AI-Driven Waste Classification and IOT-Based Bin Monitoring with AR Visualization Support: A Review

AI-Driven Waste Classification and IOT-Based Bin Monitoring with AR Visualization Support: A Review

Vedant Mansukh Rathod, Chirag Kamlesh Agarwal

Student, Department of Instrumentation Engineering, AISSMS Institute of Information Technology, Pune-411001, India

Dr. Vandana V Kale

Assistant Professor, Department of Instrumentation Engineering, AISSMS Institute of Information Technology, Pune-411001, India

Abstract

To keep pace with the growing quantities of garbage accumulating in towns is a challenge that has intruded into the economy to become of real concern for public health and environmental sustainability. Poor waste collection is still synonymous with delays in having waste picked up, improper marking off, and poor environmental maintenance. It tries to build a Smart Waste Management System that replaces the traditional waste collection system by signifying the use of Augmented Reality (AR), Internet of Things (IoT), and Artificial Intelligence (AI) in various aspects such as improved segregation, enhanced recycling, and optimized collection. AI-image classification algorithms will detect waste from the camera feed and categorize it into six primary classes: plastic, metal, paper, glass, biodegradable, and cardboard-thus eliminating the entire category by too long a grade classification. IoT provides for a smart bin having sensors to monitor fill levels and waste types and send that data to a server for further processing. AR interface overlays existing information on the user, giving instant rewards for waste disposal into the right compartments and public engagement and awareness. All savings in fuel, optimizations of collection routes, and increased recycling will be from analytics derived from the usage data. Indeed, it has an above-average accuracy of 85% for each differentiated piece of waste as per learning on different datasets by the AI model. The prototypes

Keywords

Smart Waste Management, AR, AI, IoT, Sustainability, Real-Time Monitoring, Waste Segregation

1. INTRODUCTION

   The exponential rise in waste due to rapid urbanization and population increase presents major problems for municipalities throughout the world. The traditional waste management systems have become inefficient, system pickups are delayed, and recycling has become more of an option for these systems. The advances of some new technologies, including IoT, AI, and AR, present an avenue in which waste management practices can be revamped and reshaped. IoT enables the real-time collection of information from smart bins; AI analyzes the data concerning waste, helping to optimize collection; and AR connects users with waste information via interactive visualization. The confluence of these technologies has the greatest potential to achieve intelligent, sustainable, and citizen-centric waste management systems. Thus, this paper aims to showcase these technologies' practical implementation in terms of operational effectiveness and community engagement. Newer technologies like the IoT, AI, and AR are setting the paradigm shift for waste management. In IoT, smart sensors are used inside waste bins, collection vehicles, and treatment facilities. They monitor the real-time level of waste, location of bins, and collection schedule. Dynamic monitoring of collection helps unclog the inefficiencies in the waste collection process and saves expenses and time. The

   data is analyzed using AI algorithms to optimize the collection routes in an efficient way to conserve fuel and limit emissions. Data accumulated over the years can also help forecast future patterns of waste generation, which are useful in planning and rudimentary organizing. Entering AI, there is a whole paddock where these cyclone systems identify and sort recyclable materials, thus increasing recycling rates while minimizing landfill waste.

   In other words, this broad IoT-AI-AR framework addresses full waste management-from collection to waste categorization, recycling, and, at last, public engagement. This is a very specific benefit to IoT, which ensures accurate and permanent monitoring of data sensing; while AI is referred to as the intelligent capacity of the system for excitatory analytic assessment concerning operational and optimization issues. It has an open door to creating a device for community engagement through interactive education and inspiring initiatives. All these technologies will contribute toward holistic solutions for highly complex, contemporary problems related to waste management in line with sustainability and the idea of a circular economy.

