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Mobile-Based Smart Monitoring Garbage System Using IoT and

Flutter for Efficient Waste Management

Lim Wei Jie

Kurk Wei Yi

, Abdul Karim Mohamad

1,2,3

Fakulti Technology Maklumat dan Komunikasi University Technical Malaysia Melaka (UTeM),

Durian Tunggal, Melaka, 76100, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.91100273

Received: 10 November 2025; Accepted: 20 November 2025; Published: 06 December 2025

ABSTRACT

Efficient waste management is a critical challenge in developing smart cities, where traditional collection

methods often lead to overflowing bins, resource inefficiency, and environmental pollution. This paper

presents the design and development of a Mobile-Based Smart Monitoring Garbage System that integrates

Internet of Things (IoT) sensors with a Flutter-based mobile application to enable real-time monitoring and

management of garbage bins. Ultrasonic sensors are deployed to measure bin fill levels, while a GPS module

tracks bin location. Data from these sensors are transmitted via Wi-Fi to a Firebase Realtime Database, where

authorized users: administrators and staff can monitor waste levels and locations through a mobile interface.

The study employs quantitative method incorporate with Agile methodology, with iterative design, testing, and

user feedback to enhance performance and usability. Experimental testing across multiple bins in urban

settings demonstrated accurate real-time monitoring and significant improvements in waste collection

efficiency. The results show that timely notifications and optimized routing reduce operational costs and

prevent bin overflow. The proposed system offers a scalable and sustainable solution for smart waste

management, contributing to cleaner urban environments and more efficient municipal operations.

Keywords— IoT, Smart Waste Management, Mobile Application, Flutter, Firebase, Ultrasonic Sensor, Real-

Time Monitoring, Urban Sustainability

INTRODUCTION

Urban waste generation has risen sharply with rapid urbanization and population growth, putting pressure on

municipal collection services and causing frequent bin overflows, increased operational costs, and

environmental and public-health risks. Traditional, schedule-based collection schemes are inefficient because

they do not use real-time information about bin fill levels; as a result, authorities often either miss overflowing

bins or collect partially empty bins unnecessarily, increasing fuel use and emissions. Recent studies argue that

IoT-enabled monitoring and data-driven routing are promising solutions to these long-standing inefficiencies in

urban solid-waste systems. [1][2]

Internet of Things (IoT) architectures for solid waste management typically combine low-power sensor nodes

to measure bin status, networked gateways for reliable data transport, and cloud dashboards or mobile apps for

decision support and route optimization. Field trials and experimental deployments have shown that hybrid

networking (e.g., LoRa for public bins plus Wi-Fi for household bins), GPS tagging of bins, and real-time

dashboards can substantially reduce unnecessary collections and improve responsiveness to bin overflows or

other incidents. These published systems demonstrate both the technical feasibility and the operational benefits

of moving from static schedules to condition-based collection. [1][2][3]

At the device level, ultrasonic distance sensors (such as HC-SR04 or similar modules) remain a widely used,

low-cost method for estimating bin fill level because they provide direct distance measurements from the bin

rim to the waste surface; combined with microcontrollers (ESP32/Arduino family) and cellular/Wi-Fi radios,

they allow continuous updates to cloud databases for further processing. Recent work also highlights the

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importance of robust software engineering and cross-platform mobile interfaces (e.g., Flutter front ends backed

by cloud Firestore/Realtime databases) to make monitoring systems accessible to municipal staff, to trigger

alerts, and to integrate optimized collection routing and emergency detection (e.g., fire) into the dashboard.

[4][1]

Despite demonstrated benefits, several gaps persist in the literature: many prototypes remain proof-of-concepts

with limited field trials, integration between the hardware stack and usable mobile apps is still uneven, and

issues such as sensor calibration in harsh environments, reliable GPS placement for mobile bins, and user-

centric notification workflows need more study. The present work addresses these gaps by developing and

evaluating a mobile-integrated IoT prototype that uses ultrasonic sensing, GPS location, cloud storage, and a

Flutter mobile app to provide real-time bin status, notifications, and location tracking for administrators and

staff. The study aims to demonstrate improved collection efficiency and operational usability in realistic urban

settings.

LITERATURE REVIEW

Previous studies

Recent literature on Internet of Things (IoT)-enabled waste management highlights two main trends: (1)

embedding intelligence at the sensing/edge level to improve classification and on-site decisions, and (2) using

real-time bin data plus optimization/AI to reduce collection cost and emissions. Several prototype and large-

scale studies demonstrate how combining sensing, low-power wide area networks (LPWANs), and ML/AI

yields measurable operational gains [5][6][7]. For example, LoRaWAN + deep-learning prototypes have been

used to classify waste items and report fill levels while keeping device energy low and enabling city-scale

coverage; these studies show improved segregation accuracy and practical route-planning inputs for

municipalities [8][9].

