| Journal of Collective Sciences and Sustainability
Received: 17 November 2025; Revised: 29 December 2025; Accepted: 29 December 2025; Published Online: 30 December 2025.
J. Collect. Sci. Sustain., 2025, 1(3), 25412 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/css.25412
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
A Comprehensive Review of Smart and Sustainable
Waste Management Using Artificial Intelligence,
Internet of Things, and Robotics
Sneha R. Shegar,* Umesh Dattatray Gosavi, Arpita Ramdas Lende and Trupti Vijay Walunj
Department of Computer Engineering, Samarth College of Engineering & Management, Pune, Maharashtra, 412410, India
*Email: snehashegar1@gmail.com (Sneha R. Shegar)
Abstract
Urbanization and population growth have made waste management more challenging in urban areas such as
parks, streets, and public transport. Traditional waste collection requires significant manual labor and is
inefficient, expensive, and poses health hazards to sanitation workers. Recent advances in robots, Artificial
Intelligence (AI), and the Internet of Things (IoT) have enabled autonomous and intelligent waste management
systems that can detect, collect, and separate waste. This paper reviews intelligent robotic solutions for
automated waste collection and separation. Key subsystems are discussed, including perception, navigation,
manipulation, sorting, and IoT for waste management data generation. Sensor technologies such as ultrasonic,
inductive, and moisture sensors, along with machine vision and deep learning algorithms for waste
classification, are examined. Communication architectures using Global System for Mobile Communications
(GSM), Global Positioning System (GPS), and mobile applications for real-time tracking and operation are also
discussed. Existing automated systems are reviewed, and despite significant progress, long-term challenges
remain related to energy efficiency, outdoor operation, and large-scale deployment. The review further
identifies critical research gaps in system-level integration, autonomy, and scalability, and highlights future
directions toward unified AIIoTrobotic frameworks, energy-aware designs, and adaptive navigation strategies
for sustainable and smart urban waste management.
Keywords: Autonomous waste management; Intelligent robots; Internet of Things (IoT); Artificial intelligence (AI);
Waste segregation.
1. Introduction
The management and disposal of solid waste represent both environmental and operational challenges in
contemporary urban environments.
[1-3]
Changes associated with rapid industrialization, urbanization, and
increased population sizes have led to unprecedented amounts of solid waste generated daily in public space.
[4,5]
Many of the activities that happen in parks, bus stations, railway stations, campuses, marketplaces, and many
other usual public spaces generate extra solid waste, litter, and debris beyond what is already common. Current
waste collection and disposal methodologies remain largely labor-intensive and are typically governed by fixed
or irregular cleaning schedules; such approaches are often suboptimal and inconsistently executed, resulting in
inefficient resource utilization.
[6,7]
As the organic and inorganic waste heaps and volumes grow, a traditional
approach to cleaning is not successful and cannot keep up with the need for cleansing.
[8]
Waste collection often
becomes necessary only after bins overflow and litter spreads into surrounding areas. Such unmanaged solid
waste is aesthetically displeasing and poses serious ecological and public health risks, including the spread of
infectious diseases, blocked drainage systems, and contamination of soil and water resources.
[9-11]
Therefore, the
current crisis around solid waste management and disposal must shift more toward automation where we rely
less on human participation, yet are more efficient and sustainable.
[12,13]
The rapid advancements in robotics, automation, artificial intelligence (AI), and the Internet of Things (IoT)
have made the development of a new form of useful intelligent systems that can perform complicated tasks
autonomously.
[14-18]
Autonomous robots that can perceptually sense their environment, make informed
decisions, and then act on those decisions, are being increasingly used in various industries and markets,
including manufacturing, logistics, healthcare, and agriculture.
[19,20]
The use of robots with autonomous
technology in waste management is relatively new but swiftly advancing.
[21]
Autonomous waste-collecting
robots have multiple sensors, embedded processors, and AI-based autonomous decision-making capabilities,
which allow them to detect, collect, classify, and process waste materials more efficiently than humans.
[22,23]
Autonomous waste-collecting robots are particularly well suited for public spaces characterized by high and
unpredictable waste generation, where continuous cleanliness is essential and manual cleaning may lead to
inefficiencies or disruptions. Their deployment enables uninterrupted waste management operations, thereby
maintaining hygienic conditions and improving overall environmental quality. An autonomous waste-collecting
robot is a developing area of intelligent robotic waste management as it relates to fuzzy robotic perception,
incorporated IoT networks, and all aspects of autonomous waste collection and segregation.
[24]
Despite widespread implementation, existing waste management systems exhibit numerous operational and
structural limitations. Manually collecting waste is laborious, inefficient, and oftentimes unsafe.
[25]
Waste
collectors are often exposed to dangerous materials, sharp objects, and pathogens with no protective gear,
leading to significant hazards to health.
[26-29]
In addition, manual classification of waste is rarely effective at the
collection point, resulting in recycling waste mixed in with non-recycling waste, which undermines recycling
efforts and increases overall landfill volume. Furthermore, irregular schedules and lack of monitoring systems
exacerbate the problems associated with waste control. All of these factors can serve as barriers to realizing an
automated solution that can efficiently collect waste, actively monitor disposal practices in real time, and classify
waste intelligently without the assistance of humans.
The incorporation of AI and IoT into waste management has numerous benefits and paradigm shifts.
[30-32]
AI
machines have perceptive and learning capabilities, so AI machines make predictions, detect, classify, and
distinguish objects of waste using computer vision, sensor fusion, and pattern learning algorithms.
[33]
IoT
provides connectivity that enables bi-directional communication between robots, operators, control centers,
and mobile devices to monitor, track, and ascertain decisions in real-time, thus, ensuring maximum
efficiency.
[34,35]
Waste management robots can detect and learn about the objects comprising waste using
ultrasonic sensors, inductive proximity sensors, moisture sensors, and RGB cameras, allow the robot to
distinguish between waste such as whether it is dry, wet or metallic and make accurate real-time sorting.