2. LITERATURE REVIEW

   Apart from a few, almost all of the references cited in this work represent advancements around the time of its publication as one of the very first developments in smart waste management using Radio Frequency Identification (RFID), Geographic Information Systems (GIS), and Global System for Mobile Communication (GSM). The authors have suggested a semi-automated waste management system with the idea of rectifying the inefficiencies of the traditional setups, such as irregular waste collection intervals and no provision for monitoring the status of the bin in real time. The concept uses RFID tags attached to waste bins to identify and automatically monitor fill levels, GIS to optimize routing for waste collection vehicles, and GSM modules to transmit real-time data to a central monitoring center. The system enables real-time decision-making, reduces human intervention, and offers substantial improvements in fuel consumption by obtaining optimized collection routes. Chronologically, it forms part of the early wave of intelligent waste management systems brought into play from the late 2000s to early 2010s, primarily focusing on real-time data acquisition and communication. Arebey et al.'s study is foundational yet less automated compared to more recent systems utilizing AI and AR and lacking in predictive analytics; Smart city applications are the subject of subsequent studies. In a comparative context, while more modern approaches are integrating AI for pattern recognition or employing the IoT for decentralized monitoring of these systems, this study predominantly emphasized sensor-based, networked feedback loops that are managed via a centralized system. Its primary strengths are operational effectiveness through route optimization and timely data transfer, but weak on advanced data analytics for forecasting or automated waste sorting, and is not able to engage users via an interactive platformnow a common feature of contemporary systems. The paper's other limitation, and thus the research gap it tries to open, lies in the dependence on centralized infrastructure and the GSM network, which can be rather unreliable in remote or underdeveloped areas. It also excludes the concept of user participation in dynamic data visualization or consideration of real-time environmental data- reporting, e.g., room temperature for different kinds of waste; it doesn't speak to potential scaling challenges and cybersecurity from a data-transmission perspective. These gaps create opportunities for newer models that will develop cloud computing, machine-learning capabilities, and user-facing applications to elaborate a more holistic, adaptive, and sustainable smart waste management system. The paper introduced at the IEEE International Conference on Consumer Electronics by H. Longhi, P. Castagnetti, and E. Pivato [2] examined IoT-enabled urban waste management systems in the context of smart cities. Admittedly, the work proposes a conceptual framework whereby smart bin-in the time of IoT-accompanied sensors monitoring fill levels, humidity, and temperature-communicate in real time with centralized systems via wireless technologies, such as GSM or LoRaWAN. The sensor data are now used for dynamic planning of waste collection routes, optimizing the movement of collection vehicles, and eliminating unnecessary trips, fuel consumption, and labor. Indeed, it improves operational efficiency and reduces environmental impact. Hence, humans can now use the system for predictive planning by analyzing historical trends in waste generation; this will enable better allotment of resources by municipalities and preparation for peaks of demand in the future. Thus, the research makes visible the transformative power of IoT from the existing reactive waste management to proactive models. It also provides this paper, which is the second to happen in time chronological order (mid- 2010s), on innovations in smart waste management, dependent on the earlier marked RFID and GIS-based systems like those of Arebey et al. (2011), but built even farther via real-time sensing and networked intelligence. It shifts the move from only automating current processes to developing adaptive systems that are responsive to dynamic environments. In the realm of urban conditions framed under the smart city spirit,

