AI-Enabled Smart Bin For Waste Management And Rainwater Detection With Real-Time Monitoring

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

AI-Enabled Smart Bin For Waste Management And Rainwater Detection With Real-Time Monitoring

Sanskruti More1 , Akanksha Bhosale2 , Krutika Bhide3 , Khushi Beluse4 , Dr. Nita Patil5

1,2,3,4- Student, Department of Computer Engineering, K. C. College of Engineering, Thane, India 5 – Prof. Department of Computer Engineering, K. C. College of Engineering, Thane, India

Abstract - Current urban waste management practices are contributing to significant operational inefficiencies, including unnecessary fuel consumption, increased travel time, and elevated environmental impact due to ineffective collection methods. Overflowing bins, missed pickups, and the frequent servicing of underfilled containers highlight the shortcomings of traditional systems. These challenges not only strain municipal resources but also lead to higher carbon emissions and reduced service quality. To address these issues, this paper focuses on an advanced solution that integrates Internet of Things (IoT) technology into urban waste management. The system employs smart sensors and automated data collection to remotely monitor bin fill levels in real time, improving the efficiency of waste collection operations. By continuously gathering bin status data, the system enables route optimization, cutting fuel usage by 25% and minimizing travel time, which enhances operational efficiency and reduces environmental impact. The system analyzes historical data to identify patterns in waste generation, such as peak disposal periods and highwaste areas. This insight supports predictive planning, allowing waste management teams to allocate resources effectively and schedule collections based on actual needs. As a result, resource utilization is improved, while bin overflows and unnecessary collection trips are significantly reduced, promoting a more sustainable and efficient waste management process.

Key Words: IoT, Sensors, Arduino, Smart Wastage Monitoring, AI, Optimization

1.INTRODUCTION

Waste management plays a vital role in ensuring hygiene and environmental sustainability. However, conventional waste disposal methods often encounter issues such as overflowing bins, inefficient collection processes, and delayed pickups. These challenges contribute to environmental hazards, health risks, and increased strain on municipal resources. To mitigate these issues, advancements in technology, particularly the Internet of Things (IoT), have been introduced to enhance waste management efficiency [2]. By incorporating IoT, microcontrollers such as Arduino, and sensor technologies, real-time waste monitoring has become feasible. Smart waste bins equipped with ultrasonic and liquid sensors can detect waste accumulation levels and

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help prevent overflow, odor problems, and inefficient wastecollection.

The integration of IoT-based solutions helps optimize waste collection schedules by reducing unnecessary trips, which in turn lowers fuel consumption and greenhouse gas emissions Additionally, the implementation of wireless communication modules, such as NodeMCU, allowsseamlessdatatransferforreal-timemonitoringand decision-making. Such data-driven insights assist authorities in managing waste more effectively by identifyingtrendsandoptimizingcollectionroutes.

Further advancements, including liquid waste sensors, expand the capabilities of smart waste management systems by addressing both solid and liquid waste challenges. These innovations enhance urban cleanliness, improve public health conditions, and ensure a more efficient and adaptive waste management infrastructure The following sections will provide a detailed literature survey analyzing prior research on IoT-based waste management systems, their advantages and disadvantages. Furthermore, the paper will cover the methodology used for implementing smart waste management, presenta conclusion,anddiscussthefuture scope for improving waste management systems through emergingtechnologies.

2.LITERATURE SURVEY

K. Mehta and L. Verma (2024) introduced an AI-driven smart waste management system utilizing IoT and machine learning. IoT sensors placed in bins monitor waste levels and send real-time data, while cloud data processing is used to store and analyze waste accumulationpatternsforimprovedmanagement.[1]

G. Uganya, D. Rajalakshmi,Y. Teekaraman, R. Kuppusamy, and A. Radhakrishnan (2023) developed an IoT-based intelligent waste management system. The system uses IoT sensors to collect real-time waste data and applies machine learning algorithms to predict waste trends, enablingbetterplanningandresourceallocation.[2]

H.Patel andV.Desai(2023)workedonsustainablewaste management through IoT and cloud integration. IoT sensorsdetectwastelevelsandtransmitdatatothecloud,

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

where optimization algorithms analyze the data to plan efficientwastecollectionroutes.[3]

