AI‑Enabled Smart IoT Waste Bin Monitoring and Dynamic

Collection System for Sustainable Urban Sanitation

Shikha Uniyal Gairola

, K. Ruth Isabels

, V. Priyadharsini

, P. John Britto

V. Divya

and Indhuja M.

School of Basic and Applied Science, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India

Department of Mathematics, Saveetha Engineering College (Autonomous), Thandalam, Chennai 602 105, Tamil Nadu,

India

Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, kinathukadavu,

Coimbatore, Tamil Nadu, India

Department of Electrical and Electronics Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil

Nadu, India

Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad‑500043, Telangana, India

Department of ECE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

Keywords: Smart Waste Management, IoT Monitoring, AI Optimization, Dynamic Routing, Sustainable Cities.

Abstract: Effective waste treatment is an important challenge of all of sustainable urban areas. In this paper, we propose

a smart IoT based AI system that is able to monitor a real-time status of a garbage bin and also optimize waste

collection automatically. This system is built based on cost-effective sensor nodes, edge-cloud connectivity,

and predictive analytics for real-time monitoring of bin fill-level and environmental conditions. A machine

learning routing engine allows for real-time optimization of collections based on traffic load, bin fullness, and

seasonal variances in waste generation to drive operational efficiency and cost savings. It also utilizes smart

waste type detection, making it easier to segregate, and provides an intelligent interface for both local bodies

and the citizens. Field validation shows the high accuracy of bin monitoring, as well as low fuel consuming

and resource effective, thus scalability of the solution is proven for smart city environment.

1 INTRODUCTION

Exploding urban population and rapid

industrialization have badly burdened the existing

waste disposal systems, resulting in overflowing

garbage cans, unorganized pick-up routes, and

pollution of precious environment. Classic waste

disposal systems generally work at predetermined

times and could not be adjusted in real-time according

to effective waste generation. Solutions offered by

smart technologies which are empowered by internet

of things (IoT) and artificial intelligence (AI) in this

direction are immensely transformative in updating

urban sanitation infrastructure in face of these

challenges. IoT devices provide critical data on trash

can contents and on environment conditions which,

thanks to their estimation, can be monitored

continuously to design dynamic and efficient data-

driven waste collection solutions. AI enables

predictive decision-making that adapts collection

routes and schedules according to live data and

historical patterns. This work presents a smart IoT

enabled waste bin monitoring and collection system

that fulfills such requirements through the use of

sensors, cloud-edge analytics and machine learning

algorithms. Not only does the system serve to boost

the efficiency of waste collection, but it also promotes

sustainability by curtailing emissions, which limits

expenses and encourages positive waste separation

habits.

2 PROBLEM STATEMENT

Despite significant advancements in urban

infrastructure, waste management systems in many

cities continue to rely on static collection schedules

and manual monitoring processes, resulting in

436

Gairola, S. U., Isabels, K. R., Priyadharsini, V., Britto, P. J., Divya, V. and M., I.

AI-Enabled Smart IoT Waste Bin Monitoring and Dynamic Collection System for Sustainable Urban Sanitation.

DOI: 10.5220/0013867300004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 1, pages

436-442

ISBN: 978-989-758-777-1

frequent bin overflows, inefficient route planning,

increased fuel consumption, and public health

hazards. The absence of real-time data and predictive

analytics limits the responsiveness of municipal

services, leading to resource wastage and poor

sanitation outcomes. Existing solutions often fail to

integrate low-cost, scalable technologies that can

adapt dynamically to the ever-changing demands of

waste generation in urban environments. Therefore,

there is a critical need for a smart, AI-driven, and IoT-

enabled system that can continuously monitor bin

status, predict fill levels, optimize collection routes in

real time, and support sustainable urban cleanliness

through intelligent automation and data-driven

decision-making.

3 LITERATURE SURVEY

Recent developments in smart city projects have

focused on IoT in waste management system to

improve energy efficiency as well as to support

environmental sustainability. Raju et al. (2024)

proposed sensor-based monitoring for garbage

system, but did not provide the real-time garbage

collection route optimization limiting to scalability.

Soliman et al. (2021) proposed a bin monitoring

prototype, which however was not suitable for large-

scale operation. Shiny et al. (2023) utilized fuzzy

inference systems for waste tracking, but their

approach lacked adaptive learning features.

Chowdhury and Rahman (2022) applied machine

learning for waste classification but logistics

efficiency was not completely tapped. Kumar and

Singh (2023) introduced an IoT model with static

monitoring and did not include any mechanism for

visualization or cloud analytics.