   the model by Longhi et al. is a vast improvement from its predecessors in centralized semi-automated systems by integrating decentralized sensing mechanisms using low-power wide-area networks. This facilitates the scalability and responsiveness of systems. Rather than an RFID-based approach requiring users to read tags, their model has automatic sensing and the capability to implement near real-time adjustments in the route. On the other hand, it lacks certain elements integral for next-generation waste systems, such as AI for data- based decision-making, Augmented Reality for public interaction, or blockchain for transparent data governance, which were considered out of scope in some early works. In addition, some of the gaps identified by this paper have also become the focus of many contemporary research. First, while real-time monitoring and basic analytics are performed on the model, predictive modeling using AI is not provided, which means forecasting for waste generation and recycling stream optimization would not be possible. Second, there is nearly an absence of user engagement or behavioral change, which is critical for waste segregation compliance. Third, the assumption of continual, reliable connectivity is not guaranteed in every urban or rural case. The study by A. T. M. Shamsuzzaman, S. K. Das, and M. M. Ali [3] presents a robust and scalable smart waste management system built on the Internet of Things (IoT) paradigm to address inefficiencies in traditional waste collection mechanisms. The paper proposes an intelligent infrastructure that uses a network of IoT-enabled sensors deployed in waste bins to monitor fill levels, humidity, and temperature in real time. This data is transmitted to a central control system, where decision-making algorithms help optimize collection routes, reduce overflow incidents, and avoid unnecessary waste collection trips. This innovation aims to significantly lower operational costs, environmental impact (via reduced fuel usage and emissions), and human labour while enhancing service quality. The authors validate their system through real-world implementation in urban environments, demonstrating how dynamic scheduling, based on real-time bin status, can increase efficiency compared to conventional static routing systems. In terms of classification and timeline, this work falls into the third wave of smart waste management evolution, emerging around the late 2010s, where IoT systems matured enough to support real-time, autonomous, and scalable municipal services. This wave followed earlier developments like RFID-based bin tagging (as in Arebey et al., 2011) and route optimization via GIS, by integrating edge-level sensing with cloud-based data analysis. The paper by A. H. Bhatti, K. H. Kim, and J. G. Lee [4] introduces a forward-looking smart waste management system that integrates Artificial Intelligence (AI) and Augmented Reality (AR) to address limitations in conventional waste collection and sorting methods. The system primarily focuses on two fronts: first, the use of AI- powered computer vision algorithms to automate the classification and sorting of waste at the source or collection point, and second, the deployment of AR-based applications to guide users in real-time on how to correctly dispose of waste. The AI module employs machine learning models to identify various types of waste items (e.g., plastic, metal, glass), significantly improving sorting accuracy over manual methods, while the AR interface overlays instructional graphics in a users physical environment, thereby educating users on sustainable practices and enhancing compliance with segregation norms. This dual-technology approach not only streamlines backend operations (e.g., reducing landfill waste and improving recycling rates) but also fosters community participation and environmental awareness. The paper by M. Ali, S. M. Abbas, and F. Afzal [5] presents a practical implementation of an IoT-based smart waste management system aimed at improving efficiency in waste collection and reducing environmental pollution. The system utilizes IoT sensors to monitor bin fill levels in real time, enabling dynamic scheduling of waste collection and minimizing unnecessary trips, fuel consumption, and overflow incidents. Real-world applicability is the central point of interest, particularly deployment feasibility and cost. This study fits within the timeline of technological approaches in the third wave of smart waste management (2018-2020), which had a significant focus on scalable sensor networks, cloud data handling, and real-time monitoring. In contrast to earlier works that either talked about theoretical models or focused on isolated technologies, such as RFID, this paper presents a prototype that is working and deployable, linking IoT infrastructure with backend analytics. Regarding sensor functionalities, this paper bears some resemblance to that proposed by Shamsuzzaman et al. in 2019, but focuses on the practical deployment without highly sophisticated analytics or elaborate user interfaces. AI integration for predictive analytics is missing in this system, as are user-centred technologies, like mobile apps and augmented reality (AR) tools, finding application in more recent smart waste applications. The study conducted by Dr. Iftikhar Hussain, Dr. Adel Elomri, Dr. Laoucine Kerbache, and Dr. Abdelfatteh El Omri [6] looks at how IoT-enabled waste bins can work with multi-agent simulation frameworks to provide great improvements in urban waste management in smart cities. Intelligent agents model the waste generation dynamics, sensor-based bin monitoring, and optimized collection routing. This entails real-time detection of fill level, data-driven decision-making, and automated scheduling of rounds of waste collection. Classified within the third-generation smart systems (post-2020), this approach thus goes

   beyond basic monitoring to incorporate behavioural modelling and scenario-based optimization. Compared to conventional systems relying on fixed schedules, the simulation-based model allows adaptive and cost- efficient collection. Unlike earlier RFID/GIS-based approaches, this study integrates forecasting, wireless communication protocols, and simulation analytics. However, the paper does not explore AI-based classification, public engagement interfaces (e.g., AR), or waste type-specific processing. Additionally, real- world implementation or hardware validation is limited to simulation, leaving scalability and field robustness as open research gaps. Nonetheless, this paper is instrumental in showing how multi-agent systems can simulate smart waste operations for performance benchmarking and policy planning. The above discussion was done by Teoh Ji Sheng, Mohammad Shahidul Islm, Norbahiah Misran, Mohd Hafiz Baharuddin, Haslina Arshad, Md. Rashedul Islam [7]. Meaning it's not self-sustaining, highly inefficient, and very costly in keeping a traditional waste management system, as it has wasted schedules. The public has also shown evidence of the ineffectiveness of the existing recycling bin, as people do not recycle their waste accordingly. With the progressions in IoT and AI, the ancient conventional squander management framework can presently be supplanted with sensors embedded within the framework for performing real-time monitoring and better squander management. The current inquiry is about points to create a keen squander management framework utilizing LoRa communication protocol with a TensorFlow-based deep learning model. The sensor information goes to LoRa, whereas real-time question location and classification are done using Tensor Stream. The canister is planned to isolate squander counting metal, plastic, paper, and other common squander compartments, all of which are controlled by the servo engines. Squander classification and question discovery are done utilizing the TensorFlow system with the pre-trained question location demonstrate. This protest location is prepared with pictures of squander produce a solidified deduction chart, which is utilized for question discovery done through the camera associated with the Raspberry Pi 3 Model B+. Hence, it serves as a primary handling unit. An ultrasonic sensor is implanted into each squander compartment to screen the filling level of the squander. According to Vanya Arun, E. Krishna Rao Patro,