[18,36]
Adding GPS and GSM modules can further improve operational efficiency and effectiveness as it provides
tracking of the location tracking and updates the robot status live and alerts for abnormalities such as fault, full
bin or low battery, as illustrated in Fig. 1.
[37]
The emergence of these technologies and their use in waste
management could upend traditional processes to more innovative, coordinated, and sustainable approaches.
Fig. 1: IoT based solid waste handling system. Reproduced from [37].
In the last few years, various researchers and developers have attempted different robotic systems to collect
and sort waste. These robots function with a combination of data from sensors and AI-based decision models
that allow them to detect and pick up waste, as illustrated in Fig. 2.
[38]
Early systems primarily designed for
mobile waste collection in urban streets include DustCart developed in Europe, which relied primarily on
humanrobot interaction.
[39]
Later systems started to include IoT-based modules to allow for remote monitoring
and report on the status of the robot. More recently, systems have utilized computer vision, powered by AI, to
improve sample identification of waste and refuse. For instance, convolutional neural networks (CNNs) and
object detection models such as YOLO and MobileNet, may be used to classify waste materials directly from an
image from the onboard camera of the robot.
[40]
These systems are a notable improvement because they make
the detection of waste significantly more accurate, and allow a robot to handle different types of waste in
different environments, both with and without the assistance of a human operator. However, despite numerous
advances in technologies, most existing robotic waste collection solutions still do not possess full autonomy.
Many robotic waste collection solutions still hybrid forms, relying on operators to direct the solution, predefined
route paths, and limited ability to adapt to working environments; therefore, these solutions cannot easily be
implemented in public engaged spaces.
Although significant progress has been made in robotic and IoT-enabled waste management, several challenges
still hinder the large-scale deployment of fully autonomous waste collection robots.
[41]
Power management
remains a major limitation, particularly in achieving energy-efficient operation and integrating renewable
energy sources such as solar charging. Terrain adaptability is another challenge, as most robots are optimized
for flat surfaces, whereas real-world environments include slopes, curbs, and wet conditions. In addition,
maintenance requirements, system durability, and deployment costs affect scalability and return on investment.
Ongoing research in lightweight materials, efficient motor control, and artificial intelligence is expected to
progressively address these challenges. This review examines the technological foundations, current
deployments, and research trends in robotic waste management, with a focus on perception, navigation, sorting,
and communication systems. It also highlights existing research gaps and outlines future research directions
toward sustainable urban sanitation.
Fig. 2: Conceptual architecture of an intelligent waste management system incorporating AI-based decision
making, IoT sensing, and robotic sorting mechanisms.
2. Literature survey
2.1 Review & taxonomy studies
Maghsoudi et al.
retrieved 9,095 articles on waste management published since 2000 from the Web of Science
(WoS) database.
[42]
Based on these articles, a keyword network comprising 17,808 unique keywords (nodes)
and 79,763 links was constructed. To identify the major research areas in waste management, a community
detection technique was applied to the keyword network, resulting in the identification of ten main
communities. These communities, named according to waste management characteristics and waste types,
collectively represent more than 90% of the overall waste management research. et al. provide a
collection of publications concerning innovative technology towards dealing with the challenges of a modern
city, focusing primarily on innovations in waste management.
[42]
The authors do this through a non-systematic
review using relevant conference papers on sustainability and efficiency of resource utilization. The paper
discusses the key connections between waste management systems (WMS), recycling and circular economy, as
well as plastic pollution. However, research is limited in its extensive or systematic review and lacks consistent
criteria for selecting studies in addition to no formal structure meta-review can be established in the future.
Rodríguez-Guerreiro et al. conducted a Scopus-based literature review on waste management in higher
education institutions, analyzing 293 publications across waste generation and composition, composting, food
waste, and edible food waste.
[43]
The results show a strong increase in research after 2017, with composting
receiving the greatest attention, alongside studies on zero-waste and waste minimization strategies. Hariyani et
al. adopted a systematic review methodology in accordance with the PRISMA guidelines, encompassing the
stages of identification, screening, eligibility, and inclusion.
[44]
Relevant studies were retrieved from the Scopus
database using keyword combinations related to waste management and treatment technologies within titles,
abstracts, and keywords. Following the removal of duplicates, non-English, and inaccessible records, successive
screening and full-text evaluation resulted in a final dataset of 392 articles published between 2000 and 2025,
which were subjected to in-depth analysis. Dawal et al. reviewed 69 studies published between 2018 and 2024
using the PRISMA methodology to examine the application of artificial intelligence, particularly machine
learning and deep learning techniques, in municipal solid waste management.
[45]
The review highlights the
potential of AI to improve decision-making, resource recovery, and circular economy practices, while also
identifying challenges related to data availability, quality, and interdisciplinary integration in achieving
sustainable urban waste management aligned with the UN SDGs. Pardini et al. provide a complete review of
research on IoT architecture and communication protocols in waste management systems.
[46]
This study
examined a series of research papers concerning IoT framework systems for monitoring smart bins and urban
waste. In conclusion, the authors examined how while technological solutions are improving, few IoT systems
really add any extra capability for citizen engagement (which is extremely important to waste collection time
and costs). The gap in the literature is that no design was centered around the human aspect to engage the
community. Anuardo et al. employed a combined research methodology integrating literature review and
content analysis to examine waste management research.
[47]
The literature review facilitated the identification
of research gaps, while content analysis enabled the detection of publication trends, thereby supporting the
qualitative development of policies, initiatives, and a conceptual framework for advancing waste management
research. Sahu et al. reviewed 15 studies examining the application of emerging industrial technologies for
waste sorting in urban systems in India.
[48]
Using content analysis of scientific articles and technical reports, the
authors identified several significant technological innovations in waste sorting practices. However, the study
was geographically limited to Indian metropolitan areas, which the authors acknowledged as a key limitation,
restricting cross-country comparison and broader generalization of findings. Akram et al. provided a review of
ICT applications in waste management with a focus on communication protocols and energy-efficient IoT
deployments.