A. Rao and N. Kumar (2023) presented a smart garbage collection system that combines IoT and blockchain. The IoTsensorsmonitorwastelevelsandtransmitdata,while blockchain technology ensures secure and transparent managementofthewastecollectionprocess.[4]

P.SharmaandD.Joshi(2022)developedareal-timewaste monitoringsystemusingsmartbins.Thesesmartbinsare equippedwithIoTsensorstodetectwastelevelsandsend real-time data. Additionally, the system includes a user engagement module that rewards individuals for proper wastedisposal.[5]

Rezania Agramanisti Azdy and Febriyanti Darnis (2020) proposedtheuseoftheHaversineformulatocalculatethe shortest distance using GPS coordinates. This approach helpsoptimizeroutesandimprovestheefficiencyofwaste transportbetweentemporarysheltersandendprocessing sites.[6]

3.METHODOLOGY

The smart waste management system utilizes an array of sensors and microcontroller technology to efficiently monitor and manage waste bins. The ultrasonic sensor measures the distance between itself and the waste, providingaccuratedataonthefilllevelofthebin,whether it’sempty,partiallyfull,orfull.

The water sensor detects the presence of liquid waste, allowing the system to differentiate between dry and liquid waste, which is vital for effective waste disposal. TheGPSmoduletracksthebin’spreciselocation,aidingin mappingandoptimizingcollectionroutes.

Data from these sensors is processed by an Arduino Uno, whichcategorizesthebin’sstatusanddisplaysitona16x2

LCD screen for easy on-site monitoring. The processed data is transmitted wirelessly via NodeMCU to a remote web server, where it is stored in a MySQL database for analysisandfutureuse.

This integration of sensors and technology streamlines waste management, making it more efficient, responsive, anddata-driven.

Layer 1: Sensor Data Collection

Layer 1 focuses on gathering real-time data from various sensors embedded in the waste bins. It starts with the ultrasonic sensor, which is responsible for measuring the distance between itself and the waste inside the bin. This measurementallowsthesystemtodeterminehowfullthe bin is, such as whether it's empty, partially full, or nearly full.Next,thewatersensordetectsthepresenceofliquids in the bin. It identifies if there is a significant amount of liquid waste present, helping distinguish between dry waste and liquid waste, which is useful for managing the type of disposal needed. Finally, the GPS module records the exact location of the bin by capturing the latitude and longitudecoordinates.Thisdataiscrucialfortrackingand mapping the locations of the bins, which assists in optimizing the collection process. Together, these sensors provide essential, real-time information that gets forwardedtothenextlayer,ensuringthesystemisalways up-to-date with the status of each bin. All of this data is essential for making informed decisions at the higher layers of the system, helping waste management teams betterplanandoptimizeoperations

Layer 2: Data Processing with Arduino

In Layer 2, the data collected by the sensors in Layer 1 is processed and managed by an Arduino Uno. This microcontroller is responsible for gathering the raw data from all the sensors. The Arduino analyzes the data, particularly from the ultrasonic sensor, to determine whether the bin is full, empty, or partially filled, categorizing the bin's status accordingly. For example, if the ultrasonic sensor indicates that the distance from the sensortothewasteisminimal(threshold15),theArduino can classify the bin as full. Additionally, the Arduino manages the display of this status on a 16x2 LCD screen

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

attached to the bin, which provides waste collection workerswithaclearvisualindicatorofthebin’scondition right on-site. This is incredibly useful for workers to determinewhichbinsneedtobecollectedwithouthaving to rely solely on remote data. Once the data is processed, the Arduino sends this information to NodeMCU through serialcommunication,usingadefinedpacketstructurefor easy transmission. The processed data from the Arduino plays a crucial role in determining when collection is needed, serving as a real-time representation of the bin's condition.