Patel and Shah (2021) explored an IoT framework

without municipal synchronization, limiting

administrative usability. Ahmed and Khan (2024)

developed a cloud-based solution, although latency

issues hindered real-time responsiveness. Li and Zhao

(2022) utilized GIS integration for smart routing, but

its high maintenance cost restricted applicability.

Ghosh and Das (2023) deployed smart bins in

localized zones with minimal expansion to rural

areas. Mehta and Verma (2025) proposed a

segregation-first model, lacking logistics

coordination for holistic waste handling.

Singh and Kaur (2021) built a basic IoT

infrastructure but failed to incorporate intelligent

prediction for bin fill levels. Chen and Wang (2022)

addressed route logistics using AI but ignored

ground-level bin data. Reddy and Rao (2023)

presented a local storage mechanism without cloud

integration, creating data silos. Zhang and Liu (2024)

used advanced AI but required hardware unsuitable

for low-resource environments. Khan and Ali (2025)

encountered data imbalance affecting model stability,

especially with sporadic collection records.

Patel and Joshi (2021) addressed bin monitoring

but omitted waste type classification. Sharma and

Gupta (2022) faced centralized system limitations,

making it prone to single-point failure. Lee and Kim

(2023) proposed a bin-cloud model without economic

feasibility analysis. Singh and Kumar (2024)

designed an energy-consuming sensor network,

reducing overall efficiency. Wang and Li (2025)

created a robust backend with minimal focus on user

interface design.

Kumar and Sharma (2021) provided a static route

schedule with no adaptability to real-time changes.

Patel and Desai (2022) offered a backend system

without practical validation. Zhao and Chen (2023)

proposed a high-tech AI model, making it complex

for underdeveloped municipalities. Gopi et al. (2021)

reported calibration issues, affecting sensor

reliability. Lastly, Hoque et al. (2024) ignored waste

generation variability across seasons, reducing the

system’s adaptive capacity.

These literature review recommend a necessity of

intelligent, real-time, scalable and hybridized IoT-AI

solution in the context of the waste monitoring,

optimized collection and dynamic route planning

under economic and environmental consideration.

4 METHODOLOGY

The proposed approach is based on the combination

of smart sensing technologies with artificial

intelligence and real-time communication and aims at

the realisation of an autonomous and adaptive waste

management system fluent to urban area. The system

aims to manage trash in a city by monitoring trash

bin levels for numerous locations in the city,

automatically predicting how full they will become,

and continuously optimizing collection routes, to

ultimately provide on-time trash removal and

maximize resource usage.

Table 1 shows the Sensor

Specifications and Deployment Details.

AI-Enabled Smart IoT Waste Bin Monitoring and Dynamic Collection System for Sustainable Urban Sanitation

437

Table 1: Sensor specifications and deployment details.

Parameter Specification

Sensor Type Ultrasonic HC-SR04

Measurement

Range

2 cm – 400 cm

Accuracy ±0.3 cm

Communication

Protocol

MQTT over Wi-Fi

Power Source

Solar + Battery

Backup

Number of Bins

Deployed

Areas Covered

Residential,

Commercial, Public

At the heart of the solution lies smart bins with

ultrasonic sensors that monitor the waste level in the

bin on a real-time basis. Selected sensors are those

that provide high accuracy, consume low power and

at the same time can be low-cost and suitable for

large scale deployment. All sensors are integrated to

a microcontroller that collects the measures and

forwards it to a central Cloud server via wireless

technology (e.g. LoRa or Wi-Fi) and the MQTT

protocol. This architecture allows near-real-time data

push with low energy consumption, and enhances the

sustainability of the system in power-limited

scenarios.

Figure 1: Workflow of the proposed smart waste

management system.

The cloud becomes the centre of aggregation and

analysis of the data. Reads from all channels are real-

time, time tagged, cleaned up, and stored in an

organized database. Based on historical fill patterns

versus time, weather information, and local events, a

predict model (using AI), forecasts the fill level of

each bin over some time into the future. Furthermore,

the model implicitly learns the seasonal variations

and adapts its forecasts to those, improving the

accuracy of temporal prediction while making them

proactive.

Figure 1 shows the Workflow of the

Proposed Smart Waste Management System.