[49]
The authors highlighted the significance of selecting the appropriate wireless communication
technology in order to optimize the operational life of smart bins, while also recognizing blockchain as a
significant enabler of integrating various stakeholders in a waste-management network. Despite these
contributions, it still seems that there is a gap in the research about how well these ICT have been integrated in
practice from a real-world implementation perspective. Rajeb et al. conducted a taxonomy-driven review of
Internet of Things (IoT) applications in smart cities by retrieving peer-reviewed journal articles from the Scopus
database, selected for its extensive coverage and reliable indexing of high-impact international publishers.
[50]
To
maintain methodological rigor and ensure the quality of the synthesized evidence, the study deliberately limited
its scope to journal articles, notwithstanding ongoing debates in meta-analysis literature regarding publication
type selection, as this strategy was considered most appropriate for developing a robust and comprehensive
taxonomy of IoT-based smart city research.
Ramirez et al.
included a section related to innovative waste systems in their review of articles related to IoT in
smart cities.
[51]
They conducted a review of 22 articles related to the emergence of IoT for smart bins. This review
provided a broad overview of IoT in the smart city ecosystem, but did not provide much technical detail related
to innovative waste systems. The gap in research was that there are no technical specifications for in depth
comparisons to facilitate use in practice Concari et al.
provided a bibliometric and text-mining analysis of 2061
publications assessing citizen recycling behaviors.
[52]
They employed data mining software to identify emerging
trends and thematic associations, and concluded that local city innovative policies were a major influence on
citizen recycling behaviors. The research gap noted in the study was the lack of connections between behavioral
evidence and practical areas of technology in innovative waste systems. D'Amico et al. provides a review that
looks at the digitalization of resource loops in smart cities, focusing specifically on "urban metabolism
circularity."
[53]
The authors reviewed ICT-based tools that digitalize the circulation of materials from
consumption to waste and provided case studies for illustration. The authors found that ICT can be helpful in
hastening the adoption of circular economy practices, although the authors mentioned a lack of empirical
evidence to support their assertions. The gap in the study is a (very) limited number of operational examples
that have shown any measurable environmental impact, specifically referencing the need for operational
implementations to verify the outcomes of the urban metabolism framework. Zoumpoulis et al. presented a
comprehensive review of waste collection bins with smart capabilities, highlighting their critical role in modern
urban environments and sustainable waste management systems.
[54]
The study systematically analyzed 79
selected articles from an initial pool of more than 1,400 publications, focusing on state-of-the-art smart bin
technologies and advanced functionalities such as automated material separation and integrated control
mechanisms. Although a broadly accepted definition of smart bins existscharacterizing them as waste
containers equipped with features such as fill-level monitoring and IoT connectivitythe review identified
substantial gaps in the existing literature. In particular, limitations were observed in material separation
practices, including automated waste segregation, as well as in the maturity and consistency of conceptual
frameworks, which collectively hinder efficient resource recovery and the realization of circular economy
models. Furthermore, the analysis revealed a lack of standardization and uniformity in automation approaches,
with existing studies proposing diverse solutions that exhibit varying strengths and limitations. The absence of
a unified and scalable framework underscores the need for consolidated design principles to advance smart bin
technologies and address sustainability challenges in future smart city deployments.
2.2 IoT architectures & smart-bin designs
Idwan et al.
proposed a waste management system based on IoT technology to enhance the scheduling and
routing of waste collection vehicles within a metropolitan area.
[55]
The study implemented a two-phase
metaheuristic approach that utilized a Genetic Algorithm (GA) to reduce collection distance and the number of
trucks. The study conducted two trials, an IoT trial and a conventional trial, using real geographical data
collected from Islamabad Pakistan. The results showed that the IoT-based system exhibited a considerably
lower total travel distance and total travel time. However, there is still a gap in existing research regarding
extending the system to multi-vehicle and multi-objective optimization, as well as testing robustness in larger
datasets. Antora et al. reported an IoT-enabled smart waste management system that employs sensor data and
image-based machine learning algorithms to classify and segregate waste into multiple compartments, while
enabling real-time monitoring through cloud connectivity.
[56]
Juwariyah et al. reported the design of an
integrated monitoring system for multiple IoT-based smart bins in the context of waste management under the
Industrial Revolution 4.0 paradigm.
[57]
The proposed system utilized a Wemos D1 Mini Pro microcontroller in
conjunction with an HC-SR04 ultrasonic sensor and a DHT22 temperaturehumidity sensor to monitor waste
fill levels and environmental conditions. An integrated real-time monitoring platform was developed using the
Blynk application and Arduino IDE, enabling visualization of garbage height, temperature, and humidity data
on smartphones through virtual indicators and graphical displays. Experimental evaluation was conducted
using two smart bin units, demonstrating the feasibility of centralized real-time waste monitoring. Pebriad et
al.
[58]
reported the design and development of an IoT-based Smart Waste Bin aimed at automated waste
monitoring and management. The proposed system employed barcode sensors for waste type identification, an
ESP32 microcontroller as the primary control unit, and an IoT cloud platform for real-time data visualization
via a web-based dashboard. The study followed an engineering approach based on the waterfall model,
encompassing requirement analysis, system design, implementation, and testing phases. Experimental results
demonstrated a waste detection accuracy of 95% and an average data transmission delay of less than three
seconds, indicating improved efficiency and responsiveness in waste management. The findings support the
feasibility of smart, environmentally sustainable waste management systems. Abba et al. presented an IoT-
based smart garbage monitoring system employing an Arduino Uno microcontroller, ultrasonic sensors, and Wi-
Fi communication to detect and classify bin fill levels as empty, half-filled, or filled.
[59]
The system periodically
displays bin status on an LCD and transmits real-time data to a centralized web server for graphical
visualization. The prototype was developed using Arduino IDE with embedded C, while server-side data
processing was implemented using PHP and simulated in Proteus 8.0. Experimental evaluations confirmed the
-world conditions, demonstrating its potential to support automated waste
monitoring and smart city waste management.Jun-Ho Huh et al. reported the limitations of the standard trash
bagbased waste disposal system implemented in the Republic of Korea since 1995, particularly highlighting
persistent issues of illegal dumping by households and businesses attempting to avoid disposal costs.
