A.; Iqbal, A. Assessing Greenhouse Gas Emissions and Energy Efficiency of
Four Treatment Methods for Sustainable Food Waste Management. Recycling 2023,8, 66. [CrossRef]
46.
Karimi, N. Assessing Global Waste Management: Alternatives to Landfilling in Different Waste Streams—A Scoping Review.
Sustainability 2023,15, 13290. [CrossRef]
47.
Strenner, M.; Chmelíková, L.; Hülsbergen, K.J. Compost Fertilization in Organic Agriculture—A Comparison of the Impact on
Corn Plants Using Field Spectroscopy. Appl. Sci. 2023,13, 3676. [CrossRef]
48.
Salemdeeb, R.; Zu Ermgassen, E.K.; Kim, M.H.; Balmford, A.; Al-Tabbaa, A. Environmental and Health Impacts of Using
Food Waste as Animal Feed: A Comparative Analysis of Food Waste Management Options. J. Clean. Prod.
2017
,140, 871–880.
[CrossRef] [PubMed]
49.
Kawa-Rygielska, J.; Pietrzak, W.; Lennartsson, P.R. High-efficiency conversion of bread residues to ethanol and edible biomass
using filamentous fungi at high solids loading: A biorefinery approach. Appl. Sci. 2022,12, 6405. [CrossRef]
50.
Al-Obadi, M.; Ayad, H.; Pokharel, S.; Ayari, M.A. Perspectives on Food Waste Management: Prevention and Social Innovations.
Sustain. Prod. Consum. 2022,31, 190–208. [CrossRef]
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