   V.S. Anusuya Devi, Amandeep Nagpal, Pradeep Kumar Chandra, and Ali Albawi [8], the challenge of waste disposal has become acute fast due to urbanization, population rise, and economic advancement. According to the World Bank estimates, global waste generation by the year 2050 shall be nearing 3.4 billion tonnes. The study also stresses the need for fine-tuning waste management methods and optimum resource utilization for handling various types of waste, including agricultural waste, industrial waste, municipal solid waste (MSW), e-waste, etc. Developments in artificial intelligence (AI) concerned with various domains have led to increased interest in the employment of AI for achieving efficient management against various wastes. This paper presents the possibility of energy production from non-recyclable waste with the aid of waste-to- energy (WTE) technologies. Types and the amount of waste generated from various sectors, among other relevant concerns in waste management, are described. The various waste management methods are also examined for their efficacy and efficiency in their economic and environmental dimensions. Furthermore, the prediction models used in waste management are assessed based on performance indicators. An innovative IoT technology-based approach to tackle urban solid waste management by Jeni Moni, Pramod Mathew Jacob, Shital Pawar, Renju Rachel Varghese, Prasanna Mani, and Girish K. K [9]. This system consists of intelligent smart bins having sensors, such as ultrasonic sensors, that monitor real-time fill levels of bins. Whenever the waste level crosses an ambient threshold, these sensors send the data such that authorities can be notified timely manner for action on collection. The cost-effective, IoT-enabled waste monitoring system, with ESP8266 Wi-Fi module to enhance ultrasonic sensors by the study by S. Thangam, Gurupriya M; C. Sai Kushal Yadav; M. Harshith; M. Sunil Kumar, [10]. This system mainly monitors the real-time status of the garbage, which is transmitted wirelessly to the cloud platform. As stated by the authors, the system also has many advantages, such as timely notifications for waste collection, reduced manpower requirements, and improved routing planning. The development could be classified as a second-generation smart system (20152020), which mainly emphasizes sensor integration and basic cloud communications, thus providing a scalable platform for urban deployment. Compared to other IoT solutions, this stands out in the aspect of minimal cost hardware implementation, as well as Wi-Fi, but not other advanced features, such as AI-based waste classification, AR interfaces, or route optimization algorithms. The research fully supports real-time monitoring of bins but lacks predictive information or decision-support systems, indicating the absence of intelligent automation with citizen involvement. Overall, a contribution could be made through this system to smart city waste infrastructure, particularly suitable for small-to-mid-scale implementations. According to the authors Marwan, Nurhayati, Nur Aminah, Rusdi Wartapane, Muhammad Ilyas Syarif, and Ibrahim Abduh [11], smart waste management within a framework utilizing wireless communication technologies improves the efficiency of waste collection. Hence, the study proposes a smart bin filled with