[60]
Efforts
by the Seoul Metropolitan Government to mitigate the problem by removing street trash bins proved ineffective,
as illegal dumping continued to cause environmental, hygienic, and odor-related concerns. To address these
challenges while reducing manpower and maintenance costs, the authors proposed an IoT-based smart trash
separation bin model. The study presented three alternative system designs incorporating sensor-based
detection, image processing, and spectroscope technologies, demonstrating the potential of IoT-enabled
solutions to reduce labor requirements and administrative costs in urban waste management. Khan et al.
proposed a novel IoT-enabled, knapsack-based optimization framework for waste collection that leverages
sensor data on waste quantity and toxicity to optimally load collection trucks while prioritizing high-toxicity
waste.
[61]
The model was implemented and evaluated using MATLAB simulations, where it outperformed
conventional waste collection strategies. Experimental results demonstrated an improvement of up to 47% in
high-priority toxic waste collection and a reduction in the number of collection trips, leading to equipment cost
recovery within one year Lu et al. introduces an ICT-based novel waste collection system that uses a hybrid bi-
objective metaheuristic based OEGA, mixing a Whale Optimization Algorithm and Genetic Algorithm for
balancing total cost and workload across multiple disposal centers.
[62]
The proposed method was tested using
real data from the Pudong community in Shanghai and the resulting multi-objective compound had better multi-
objective optimization ability compared to the NSGA-II and MOEA/D. However, there was no implementation of
a real-life case to substantiate the findings of this study. This demonstrates a gap in future research examining
operational feasibility and cost of maintenance for the proposed waste collection system models. Aleyadeh et
al. proposed an IoT-based architecture that addresses two key components of smart waste management.
[63]
The
first component focuses on monitoring the waste volume and composition within bins, as well as environmental
conditions in their surrounding areas. The second component enables dynamic scheduling and optimized
routing of waste collection vehicles based on real-time data transmitted from the bins. The proposed smart bin
design is capable of detecting obstacles and identifying illegal dumping activities in the vicinity. Furthermore,
the routing protocol computes optimal collection paths for servicing filled bins in high-density residential areas
while minimizing travel distance. The integration of these components enhances overall waste collection
efficiency and contributes to a reduction in operational costs and carbon footprint.
Namoun et al. offers a thorough overview of machine learning (ML) applications in solid waste management for
smart cities, specifically through optimizing waste flows organization.
[64]
The authors reviewed 23 articles,
which used artificial neural networks (ANNs) for predicting waste generation and waste disposal behaviors.
The comparative approach, reviewing ML models and algorithms for waste forecasting was the method. Overall,
the study results conclude that ANNs provide the most viable modelling processes for predicting trends in waste
generation, due to its applicability to nonlinear patterns. Namoun et al. noted limited standardized and recently
curated datasets for waste, as well as a lack of benchmark case studies, therefore highlighting a gap in research
to support the development of robust and transferable models to explore datasets for comparisons. Alsabt et al.
explored the application of artificial intelligence (AI) and machine learning (ML) techniques to optimize waste
management strategies with an emphasis on economic efficiency and environmental sustainability.
[65]
Using the
including cleaning and feature selection, to improve model performance. Several ML models, such as regression
techniques, Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost),
were employed to forecast waste generation trends and evaluate waste management alternatives. Additionally,
optimization methods, including linear programming, were used to enhance resource allocation and operational
efficiency. The proposed framework achieved an accuracy of 85 % in waste generation prediction, attributed to
the incorporation of diverse socio-economic variables. Moreover, the optimization model resulted in a 15 %
improvement in operational efficiency, demonstrating the potential of ML-driven approaches for sustainable
and cost-effective waste management. Ali et al. introduced an optimization framework that integrates machine
learning and regression to improve economic and environmental performance in waste management
systems.
[66]
Synthetic datasets simulating the performance of different waste materials, and conversion
technologies as configured and practiced in Malaysia were created to develop linear regression, multilayer
perceptron (MLP), and sequential minimal optimization regression models. The authors identified the MLP as
having the strongest correlation coefficient (0.7169), which was useful to predict the outputs of waste
conversion. However, the authors did not validate the models against real-world, municipal waste data or
analyze their generalizability, representing a notable research gap in using and testing this method in changing
urban waste management processes.
et al
et al
et al
et al
et al
et al
et al
et al
et al
et al
et al
et al
et al
Despite significant advances in smart waste management, IoT integration, and robotic automation, most of the
studies indicates that the development of fully autonomous, intelligent, and adaptable robotic systems for waste
collection and sorting in public environments remains an emerging and underexplored research area. Most
existing studies primarily focus on smart bin designs, IoT-based monitoring systems, and data-driven route
optimization.
[80]
Consequently, there is a notable lack of fully integrated systems capable of autonomously
managing the entire waste handling process, including waste detection, collection, classification, and final
disposal. A key limitation identified across the reviewed studies is the insufficient integration of artificial
intelligence (AI), robotics, and IoT into a unified framework. While several works incorporate IoT-enabled
smart bins for monitoring and alerting, they often rely on human-operated vehicles for physical waste
collection.
[54,56,57]
Conversely, robotic arms and mobile platforms-largely evaluated in laboratory or controlled
environments may support autonomous collection and sorting but frequently lack IoT-based communication
and system-level coordination.
[20,21,40]
This separation between cyber (IoT) and physical (robotic) components
restricts scalability and coordinated operation across large public spaces. Waste classification represents
another critical research gap. Many current systems depend on basic sensor-based segregation using moisture
or inductive sensors, limiting classification to coarse categories such as dry, wet, or metallic waste. Although
vision-based approaches using deep learning techniques (e.g., CNNs and YOLO) have shown promising results
in controlled environments, their robustness in real-world outdoor settings remains limited due to challenges
such as variable lighting, occlusion, and irregular waste shapes.
[23,33]
This highlights the need for robust, sensor-
fused AI models capable of reliable real-time waste classification in dynamic environments. Furthermore, the
majority of reviewed systems operate within simulated or semi-controlled settings, constraining their
applicability to real-world scenarios. Limited attention has been given to practical deployment challenges,
including dynamic obstacle avoidance, uneven terrain, battery constraints, and changing weather conditions.