For the efficiency of the waste-collection, a route

optimization engine is also implemented in cloud

system. This module is based on Dijkstra’s algorithm

and reinforcement learning for the collection vehicle

optimal routes. It factors in bin fill level, location,

truck capacity, and real-time traffic flow to create

flexible collection routes. Automatic warnings are

sent to the closest collection points with instructions

regarding the most suitable, optimised route when bin

fill thresholds reach defined levels.

Table 2: AI model configuration and training summary.

Parameter Value

Model Type

LSTM with

Reinforcement

Learning

Training Data Size

6 months of bin-level

data

Forecast Horizon 24 hours

Training Accuracy 96.7%

Validation Accuracy 93.2%

timizer Use

Adam

Training Duration 3 hours (on GPU)

An easy-going Dashboard will be available for the

Municipality users as well as for the system

administrator. It includes: visual status of bins across

the entire city, alert notifications, historical collection

trends and system performance metrics. Mobile apps

also allow field workers to be notified of route

changes and bin details in real time, enabling them to

be more responsive and have less time where they are

sitting idle.

Table 2 shows the AI Model

Configuration and Training Summary.

The system is also equipped with waste type

classification by adding more gas sensors and an

image processing unit resulting in efficient separation

of dry and wet waste at source. This not only helps

recycle but it also helps in being more

environmentally friendly. Self-diagnostics and

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calibration reminders verify sensor authenticity and

reduce maintenance requirements.

On the whole, this approach integrates IoT,

machine learning and smart logistics to create a

single platform that overcomes the shortcomings of

traditional waste collection systems. The

infrastructural design is modular so it can scale in

deployment with the demands of a variety of urban

installation levels and the artificial intelligence-

enriched cognition empowers dynamic adaptation to

reality, thus minimizing human intervention and

substantially ameliorating the cleanliness/hygienicity

related aspects of urban surroundings.

5 RESULT AND DISCUSSION

The performance of the proposed AI-aided IoT-

supported waste monitoring and collection system

was proved to be very promising for several

performance indicators such as bin fill level accuracy,

route optimization effectiveness, system response,

power consumption, and user satisfaction. To

evaluate the developed systems, a prototype system

was implemented in a mid-size urban area by

deploying 50 smart …0bins in different waste

density zones consisting of …home areas, business

zones and public areas. Through a 45-day study, real-

time data was captured and analyzed in the system

comparing to the conventional static catch method.

Table 3 shows the Comparative Analysis of Static vs.

Dynamic Collection.

Table 3: Comparative analysis of static vs. dynamic

collection.

Metric

Static

Routing

Dynamic AI-

Based Routing

Avg. Daily

Distance (km)

110 76

Fuel

Consumption

(liters/day)

23.4 15.9

Missed Pickups

er Wee

6 1

Vehicle Idle

Time

(

mins/da

80 42

The first major result was the accuracy of monitoring

the fill-level in the bins. Having implemented ultrasonic

sensors in combination with an auto-correction algorithm,

the solution delivered an average detection fill-level rate of

97.6% - thus exceeding agreements made by previous

systems or procedures based on spot checks or human

assessment.

Figure 2: Performance comparison between static and AI-

optimized routes.

This kind of precision was instrumental in getting

the pick-ups just in time, thus reducing the number

of overs pilling waste bins by a great amount. What’s

more, the predictive model of the system, using Time

Series predictive model as well as seasonal predictor

model, could predict the bin full/overflow condition

up to the next 24 h with an accuracy of 93.2%. These

proactive predictions resulted in a 62% reduction in

emergency collections, since waste collection

vehicles were sent out on the basis of predictive

triggers, not reactive ones.

Figure 2 shows the

Performance Comparison Between Static and AI-

Optimized Routes.

Table 4: Bin fill level monitoring accuracy.

Area Type

Accuracy

(

)

False Alerts

(

)

Residential 97.2 1.6

Commercial 96.5 2.1

Public

aces

98.9 0.9

Overall

Avera

97.6 1.5

Figure 3: Bin fill-level accuracy across zones.

AI-Enabled Smart IoT Waste Bin Monitoring and Dynamic Collection System for Sustainable Urban Sanitation

439

Optimization announcement result was also

impressive. Get a quote. CONCLUSIONS: In

comparison to the fixed-route method, the AI-

solution resulted in an average reduction of

approximately 31% with respect to travel distance,

which lead to significant savings derived from fuel

consumption and pollutant emissions. During peak

days, the system also reconfigured the collection

paths in real-time based on demand, enabling vehicles

to bypass full routes to empty the most urgent bins.