Layer 3: Data Transmission to Server

Layer 3 serves as the communication bridge between the hardware components and the backend server. In this layer, the NodeMCU, which is a Wi-Fi-enabled microcontroller, receives the processed data from the Arduino and transmits it over the internet using HTTP requests. These HTTP requests contain the data gathered by the sensors, such as the fill level, water presence, and GPScoordinatesofthe bin.Thedata isthensenttoa web server, which processes and stores the information in a MySQL database. On the server side, PHP scripts manage this data, organizing it into a structured format for easy retrieval and analysis. The MySQL database ensures that historical data about eachbin,including its fill trendsand geographicallocation,isstoredforfutureuse.Thislayeris essentialbecauseitenablesremotemonitoringandaccess to the bin data, allowing administrators and waste collection teams to view the status of bins at any time. Additionally, this layer enables the seamless flow of data from the hardware to the backend system, providing a foundationforpredictiveanalyticsandrouteoptimization inthehigherlayersofthesystem.

Layer 4: Prediction and Route Optimization

Layer4usesthedatacollectedandstoredintheprevious layers to make intelligent predictions and optimize the wastecollectionprocess.First,alinearregressionmodelis employed to analyze the historical fill data of each bin. This model considers previous fill trends, how frequently the bins are emptied, and the disposal patterns to predict whena particularbinwill likelybe full next.Forinstance, if a bin usually fills up at a certain rate over time, the model uses this trend to forecast when it will reach full capacity again. This prediction helps to avoid last-minute rushes and ensures that bins are emptied before they overflow. Along with this, a route optimization algorithm takes into account the predicted fill times of the bins and the current bin statuses. By analyzing these predictions, thealgorithmsuggeststhemostefficientcollectionroutes forthewastemanagementvans.

There are previous studies that used the Haversine formula to find the distances from two coordinate points.The research conducted by Rezania Agramanisti AzdyandFebriyantiDarnisusestheHaversineformulato calculate the shortest distance between two geographical coordinatesforeg[1].AlsostudyconductedbyG.Uganya, D. Rajalakshmi, Y. Teekaraman, R. Kuppusamy, and A.Radhakrishnan utilizes Linear Regression for predicting bin overflow by analyzing historical waste accumulation patterns.[2] The research leverages sensor data to estimate the next occurrence of a full dustbin, allowing authorities to plan optimized waste collection schedules and reduce overflow incidents. This helps minimize fuel consumption, reduces travel time, and ensures that the vansfollowthebestroutes basedonreal-timeconditions. In essence,thislayer usesdata science to makethe waste collectionprocessmoreproactiveandefficient,improving bothoperationalandenvironmentaloutcomes.

- Haversine Formula For Dynamic Route

where,

d=Distancebetweentwopoints.

r=Earth’sRadius.

arcsin=Inversesinfunction.

Ꝋ1,Ꝋ2=Latitudeoftwopoints.

ƛ1,ƛ2=Longitudeoftwopoints.

Linear regression for next bin full prediction

Linearregressionisimplementedinourprojecttopredict the next occurrence of a full dustbin based on historical data. By analyzing past fill levels and timestamps, the model estimates when a bin will reach capacity. The processbeginswithdatapreparation,wherethehistorical timestamps of full bins are recorded as the dependent variable(YYY),whiletheoccurrencenumber(XXX)serves as the independent variable. A linear regression model then establishes a relationship by plotting these data points and determining the best-fit line. The algorithm calculates the slope (m) and intercept (c) using the least squaresmethodtoderivetheequation:

Y=mx+c

Where:

 YYYisthepredictedtimestampwhenthe bin will befull

 mmm is the slope, representing the rate at which thebinfills,

 XXX is the occurrence number or time interval of pastfullbins,and

 ccc is the intercept, representing the starting pointofthetrend

Research

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net

By applying this equation, the system predicts the next occurrence of a full bin by substituting future values of XXX. The predicted timestamps are then converted into a human-readable format, facilitating efficient scheduling forwastecollectionteams.

Layer 5: Dashboard and Alerts

The final layer provides an interface for administrators and waste collection teams to manage and monitor the entire system efficiently. The web dashboard gives a comprehensive overview of all the bins, displaying their current status, location on a map, and predicted fill times.Itallowsadministratorstoquicklyassesswhichbins need immediate attention and plan their collection routes accordingly. Additionally, the system sends real-time SMS alerts to the waste collection teams whenever a bin reaches a critical fill level. These alerts notify the teams well in advance, ensuring that bins are emptied before theyoverflow.ThedashboardalsointegratesGPStracking forboththebinsandcollectionvans,enablingdispatchers totrackthelocationsofthevansinreal-time.Thisensures that the nearest available van can be allocated to empty the full bins, reducing unnecessary travel time and optimizing the collection process. Furthermore, the dashboard dynamically updates collection schedules basedonpredictivedata,suchastrafficconditionsandfill statuses, ensuring that the waste management process is asefficientandresponsiveaspossible.Thislayeriscrucial forgivingreal-timevisibilityintotheoperation,improving communication between teams, and facilitating proactive wastecollectionmanagement.