Autonomous navigation, energy efficiency, and adaptive decision-making under uncertain outdoor conditions
remain insufficiently addressed in existing research.
Finally, the communication and fleet management aspects of autonomous waste collection robots are relatively
under-researched. Few studies explore multi-robot coordination, cloud synchronization, real-time analytics, or
predictive maintenance. Emerging technologies such as edge computing, low-power IoT protocols, and 5G
connectivity offer significant potential to improve scalability, reliability, and operational efficiency, yet remain
largely unexplored in this domain. Future research should focus on developing holistic AIIoTrobotics
frameworks for autonomous waste management, robust vision-assisted classification systems for outdoor
environments, energy-efficient and adaptive navigation strategies, and scalable multi-robot coordination
architectures to support sustainable smart city waste management.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
No data were generated or analyzed during the current study. Therefore, data sharing is not applicable to this
article.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
References
[1]
C. Arteaga, J. Silva, C. Yarasca-Aybar, Solid waste management and urban environmental quality of
public space in Chiclayo, Peru, City and Environment Interactions, 2023, 20, 2023, 100112, doi:
10.1016/j.cacint.2023.100112.
[2]
I. R. Abubakar, K. M. Maniruzzaman, U. L. Dano, F. S. AlShihri, M. S. AlShammari, S. M. S. Ahmed, W. A.
G. Al-Gehlani, T. I. Alrawaf, Environmental sustainability impacts of solid waste management practices
in the global south, International Journal of Environmental Research and Public Health, 2022, 19,
12717, doi: 10.3390/ijerph191912717.
[3]
K. M. O. Landim, D. Ceglia, E. P. Lopes Júnior, S. H. de Oliveira Lima, A systematic review of urban solid
waste management to mitigate greenhouse gas emissions: a quantitative and qualitative approach,
Sustainable Development, 2025, 33, 7474-7512, doi: 10.1002/sd.3510.
[4]
P. Awasthi, G. Chataut, R. Khatri, Solid waste composition and its management: A case study of Kirtipur
Municipality-10, Heliyon, 2023, 9, e21360, doi: 10.1016/j.heliyon. 2023.e21360.
[5]
S. Shahab, M. Anjum, Solid waste management scenario in India and illegal dump detection using deep
learning: an ai approach towards the sustainable waste management, Sustainability, 2022, 14, 15896,
doi: 10.3390/su142315896.
[6]
R. T. Wasai, M. A. Yohannis, Internet of things-based smart waste segregation system for sustainable
Journal of Information and Technology, 2025, 5,
6896, doi: 10.70619/vol5iss10pp68-96.
[7]
J. N. Jebaranjitham, J. D. S. Christyraj, A. Prasannan, K. Rajagopalan, K. S. Chelladurai, J. K. J. Samuel
Gnanaraja, Current scenario of solid waste management techniques and challenges in Covid-19 A
review, Heliyon, 2022, 8, e09855, doi: 10.1016/j.heliyon.2022.e09855.
[8]
H. I. Abdel-Shafy, M. S. M. Mansour, Solid waste issue: Sources, composition, disposal, recycling, and
valorization, Egyptian Journal of Petroleum, 2018, 27, 1275-1290, doi: 10.1016/j.ejpe.2018.07.003.
[9]
R. Tlou, M. Nelisiwe, E. Mariana, The impact of improper waste disposal on human health and the
environment: a case of Umgungundlovu District in KwaZulu Natal Province, South Africa, Frontiers in
Sustainability, 2024, 5, doi: 10.3389/frsus.2024.1386047.
[10]
R. Mahajan, Environment and health impact of solid waste management in developing countries: a
review-Current World Environment, 2023, 18, doi: 10.12944/CWE.18.1.3.
[11]
F. A. Kitole, T. O. Ojo, C. U. Emenike, N. Z. Khumalo, K. M. Elhindi, H. S. Kassem, The impact of poor waste
management on public health initiatives in shanty towns in Tanzania, Sustainability, 2024, 16, 10873,
doi: 10.3390/su162410873.
[12]
E. O. Atofarati, V. O. Adogbeji, C. C. Enweremadu, Sustainable smart waste management solutions for
rapidly urbanizing African Cities, Utilities Policy, 2025, 95, 101961, doi: 10.1016/j.jup.2025.101961.
[13]
A. Lakhouit, Revolutionizing urban solid waste management with AI and IoT: A review of smart
solutions for waste collection, sorting, and recycling, Results in Engineering, 2025, 25, 104018, doi:
10.1016/j.rineng.2025.104018.
[14]
T. M. Kanade, S. B. Sharma, T. K. Savale, A. A. Medhekar, Advancements and evolution of artificial
intelligence (ai) in enhancing robotics: innovations, applications, and future directions. Academy of
Marketing Studies Journal, 2025, 29, 1-18.
[15]
challenges, applications, and future research topics, Sensors, 2025, 25, 765, doi: 10.3390/s25030765.
[16]
P. Das, G. Gargate, IOT for solid waste management in India: a road towards sustainability, 2024
Portland International Conference on Management of Engineering and Technology (PICMET),
Portland, OR, USA, 2024, 1-15, doi: 10.23919/PICMET64035.2024.10653160.
[17]
S. Neema, K. Gor, Smart waste management using IoT, International Journal of Scientific Research in
Science, Engineering and Technology, 2022, 9, doi: 10.32628/IJSRSET229529.
[18]
B. Fang, J. Yu, Z. Chen, A. I. Osman, M. Farghali, I. Ihara, E. H. Hamza, D. W. Rooney, P-S. Yap, Artificial
intelligence for waste management in smart cities: a review, Environmental Chemistry Letters, 2023,
21, 19591989, doi: 10.1007/s10311-023-01604-3.
[19]
A. B. Rashid, MD A. K. Kausik, AI revolutionizing industries worldwide: A comprehensive overview of
its diverse applications, Hybrid Advances, 2024, 7, 100277, doi: 10.1016/j.hybadv.2024.100277.