   sensors that monitor the fill level of waste and transmit that data wirelessly to a central system via a protocol like GSM or Wi-Fi. Thus, the real-time data enables municipal authorities to stay abreast with updates to intelligently plan their waste collection routes to avoid any potential overflow and minimize unnecessary trips. The authors underline the role of wireless networks in building scalable and flexible systems that can be used in urban settings. In addition, environmental sustainability will be supported through minimized fuel consumption and reduced emissions from garbage trucks. The paper thus presents a practical avenue for technology integration into urban waste management infrastructure, hence promoting cleaner and smarter cities. According to Abin George, Sham H. Mankar, N V S Suryanarayana, Manish Gupta, R Sathish, A Priya [12], the idea evolved here in this paper is a novel deep learning solution in overcoming challenges of solid waste management. The integrating system proposed by the authors will perform actions of monitoring the levels of waste in real time, as well as classification of waste material into segregated garbage using image- based deep learning models. The smart bins using IoT are going to be fitted with cameras and sensors to enable capturing of the visual data in real time of waste items; and these will be deciphered and classified into the types that might include biodegradable, plastic, metal, etc., using convolutional neural networks (CNNs). It automates classification for better recycling as well as minimum human sorting. The paper discusses integrating deep learning with smart cities to improve operational efficiency, reduce human intervention, and finally, render environmental sustainability. The monitoring component helps in reducing the cost of collecting waste by informing the authority when bins are full, thereby optimizing the use of resources with cleaner spaces across cities. So the whole study shows how AI, especially through deep learning factors, would redefine urban waste management. Gouskir Lahcen, Edahbi Mohamed, Gouskir Mohammed, Hachimi Hanaa, and Abouhilal Abdelmoula [13] discuss a method that can be very intelligent and practical in enhancing the efficiency of solid waste management systems by using ML algorithms. The authors have worked toward the integration of ML models for predicting waste generation patterns, improving collection schedules, and optimizing waste segregation. The proper functioning of this system depends on historical and current data collected through IoT-enabled sensors installed inside the bins, as it can forecast when the bins are likely to become full, suggest optimal routes for the collection vehicles, and thus help in minimizing fuel consumption and operational costs. The machine learning approach also plays a critical role in identifying the type of waste and suggesting proper recycling and disposal pathways, greatly contributing to enhancing recycling rates. The research work carried out by Mary P. Varghese, V.S. Anooja,

   R. Akhila, M. Krishnakumar, and Arun Xavier [14] introduces an IoT-enabled smart bin system that has ultrasonic leel sensors to monitor waste containers continuously in fill status. It involves real-time alerts for waste collection and has basic features for waste segregation into biodegradable and non-biodegradable materials using an appropriate sensor configuration. The form falls under the second generation of IoT systems within a timeline of 2015-2020, emphasizing sensing and communication hardware at the level of the internet-connected devices. As compared to other systems, the solution provides the benefit of segregated monitoring but does not extend into AI classification or dynamic route optimization. The strong point lies in its viability for timely actions on the part of municipal authorities to prevent overflow and enhance hygiene and service efficiency. However, there is no provision for data analytics and prediction models, nor is there a user interaction layer such as mobile apps or AR tools. The study thus identifies a gap in intelligent control and citizen engagement while providing real-time awareness with basic segregation for cleaner city environments. Perumalla Dharan, V. M. Manikandan et al. [15] suggest an IoT-based waste monitoring system with a peculiar focus on optimizing routing for waste collection vehicles. The system measures bin fill levels in real-time with ultrasonic sensors, and data sent via wireless communication through microcontrollers to a centralized server allows dynamic routing decisions to be made. This work, positioned in the third generation (post-2020) of smart waste management solutions, moves past simply planning collections based on static scheduling to applying routing algorithms that prioritize emptying full bins over partially filled ones. This conserves fuel, labour, and time. On the contrary, the current system is logistics- oriented, unlike the earlier ones that confined themselves to either monitoring or alerts. The absence of some extra advanced features like AI-based waste classification or AR for the interaction of users is noted as a disadvantage. Novelty is noted in its municipal vehicle-oriented route optimization; the rest of the integration of real-time decision-making, predictive analytics, and hassle-free full-scale implementations remain untouched. Overall, the system fills a gap in collection efficiency but exposes shortcomings in intelligent processing, waste classification, and end-to-end automation.