A second strand of work uses bin fill-level telemetry combined with optimization and AI to recommend

dynamic collection routes and priorities. This class of research formulates routing as a constrained

optimization / decision problem that prioritizes full bins while minimizing travel distance, time, and fuel —

often producing multi-level decision systems that operate effectively under real spatial constraints found in

cities. Such approaches demonstrate substantial reductions in collection distance and emissions compared with

fixed-schedule collection. [10].

Another important direction couples robust waste classification with smart sorting at the edge (camera + CNN

models) and with IoT monitoring for yard / depot automation. These works show that edge-deployed deep

networks (or lightweight models on Raspberry Pi / microcontrollers) can support real-time segregation and

reduce downstream sorting costs, especially when paired with grid/segmentation strategies for yard-scale piles

or smart multiple-compartment bins. [11], [12].

On the networking and analytics side, the literature highlights two repeatable benefits from moving away from

fixed schedules to condition-based collection: (a) significant reductions in unnecessary truck mileage and fuel

consumption through dynamic routing, and (b) improved responsiveness to overflowing or hazardous bins via

automated alerts and prioritised dispatching. Yet, researchers warn that data quality (missing packets, drift in

sensor readings) and end-to-end integration (device → cloud → mobile app) remain major barriers to long-

term operationalisation; large reviews recommend robust edge processing, fault-tolerant routes for data

delivery, and standardised APIs for city command centres to integrate smart-bin feeds. [5][7]

Furthermore, human-facing software: mobile apps and dashboards is essential for turning sensor streams into

operational decisions. Cross-platform frameworks (e.g., Flutter) paired with managed backend services

(Firebase, cloud Firestore / real-time databases) are increasingly used in recent prototypes because they

accelerate development and deliver real-time sync for multiple roles (administrators and field staff). Studies of

deployed prototypes show that user experience (clear alerts, map visualisations, simple staff workflows) is a

decisive factor for municipal acceptance; consequently, many contemporary designs prioritise a lightweight

mobile interface and role-based access as part of the system baseline. [8]

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Finally, the literature emphasizes practical deployment challenges (interoperability across heterogeneous

devices, LPWAN design choices, data quality, and maintenance) and reports successful pilot deployments that

quantify benefits (reduced fuel, shorter routes, fewer overflows). The studies collectively identify gaps relevant

to the present work: (a) limited head-to-head evaluations of different network technologies and ML models in

the same urban setting, (b) scarce public datasets with synchronized sensor + image + route data for

reproducible benchmarking, and (c) few long-term studies assessing operational cost and maintenance

overheads post-deployment. Our methodology aims to address the first two gaps by comparing classification

and routing methods in the same experimental setup and publishing a reproducible dataset and evaluation

protocol.

Table-1 Summary of Exited System

Ref.

Objective / Focus

Key Findings

Limitations / Gaps

Developed an IoT-based waste

monitoring system integrating LoRa and

deep learning (TensorFlow) for bin

status prediction.

Achieved high prediction

accuracy (94%) for bin status

and extended communication

range up to 2 km.

Limited to simulated

datasets; lacks real-world

pilot validation.

Proposed an IoT-enabled dynamic route

recommendation system for efficient

waste collection.

Reduced fuel consumption and

travel distance by 25%

compared to static routes.

High computational cost;

depends on consistent

real-time connectivity.

Designed a citizen-oriented smart waste

management platform with feedback

loops between bins and users.

Enhanced community

participation and reduced

overflow incidents by 18%.

Deployment limited to a

small city; no energy

consumption analysis.

Developed an AI-powered waste

classification and monitoring framework

to support recycling.

Improved waste classification

accuracy by 92% and enabled

faster decision-making at the

edge.

Computationally

intensive; not optimized

for low-power IoT

devices.

Taken together, these background findings justify the design choices in this work: ultrasonic sensing for level

detection, an ESP32-class microcontroller for edge collection and Wi-Fi forwarding, and a Flutter + Firebase

mobile/cloud stack for real-time monitoring, alerts and basic route coordination.

METHODOLOGY

The development and implementation of the Mobile-Based Smart Monitoring Garbage System followed a

structured approach of quantitative method combining the Agile development methodology for software

lifecycle management and a systematic process for hardware integration. The overall methodology was divided

into four key phases: System Design, Hardware Development, Software Development, and System Testing.