[20]
L. Li, L. Li, M. Li, K. Liang, AI-driven robotics: innovations in design, perception, and decision-making,
Machines, 2025, 13, 615, doi: 10.3390/machines13070615.
[21]
B.G.M. Mwanza, Robotics in Waste Management: A Smart Technique to Segregate, Recycle, and
Recover Electronic Waste. In: Yatoo, A.M., Sillanpää, M. (eds) Smart Waste and Wastewater
Management by Biotechnological Approaches. Interdisciplinary Biotechnological Advances. Springer,
Singapore. 2025, doi: 10.1007/978-981-97-8673-2_4.
[22]
S. G. González, D. C. García, R. H. Pérez, A. Á. Sanchez, G. V. González, Autonomous waste classification
using multi-agent systems and blockchain: a low-cost intelligent approach, Sensors, 2025, 25, 4364,
doi: 10.3390/s25144364.
[23]
M. Kulshreshtha, S. S. Chandra, P. Randhawa, G. Tsaramirsis, A. Khadidos, A. O. Khadidos, OATCR:
outdoor autonomous trash-collecting robot design using YOLOv4-Tiny, Electronics, 2021, 10, 2292,
doi: 10.3390/electronics10182292.
[24]
S. Fuqaha, N. Nursetiawan, Artificial intelligence and IOT for smart waste management: challenges,
opportunities, and future directions, Journal of Future Artificial Intelligence and Technologies, 2025,
2, 2446, doi: 10.62411/faith.3048-3719-85.
[25]
K. S. Salem, K. Clayson, M. Salas, N. Haque, R. Rao, S. Agate, A. Singh, J. W. Levis, A. Mittal, J. M. Yarbrough,
R. Venditti, H. Jameel, L.Lucia, L. Pal, A critical review of existing and emerging technologies and
systems to optimize solid waste management for feedstocks and energy conversion, Matter, 2023, 6,
3348-3377, doi: 10.1016/j.matt.2023.08.003.
[26]
N. Ferronato, V. Torretta, Waste mismanagement in developing countries: a review of global issues,
International Journal of Environmental Research and Public Health, 2019, 16, 1060, doi:
10.3390/ijerph16061060.
[27]
awareness of occupational hazards and personal protective equipment practices in northern Malawi.
Environmental Health Insights, 2024, 18, doi:10.1177/11786302241303688.
[28]
J. Gutberlet, S. M. Nazim Uddin, Household waste and health risks affecting waste pickers and the
environment in low- and middle-income countries, International Journal of Occupational and
Environmental Health, 2017, 23, 299310, doi: 10.1080/10773525.2018.1484996.
[29]
in a Lassa fever treatment centre in Nigeria, Global Health Journal, 2025, 9, 37-45, doi:
10.1016/j.glohj.2025.02.007.
[30]
D. B. Olawade, O. Fapohunda, O. Z. Wada, S. O. Usman, A. O. Ige, O. Ajisafe, B. I. Oladapo, Smart waste
management: A paradigm shift enabled by artificial intelligence, Waste Management Bulletin, 2024, 2,
244-263, doi: 10.1016/j.wmb.2024.05.001.
[31]
R. Anitha, A. Parthiban, AI-IoT-graph synergy for smart waste management: a scalable framework for
predictive, resilient, and sustainable urban systems, Frontiers in Sustainability, 2025, 6, doi:
10.3389/frsus.2025.1675021.
[32]
N. Kumar, Prabhansu, Artificial intelligence and machine learning techniques for next-generation
waste management system: an overview, Cureus Journal of Computer Science, 2025, 2, es44389-025-
04880-y, doi: 10.7759/s44389-025-04880-y.
[33]
A. Rahmatulloh, I. Darmawan, A. P. Aldya, F. M. S. Nursuwars, WasteInNet: deep learning model for
Cleaner Waste Systems, 2025, 10, 100198, doi:
10.1016/j.clwas.2024.100198.
[34]
H. Nguyen, D. Nawara, R. Kashef, Connecting the indispensable roles of IoT and artificial intelligence
in smart cities: A survey, Journal of Information and Intelligence, 2024, 2, 261-285, doi:
10.1016/j.jiixd.2024.01.003.
[35]
I. Rafiq, A. Mahmood, S. Razzaq, S.H.M. Jafri, I. Aziz, IoT applications and challenges in smart cities and
services, Journal of Engineering, 2023, 125, doi: 10.1049/tje2.12262.
[36]
A. Hoque, M. Padhiary, K. Kumar, J. A. Barbhuiya, The role of robotics in sustainable agriculture and
waste management, In A. J. Obaid, Muthmainnah, S. Rajest, & M. Baron (Eds.), Multidisciplinary
Advancements in Human-AI Augmentation, IGI Global Scientific Publishing, 2026, 265-302, doi:
10.4018/979-8-3373-1987-2.ch012.
[37]
P. S. Kumar, V. B. Sree, N. Ramanjaneyulu, S. Nagurvali, V. Rohith, R. S. Teja, Effective garbage
monitoring system using GSM module, 2024 Third International Conference on Intelligent Techniques
in Control, Optimization and Signal Processing (INCOS), Krishnankoil, Virudhunagar district, Tamil
Nadu, India, 2024, 1-5, doi: 10.1109/INCOS59338.2024.10527614.
[38]
S. Vishnu, S.R.J. Ramson, M. S. S. Rukmini, A. M. Abu-Mahfouz, Sensor-based solid waste handling
systems: a survey, Sensors, 2022, 22, 2340, doi: 10.3390/s22062340.
[39]
A. Snoun, M. K. Mufida, A. A. El-Cadi, T. Delot, AI-Driven innovations in waste management: catalyzing
the circular economy, Engineering Proceedings, 2025, 97, 12, doi: 10.3390/engproc2025097012.
[40]
G. Ferri, A. Manzi, P. Salvini, B. Mazzolai, C. Laschi, P. Dario, DustCart, an autonomous robot for door-
to-door garbage collection: From DustBot project to the experimentation in the small town of Peccioli,
2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 2011, 655-660, doi:
10.1109/ICRA.2011.5980254.