Furthermore, vehicle idle time was reduced by 48%,

bringing about less wear-and-tear and a smaller

carbon footprint in operation.

On the responsiveness front, the system reacted

to bin level changes and sent alerts with an average

delay of only 3.8 seconds! This immediate reactivity

means that high-priority bins (either those filling up

or located in sensitive places such as hospitals and

schools) would be quickly attended to. Edge

computing modules deployed in the smart bins had

reduced the response time by making the data

analytics and filtering at local level before forwarding

it to cloud.

Table 4 shows the Bin Fill Level

Monitoring Accuracy.

Figure 3 shows the Bin Fill-

Level Accuracy Across Zones.

Table 5: User satisfaction and feedback summary.

User Group

Satisfaction

Level

(

)

Key

Feedbac

Municipal Workers 85.3

Better

routing, time

savin

Citizens 78.1

Cleaner

streets, fewer

overflows

Admin Interface

Users

82.4

Intuitive

dashboard,

real-time

alerts

Energy efficiency of the IoT modules was also

analyzed. Each smart bin unit, powered by solar-

charged batteries, sustained operations continuously

without manual intervention throughout the 45-day

testing period. The average energy consumption per

bin was under 0.1 kWh per day, affirming the

system’s suitability for sustainable deployment in

resource-constrained environments. Maintenance

logs showed a sensor failure rate of only 2%, and

those were resolved automatically through the

system’s built-in calibration checks.

Table 5 shows

the User Satisfaction and Feedback Summary.

Figure 4: Predicted vs actual fill levels over time.

User engagement and satisfaction were gauged

through municipal staff surveys and citizen feedback

via a mobile app interface. Over 85% of municipal

workers found the dashboard and route notifications

intuitive and helpful, citing reduced workload and

better control over operations. Citizens reported a

78% improvement in local cleanliness perception,

with a significant decline in visible garbage

accumulation and odor issues. The option to report

missed pickups or overflow events directly through

the mobile app empowered community involvement

and accountability.

Figure 4 shows the Predicted vs

Actual Fill Levels Over Time.

Regarding garbage sorting, the combination of

gas sensor with the simple image recognition system

can classify whether the waste is Dry or Wet

persistently in 87% for tested case. It is not perfect

yet, but this feature seems to have high potential on

automating source-level waste separation, which is

essential for recycling and composting. Further

improvements in accuracy with more training and

sensor optimization can be expected for future

generations.

Figure 5: Accuracy of waste type classification.

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They also tested scalability of the system by

simulating high levels of traffic. Even when the

number of bins was increased almost 100%, virtually

no latency spikes or slow-downs of the route

optimization routine was observed on the cloud

server. This also shows the ability of the modular

architecture that scale(out)horizontally without

performance loss which are feasible for large

metropolises.

Figure 5 shows the Accuracy of Waste

Type Classification

Figure 6: User satisfaction survey results.

In conclusion, the findings of this study prove that

IoT, AI, and smart logistics combination is a

significant game changer that can lift the

performance on the efficiency, responsiveness, and

sustainability of UWM systems in urban life. The

proposed system addresses the deficiencies of

previous systems - static routing, manual supervision,

and inability to adapt in real time, yielding a stable

and user-friendly scalable system. The researchers

say that if people adopted this idea, the benefits could

be less polluted cities, lower management costs for

townships waste and better community satisfaction

with the end result being a changed mindset towards

building smarter, more eco-friendly cities.

Figure 6

shows the User Satisfaction Survey Results.

6 CONCLUSIONS

This study nutate and clever way for the urban waste

management system which includes the integration of

IoT based Smart Technology with AI-based

Predictive analytical and Dynamic route

optimization. The proposed system solves some of

the setbacks of regular waste collection; namely,

inefficiency, passing time between requests and

responses, and managing the adapter to currents

conditions. Through real-time bin monitoring,

proactive scheduling and optimized routing, the

proposed approach can dramatically enhance the

general cleanliness, operational efficiency and

sustainability of municipal sanitation services. The

actual level of deployment has confirmed the

efficiency of the system in avoiding overflowing, in

saving fuel and in increasing citizen acceptance.

Additionally, its scalable low-costs nature will make

it possible to apply in different type of urban and

semi-urban settings. As cities grow and waste

generation escalates, this smart intervention provides

a futuristic playbook to keep cities clean, dump less

waste in environment and to reinforce the decision-

making process by giving the power to the city

administrators.

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