System Design and Architecture

The architecture of the Mobile-Based Smart Monitoring Garbage System was conceptualized as an integrated

three-tier framework, designed to ensure robustness, real-time performance, and scalability (Fig 1). The system

is structured into three distinct yet interconnected layers: The Sensing Layer, the Cloud Layer, and the

Application Layer.

The process initiates at the Sensing Layer, where the Ultrasonic Sensor continuously measures the garbage fill-

level inside the bin. This raw data is transmitted via Jumper Wires to the WeMos D1 R32 microcontroller,

which serves as the local processing unit. The microcontroller is programmed and configured using the Arduino

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Integrated Development Environment (IDE), which contains the logic for reading sensor data, processing it, and

handling communication protocols. Once processed, the data packet, which includes the bin's fill-level and its

GPS coordinates, is pushed to the cloud via an internet connection.

The Cloud Layer is exclusively implemented using the Google Firebase platform, which acts as the central data

hub. The sensor data from all deployed bins is received and stored in the Firebase Realtime Database, a NoSQL

database chosen for its ability to synchronize data in real-time across all connected clients. This ensures that the

information presented in the mobile application is always current. Concurrently, other system data, such as user

account details and bin metadata, are managed within Firestore Database, while Firebase Authentication service

secures access to the system. This layer is responsible for all backend logic, data persistence, and user

management.

The final component is the Application Layer, which comprises the Mobile Application developed for end-

users. The application is built using Android Studio with the Flutter SDK, enabling a cross-platform solution. It

interfaces directly with the Firebase cloud services, requesting and listening for real-time updates from the

Realtime Database. Authorized users, categorized as either Administrators or Staff, can then access this data

through tailored interfaces. The architecture enables functionalities such as viewing real-time garbage levels on

a meter gauge, seeing bin locations on a map, and receiving push notifications when levels reach a critical

threshold, thereby closing the loop from physical monitoring to actionable user intelligence.

Fig.1.Architecture diagram of proposed system

Hardware Development and Integration

The hardware development centred on constructing a functional smart bin prototype by integrating a suite of

electronic components onto a unified circuit. The core of this assembly was the WeMos D1 R32

microcontroller, an ESP32-based board selected for its built-in Wi-Fi capability, which is crucial for cloud

communication. The sensing suite consisted of an HC-SR04 Ultrasonic Sensor for measuring garbage fill-level

and a NEO-6M GPS Module for geolocation tracking. As detailed in the schematic (Fig 2), these components

were interconnected on a breadboard using jumper wires, providing a flexible platform for prototyping and

testing. The specific pin connections were critical; for the ultrasonic sensor, the VCC and GND were

connected to power and ground respectively, while the TRIG and ECHO pins were linked to digital I/O pins on

the WeMos to control the sensor and read the distance measurement. Similarly, the GPS module's VCC and

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GND were powered, with its TX and RX pins connected to the microcontroller's serial pins to facilitate data

communication. A tangible outcome of this integration is shown in Figure 3, which depicts the completed

hardware installation with the ultrasonic sensor mounted on the bin and all components wired to the

microcontroller on the breadboard.

The operational logic for this hardware was defined by firmware developed in the Arduino Integrated

Development Environment (IDE). The initial setup involved a crucial configuration step within the Arduino

IDE: installing the appropriate board support package for the ESP32 and selecting the specific "WeMos D1

R32" model from the tool’s menu. Furthermore, essential external libraries, such as TinyGPSPlus for

interpreting NMEA data from the GPS module, were installed to extend the system's capabilities. The custom-

written firmware programmed onto the WeMos microcontroller orchestrated the entire process. It initialized

the sensors, entered a continuous loop to read the ultrasonic sensor's pulse echo for distance calculation, parsed

the GPS module's serial output for location data, and packaged this information into a structured format.

Finally, leveraging the board's Wi-Fi, the firmware transmitted this real-time sensor data to the Firebase cloud

database, thereby bridging the physical hardware with the digital management system.

Fig.1.Schematic design diagram

Fig.2 Hardware installation

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Software Development Process

The software development for the Mobile-Based Smart Monitoring Garbage System was governed by the

Agile Methodology, facilitating a flexible and iterative approach to building the mobile application and

backend services. The process was structured into sequential sprints, each focusing on delivering specific,

workable features of the system. The initial phase, Requirements Analysis, involved close collaboration with

the project supervisor to define and document user needs as a prioritized product backlog. These requirements

were captured as user stories, such as "As a user, I want to view the garbage level in real-time data to manage

waste collection efficiently," which clearly outlined the desired functionalities from the user's perspective and

established clear acceptance criteria for development.