[41]
H. Abdu, M. H. Mohd Noor, A survey on waste detection and classification using deep learning, IEEE
Access, 2022, 10, 128151-128165, doi: 10.1109/ACCESS.2022.3226682.
[42]
M. Maghsoudi, S. Shokouhyar, S. Khanizadeh, S. Shokoohyar, Towards a taxonomy of waste
management research: An application of community detection in keyword network, Journal of Cleaner
Production, 2023, 401, 2023,136587, doi: 10.1016/j.jclepro.2023.136587.
[43]
, A. Papadopoulos, J. Rodrigues, Smart technologies for promotion of energy
efficiency, utilization of sustainable resources and of the waste management, Journal of Cleaner
Production, 2019, 231, 565591, doi: 10.1016/j.jclepro.2019.04.397.
[44]
M.-J. Rodríguez-Guerreiro, V. Torrijos, M. Soto, A review of waste management in higher education
institutions: the road to zero waste and sustainability, Environments, 2024, 11, 293, doi:
10.3390/environments11120293.
[45]
D. Hariyani, P. Hariyani, S. Mishra, M. Kumar Sharma, A literature review on waste management
treatment and control techniques, Sustainable Futures, 2025, 9, 100728, doi:
10.1016/j.sftr.2025.100728.
[46]
I. Dawar, A. Srivastava, M. Singal, N. Dhyani, S. Rastogi, A systematic literature review on municipal
solid waste management using machine learning and deep learning, Artificial Intelligence Review,
2025, 58, 183 doi: 10.1007/s10462-025-11196-9
[47]
K. Pardini, J. Rodrigues, S. Kozlov, N. Kumar, V. Furtado, IoT-based solid waste management solutions:
A survey, Journal of Sensor and Actuator Networks, 2019, 8, 5, doi: 10.3390/jsan8010005.
[48]
R. G. Anuardo, M. Espuny, A. C. Ferreira Costa, O. José Oliveira, Toward a cleaner and more sustainable
world: A framework to develop and improve waste management through organizations, governments
and academia, Heliyon, 2022, 8, e09225, doi: 10.1016/j.heliyon.2022.e09225.
[49]
P. Sahu, S. Shelare, C. Sakhale, Smart cities waste management and disposal system by innovative
system: A review. International Journal of Scientific Research & Technology, 2020, 9, 44674470.
[50]
S. Akram, R. Singh, A. Gehlot, M. Rashid, A. AlGhamdi, S. Alshamrani, D. Prashar, Role of wireless aided
technologies in the solid waste management: A comprehensive review, Sustainability, 2021, 13, 13104,
doi: 10.3390/su132313104.
[51]
A. Rejeb, K. Rejeb, S. Simske, H. Treiblmaier, S. Zailani, The big picture on the internet of things and the
smart city: a review of what we know and what we need to know, Internet of Things, 2022, 19, 2022,
100565, doi: 10.1016/j.iot.2022.100565.
[52]
M. Ramírez, S. Keshtkar, D. Padilla, E. Ramos, M. García, M. Hernández, A. Mogro, J. Mahlknecht, J.
Huertas, R. Peimbert-García, R. A. Ramírez-Mendoza, A. M. Mangini, M. Roccotelli, B. L. Pérez-
Henríquez, S. C. Mukhopadhyay, J. de Jesús Lozoya-Santos, Sensors for sustainable smart cities: a
review, Applied Science, 2021, 11, 8198, doi: 10.3390/app11178198.
[53]
A. Concari, G. Kok, P. Martens, Recycling behaviour: Mapping knowledge domain through bibliometrics
and text mining, Journal of Environmental Management, 2022, 303, 114160, doi:
10.1016/j.jenvman.2021.114160.
[54]
circularity: A review and analysis of circular ci.ty initiatives, Land Use Policy, 2022, 112, 105819, doi:
10.1016/j.landusepol.2021.105819.
[55]
Panagiotis Zoumpoulis, Fotios K. Konstantinidis, Georgios Tsimiklis, Angelos Amditis, Smart bins for
enhanced resource recovery and sustainable urban waste practices in smart cities: A systematic
literature review, Cities, 2024, 152, 2024, 105150, doi: 10.1016/j.cities.2024.105150.
[56]
S. Idwan, I. Mahmood, J. Zubairi, I. Matar, Optimal management of solid waste in smart cities using
Internet of Things, Wireless Personal Communications, 2020, 110, 485501, doi: 10.1007/s11277-019-
06738-8.
[57]
N. A. Antora, M. A. Rahman, A. A. Mosharraf, M. Ibn Ehsan, M. Alve, M. M. Elahi, Design and
implementation of a smart bin using IOT for an efficient waste management system, 2022 25th
International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh,
2022, 774-779, doi: 10.1109/ICCIT57492.2022.10055998.
[58]
T. Juwariyah, L. Krisnawati, S. Sulasminingsih, Design of IoT-based smart bins integrated monitoring
system using Blynk, IOP Conference Series: Materials Science and Engineering, Workshop on
Environmental Science, Society, and Technology (WESTECH 2020) 16-17 October 2020, Makassar,
Indonesia, 2021, 1125, 012078, doi: 10.1088/1757-899X/1125/1/012078.
[59]
F. Fitria, M. S. Pebriadi, A. R. Rakhmawati, W. Windarsyah, A. Khalid, Design and build Internet of
Things (IoT)-based smart waste bin for waste management, Formosa Journal of Multidisciplinary
Research, 2025, doi: 10.55927/fjmr.v4i10.597.
[60]
S. Abba, C. I. Light, IoT-based framework for smart waste monitoring and control system: A case study
for smart cities, Engineering Proceedings, 2020, 2, 90, doi: 10.3390/ecsa-7-08224.
[61]
J-H. Huh, J-H. Choi, K. Seo, Smart trash bin model design and future for smart city, Applied Sciences,
2021, 11, 4810, doi: 10.3390/app11114810.