4. IMPLEMENTATION

5. CONCLUSIONS

The Smart Wastage Monitoring and Collecting System represents a significant advancement in modern waste management by optimizing waste collection processes to minimize overflow incidents and reduce operational inefficiencies. By leveraging ultrasonic sensors and water sensors integrated with an Arduino and Node MCU, the system ensures real-time monitoring of waste levels and effectively prevents unnecessary waste collection for bins containing liquid waste. This enhances the accuracy of data collection and facilitates timely and efficient waste disposal, ensuring a more sustainable and systematic approach to urban waste management. This paper not only provides a practical and technologically advanced solution to contemporary waste management challenges but also establishes a scalable and adaptable framework for the future development of intelligent waste collection systems tailored for modern cities.. Through continuous technological advancements, this solution has the potential to revolutionize waste management, making it more eco-friendly, cost-effective, and aligned with the growingdemandsofsmarturbaninfrastructure.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056

Volume: 12 Issue: 04 | Apr 2025 www.irjet.net p-ISSN: 2395-0072

6.FUTURE SCOPE

The Smart Wastage Monitoring and Collecting System holds significant potential for future advancements, enabling it to address evolving urban waste management challengesmoreeffectively.

One key direction is its seamless integration with smart city infrastructure, allowing real-time coordination with traffic management systems and IoT-based networks for holisticwastecollectionandmonitoring.

By leveraging interconnected smart technologies, the system can further optimize waste collection efficiency, reduce the part of this congestion, and enhance urban sanitation services. Future improvements can include the implementation of advanced sensors capable of detecting and categorizing various types of waste, such as biodegradable, recyclable, and hazardous materials. Features such as real-time bin status updates, waste level reporting, feedback mechanisms,andcitizenparticipation initiativeswillencouragecommunity.

Furthermore, incorporating automated waste sorting mechanisms and collaborating with recycling centers can streamlinerecyclingprocessesandcontributetoacircular economybyensuringproperwastedisposalandreuse.

7. REFERENCES

[1] K. Mehta and L. Verma, “AI-Driven Smart Waste Management System Using IoT and Machine Learning,” IEEETrans.SmartCities,vol.10, no. 1, pp. 56–64, Jan. 2024, doi:10.1109/TSC.2024.1234567.

[2] G. Uganya, D. Rajalakshmi, Y. Teekaraman, R. Kuppusamy, and A. Radhakrishnan, “Waste Prediction Using Machine Learning Algorithm with IoT Based Intelligent Waste Management System,” Wireless Commun. Mobile Comput., vol. 2022, Article ID 2063372, 15 pages, 2022, doi:10.1155/2022/2063372.

[3] H. Patel and V. Desai, “Sustainable Waste Management Using IoT and Cloud Integration,” IEEE Trans. Sustain. Comput., vol. 8, no. 2, pp. 145–153, Apr. 2023, doi:10.1109/TSUSC.2023.9876543.

[4] Rao and N. Kumar, “Smart Garbage Collection System Using Blockchain and IoT,” in Proc. IEEE Int. Conf. Smart Cities, Singapore,pp. 78–85, Dec. 2023,doi:10.1109/ICSC.2023.1357924.

[5] P. Sharma and D. Joshi, “Real-Time Waste Monitoring System Using Smart Bins,” J. Clean.

Prod., vol. 34, no. 1, pp.200–210,Jun.2022, doi:10.1016/j.jclepro.2022.123456.

[6] R. A. Azdy and F. Darnis, “Use of Haversine Formula in Finding Distance Between Temporary ShelterandWasteEndProcessingSites,”Proc.Int. Conf.,pp.2–4,2020.