   Reference Paper	Paper Authors	Technology Summary	Comparative Analysis	Trends/Gaps/Future Directions
   [1]	M. Arebey, M. A. Hannan, H. Basri, and R. A. Begum	RFID + GSM + GIS for real- time monitoring	Early tech with limited intelligence	Lacked AI & IoT. Good foundation but outdated
   [2]	H. Longhi, P. Castagnetti, and E. Pivato	Smart city context with bin monitoring	Emphasized system integration	Needed deeper technical validation
   [3]	A. T. M. Shamsuzzaman, S. K. Das, and M. M. Ali	IoT-based sensors for fill- level monitoring	Effective cloud architecture	No waste classification component
   [4]	A. H. Bhatti, K. H. Kim, and J. G. Lee	AR + AI integration for awareness & monitoring	Pioneered visualization with AR	Limited real-world deployment & datasets
   [5]	M. Ali, S. M. Abbas, and F. Afzal	Practical IoT implementation with sensor nodes	Demonstrated reliability in real settings	Weak on scalability and AI use
   [6]	Dr. Iftikhar Hussain, Dr. Adel Elomri, Dr. Laoucine Kerbache, Dr. Abdelfatteh El Omri	Multi-agent system simulation for model comparison	Analytical & simulation- based	Lack of field implementation
   [7]	Teoh Ji Sheng, Mohammad Shahidul Islam, Norbahiah Misran, Mohd Hafiz Baharuddin, Haslina Arshad	LoRa & TensorFlow for waste classification	Good accuracy with deep learning & long-range comm.	Limited battery/power efficiency analysis
   [8]	Vanya Arun, E. Krishna Rao Patro, V.S. Anusuya Devi, Amandeep Nagpal, Pradeep Kumar Chandra, and Ali Albawi	AI prediction algorithms compared (SVM, RF, CNN)	Comparative ML performance analysis	Lack of integration with physical systems
   [9]	Jeni Moni; Pramod Mathew Jacob; Shital Pawar; Renju Rachel Varghese; Prasanna Mani; Girish K K	Smart bins using sensors and microcontrollers	Real-time monitoring with alerts	Did not explore waste types or prediction
   [10]	S. Thangam; Gurupriya M; C.Sai Kushal Yadav; M. Harshith; M. Sunil Kumar	ESP8266-based system with weight & ultrasonic sensors	Simple, cost- effective prototype	Needed AI or AR features for smart cities
   [11]	Marwan; Nurhayati; Nur Aminah; Rusdi Wartapane;	Wireless communication	Focus on real- time data handling	No integration with AI or cloud analytics

   	Muhammad Ilyas Syarif; Ibrahim Abduh	in smart waste bins
   [12]	Abin George; Sham H. Mankar; N V S Suryanarayana; Manish Gupta; R Sathish; A Priya	Deep learning for garbage separation	CNN-based classification of dry vs wet	Lacked IoT implementation & data diversity
   [13]	Gouskir Lahcen; Edahbi Mohamed; Gouskir Mohammed; Hachimi Hanaa; Abouhilal Abdelmoula	ML-based solid waste classification	Performance comparison of ML models	Future work: real- time camera integration
   [14]	Mary P. Varghese; V.S. Anooja; R. Akhila; M. Krishnakumar; Arun Xavier	IoT with level indicators and segregation logic	Practical system with basic logic	No cloud sync, limited AI decision making
   [15]	Perumalla Dharan; V. M. Manikandan	Optimized routing using smart bin data	Good for reducing fuel cost/logistics	Needs dynamic adjustment based on live data

3. TECHNOLOGIES USED

   The proposed system combines three primary technologies:

   • IoT (Internet of Things): Smart IoT-enabled bins are outfitted with weight sensors, gas sensors, and ultrasonic sensors. These sensors are linked to microcontrollers such as the ESP8266 and send real-time data about the fill level of the bins to the cloud.

   • Artificial Intelligence (AI): CNNs and AIs are the types for waste classification to be created by different waste forms (plastics, metals, papers) and predict the time at which bins are likely to be full. This can be made possible by using libraries such as TensorFlow and Keras present in Python..

   • Augmented Reality (AR): AR applications built in Unity and Vuforia have enabled end users to visualize waste levels and types through a mobile device. This also creates awareness and formalizes waste management at the source.

4. SYSTEM ARCHITECTURE AND METHODOLOGY

   An architecture that connects smart bins with a central server using Wi-Fi brings the capabilities of an ESP8266, collecting data from the ultrasonic sensors measuring the waste level and transmitting it to Firebase. An AI model processes these images or sensor data inputs to classify the type of waste and predict the collection timelines. A web dashboard, visualizing these live instantaneous results, complements the system. Mobile applications on AR (Unity-based) enable users to scan a code or view the bin using the camera to know how much space is filled and its category breakdown visually. The above data-driven solution is credible concerning the timely collection of the waste and helps avoid overflows as well as ensure recycling.