[62]
S. Khan, B. Ali, A. A. K. Alharbi, S. Alotaibi, M. Alkhathami, Efficient IoT-assisted waste collection for
urban smart cities, Sensors, 2024, 24, 3167, doi: 10.3390/s24103167.
[63]
X. Lu, X. Pu, X. Han, Sustainable smart waste classification and collection system: A bi-objective
modeling and optimization approach, Journal of Cleaner Production, 2020, 276, 124183, doi:
10.1016/j.jclepro.2020.124183.
[64]
S. Aleyadeh, A. -E. M. Taha, An IoT-based architecture for waste management, 2018 IEEE International
Conference on Communications Workshops (ICC Workshops), Kansas City, MO, USA, 2018, 1-4, doi:
10.1109/ICCW.2018.8403750.
[65]
A. Namoun, A. Tufail, M. Khan, A. Alrehaili, T. Syed, O. BenRhouma, Solid waste generation and disposal
using machine learning approaches: A survey of solutions and challenges, Sustainability 2022, 14,
13578, doi: 10.3390/su142013578.
[66]
R. Alsabt, W. Alkhaldi, Y.A. Adenle, H. M. Alshuwaikhat, Optimizing waste management strategies
through artificial intelligence and machine learning - An economic and environmental impact study,
Cleaner Waste Systems, 2024, 8, 2024, 100158, doi: 10.1016/j.clwas.2024.100158.
[67]
S. Boovaneswari, I. Varalakshmi, S. Sarathi, P. Sriram, Artificial intelligence enhanced waste sorting
and classification system for urban recycling, Current Trends in Signal Processing, 2025, 15, 23-32.
[68]
L. Huang, J. Hou, H. Liu, Machine-learning intervention progress in the field of organic waste
composting: Simulation, prediction, optimization, and challenges, Waste Management, 2024, 178,
2024, 155-167, doi: 10.1016/j.wasman.2024.02.022.
[69]
Z. Boudanga, S. Benhadou, H. Medromi, An innovative medical waste management system in a smart
city using XAI and vehicle routing optimization, F1000 Research, 2023, 12, 1060, doi:
10.12688/f1000research.138867.2.
[70]
R. Gupta, Z. H. Ouderji, Uzma, Z. Yu, W. T. Sloan, S. You, Machine learning for sustainable organic waste
treatment: a critical review, npj Materials Sustainability, 2024, 2, doi: 10.1038/s44296-024-00009-9.
[71]
S. A. Bandh, F. A. Malla, S. A. Wani, A. T. Hoang, Waste Management and Circular Economy. In: Bandh,
S.A., Malla, F.A. (eds) Waste Management in the Circular Economy. Springer, 2023, Cham., doi:
10.1007/978-3-031-42426-7_1.
[72]
M. Kulwant, N. Rai, D. Patel, Circular economy and waste management: advancing sustainability
through integration and innovation. In: Mandpe, A., Paliya, S., Shah, M.P. (eds) A vision for
environmental sustainability: overcoming waste management challenges in developing countries,
Environmental Science and Engineering, Springer, Cham, doi: 10.1007/978-3-031-89230-1_12.
[73]
R. Paliwal, From waste to wealth: stepping toward sustainability through circular economy. In: Baskar,
C., Ramakrishna, S., Baskar, S., Sharma, R., Chinnappan, A., Sehrawat, R. (eds) Handbook of Solid Waste
Management. Springer, Singapore. 2022, doi: 10.1007/978-981-16-4230-2_82.
[74]
S. Shooshtarian, T. Maqsood, P. S. P. Wong, Policy intervention of waste management, In: Bandh, S.A.,
Malla, F.A. (eds) Waste management in the circular economy, Springer, Cham. 2024, doi: 10.1007/978-
3-031-42426-7_5.
[75]
J. Treacy, Policy implementation on waste management and achievement of related SDGs. In: Leal
Filho, W., Dinis, M.A.P., Moggi, S., Price, E., Hope, A. (eds) SDGs in the European Region, Implementing
the UN Sustainable Development Goals Regional Perspectives. Springer, Cham. 2023, doi:
10.1007/978-3-030-91261-1_35-1.
[76]
H. Zameer, S. Irshad, S. Sarwar, Waste management policy formulation using environmental
Waste Management and Inclusive Economic Development. Springer, Singapore. 2025, doi:
10.1007/978-981-96-5569-4_5.
[77]
W. Berger, Y. Nagase, Waste management regulation: policy solutions and policy shortcomings,
Scottish Journal of Political Economy, 2018, 65, 205-223, doi: 10.1111/sjpe.12137.
[78]
Chikkahanumaiah, B. H. Anjanappa, A case study on strategies of citizen involvement in waste
management, IOSR Journal of Humanities and Social Science, 2023, 28, 45-49.
[79]
K. Tembo, H. Vasudavan, U. Eaganathan, Citizen-powered waste management system for cleaner
cities, 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing
(ASSIC), Bhubaneswar, India, 2025, 1-6, doi: 10.1109/ASSIC64892.2025.11158334.
[80]
D. Mishra, R. Kumar A. B. bin Abdul Hamid, Empowering sustainable waste management: a
comparative study of machine learning models for citizen engagement, 2025 3rd International
Conference on Disruptive Technologies (ICDT), Greater Noida, India, 2025, 633-638, doi:
10.1109/ICDT63985.2025.10986713.
Publisher Note: The views, statements, and data in all publications solely belong to the authors and
contributors. GR Scholastic is not responsible for any injury resulting from the ideas, methods, or products
mentioned. GR Scholastic remains neutral regarding jurisdictional claims in published maps and institutional
affiliations.
Open Access
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which
permits the non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format,
as long as appropriate credit to the original author(s) and the source is given by providing a link to the Creative
Commons License and changes need to be indicated if there are any. The images or other third-party material
in this article are included in the article's Creative Commons License, unless indicated otherwise in a credit line
to the material. If material is not included in the article's Creative Commons License and your intended use is
not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this License, visit: https://creativecommons.org/licenses/by-
nc/4.0/
© The Author(s) 2025