5. FUTURE SCOPE

   Evolving work may encapsulate the integration of blockchain for waste tracking procedures, gamification of AR apps to augment citizens' involvement, and upgraded AI modeling frameworks for improved prediction and classification exercises. Citywide smart infrastructure implementation can facilitate the dynamic scheduling of the collection trucks, air quality monitoring linked to waste emissions, and user incentivization for positive behavioral feedback. Further integration with government platforms for incentive-based recycling can augment the scalability and the adoption of the system.

6. CONCLUSION

The review demonstrates how the joining of AR, AI, and IoT provides a powerful tool for changing urban waste management. These technologies work together to monitor bins, classify waste, visualize data, and engage the community. The system provides a sustainable, real-time, and efficient solution that deals with technical shortcomings while also increasing public participation. With good implementation, the system can change the face of waste management in any city while creating a more livable environment.

REFERENCES

1. M. Arebey, M. A. Hannan, H. Basri, and R. A. Begum, "Solid waste monitoring and management using RFID, GIS, and GSM," Waste Management, vol. 31, no. 8, pp. 1880-1886, Aug. 2011.

2. H. Longhi, P. Castagnetti, and E. Pivato, "Smart waste management for smart cities," in Proceedings of the 2016 IEEE 7th International Conference on Consumer Electronics (ICCE), Las Vegas, NV, 2016, pp. 177-178.

3. A. T. M. Shamsuzzaman, S. K. Das, and M. M. Ali, "Smart waste management system using IoT," Journal of Cleaner Production, vol. 220, pp. 1263-1272, May 2019.

4. A. H. Bhatti, K. H. Kim, and J. G. Lee, "AR and AI-based smart waste management system for a sustainable environment," Sustainability, vol. 11, no. 5, pp. 1444, Mar. 2019.

5. M. Ali, S. M. Abbas, and F. Afzal, "IoT-based smart waste management system: a practical approach," Environmental Science and Pollution Research, vol. 27, no. 14, pp. 17861-17872, May 2020.

6. Dr. Iftikhar Hussain, Dr. Adel Elomri, Dr. Laoucine Kerbache, Dr. Abdelfatteh El Omri, Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation

7. Teoh Ji Sheng, Mohammad Shahidul Islam, Norbahiah Misran, Mohd Hafiz Baharuddin, Haslina Arshad, Md. Rashedul Islam, An Internet of Things-Based Smart Waste Management System Using LoRa and TensorFlow Deep Learning Model

8. Vanya Arun, E. Krishna Rao Patro, V.S. Anusuya Devi, Amandeep Nagpal, Pradeep Kumar Chandra, and Ali Albawi, AI-Based Prediction Algorithms for Enhancing the Waste Management System: A Comparative Analysis

9. Jeni Moni; Pramod Mathew Jacob; Shital Pawar; Renju Rachel Varghese; Prasanna Mani; Girish K, "An Intelligent Smart Bin System for Solid Waste Management in Smart Cities."

10. S. Thangam; Gurupriya M; C. Sai Kushal Yadav; M. Harshith; M. Sunil Kumar, "A Smart Waste Management System Using ESP8266."

11. Marwan; Nurhayati; Nur Aminah; Rusdi Wartapane; Muhammad Ilyas Syarif; Ibrahim Abduh, "Designing smart waste management under a wireless communication system."

12. Abin George; Sham H. Mankar; N V S Suryanarayana; Manish Gupta; R Sathish; A Priya, "Deep learning-based Identification of Solid Waste Management in Smart Cities through Garbage Separation and Monitoring."

13. Gouskir Lahcen; Edahbi Mohamed; Gouskir Mohammed; Hachimi Hanaa; Abouhilal Abdelmoula, "Waste solid management using Machine learning approach."

14. Mary P. Varghese; V.S. Anooja; R. Akhila; M. Krishnakumar; Arun Xavier, "IoT-Based Smart Waste Management System with Level Indicators for Effective Garbage Waste Segregation."

15. Perumalla Dharan; V. M. Manikandan, "A Smart Waste Management System with Optimized Routing Method."