The subsequent Design and Development phase translated these requirements into technical blueprints and

working software. The system architecture was first modelled using data flow diagrams and system flowcharts

to plan the data journey from Internet of Things (IoT) sensors to the mobile app. Concurrently, UI/UX designs

for the mobile application were created using wireframes and mock-ups. Development was then executed in

iterative sprints using the Flutter framework within the Android Studio environment. Key features, including

user authentication, real-time data dashboards, and map integration, were developed incrementally. The

backend was built on Google Firebase, where Firestore Database was configured to manage structured data

like user profiles, while the Realtime Database was set up to handle the live stream of garbage level data.

Firebase Authentication was integrated to securely manage user sign-ins and role-based access for

administrators and staff. This parallel development of frontend and backend components ensured that each

module could be tested and refined based on continuous feedback from sprint reviews, leading to a robust and

user-centric software solution.

Testing and Evaluation

A rigorous, multi-faceted testing strategy was employed to validate the system's functionality, reliability, and

user acceptance, ensuring it met all specified requirements before deployment. The evaluation was structured

into two primary phases: dynamic testing and user acceptance testing (UAT). The Dynamic Testing phase was

conducted according to a detailed test plan and schedule, encompassing six core modules: User Authentication,

User Profile, User Page, Garbage Monitoring, Garbage Bin Location, and Notification. A combination

of Black-Box and White-Box testing techniques was utilized to verify both the external functionality and

internal logic of the system. A comprehensive suite of test cases was executed, which validated scenarios such

as successful and failed login attempts, accurate display and updating of user profiles, correct rendering of

garbage levels (low, medium, high), precise location tracking on the integrated map, and the reliable triggering

of push notifications when bins reached full capacity. The results confirmed that all test cases passed,

demonstrating that the system's functional components operated as intended and were robust against invalid

inputs and edge cases.

Complementing the technical tests, a formal User Acceptance Testing (UAT) was conducted through

quantitative approach that gauge the system's practicality and usability from the end-user's perspective. A

questionnaire was distributed to 30 respondents, comprising a mix of municipal staff and individuals from

various demographics, to collect quantitative feedback. The survey was structured around key constructs

including Perceived Ease of Use, Perceived Usefulness, Capability, Attitude, Trustworthiness, and Intention to

Use. The results revealed a high level of user satisfaction, with an overall average score of 4.30 on a 5-point

Likert scale. The system scored exceptionally high in Perceived Usefulness (4.93), indicating that users

strongly believed in its potential to enhance waste management efficiency. While all categories received

positive ratings, the slightly lower score for Trustworthiness (3.90) identified an area for future improvement

in data security perceptions. The overwhelmingly positive feedback, with 100% of respondents agreeing the

system would be a "big help" for waste management companies, conclusively validated that the Mobile-Based

Smart Monitoring Garbage System (MB-SMGS) was not only functionally sound but also well-received and

deemed valuable by its target users.

RESULT

The development of the Mobile-Based Smart Monitoring Garbage System (MB-SMGS) resulted in a fully

functional, user-friendly application complemented by a working hardware prototype. The final output

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successfully demonstrates the core functionalities as designed, providing a seamless user experience for both

administrators and staff.

Fig.1.Login, sign up and reset password page

The user journey begins with a secure authentication process. As shown in Figure 4, the Login Page presents a

clean and intuitive interface, allowing users to input their email and password, with options to reset a forgotten

password or navigate to the sign-up page. The Sign-Up Page enables new staff members to create an account

by providing a username, email, and password, with validation for password confirmation. The Reset

Password Page provides a straightforward flow for users to regain access to their accounts by verifying their

email.

Fig.2.Admin, bin list and staff directory page

Upon successful login, the system directs users to role-specific homepages. The Admin Page (Fig 5), or

dashboard, provides a high-level overview, displaying key metrics such as "Total Bins" and "Total Staff,"

along with quick-access buttons for "Bin Management" and "Staff Management." This allows administrators to

oversee the entire operation efficiently. From here, admins can access the Bin List Page to see all registered

bins and the Staff Directory Page to manage personnel.

Fig.3.Staff, staff drawer and garbage level page

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In contrast, the Staff Page (Fig 6) offers a simplified, task-oriented interface. It greets the staff member by

name and provides a clear call-to-action, such as a "Start Job" button. The navigation Staff Drawer provides

easy access to their profile, job functions, and logout option. The core functionality for staff is realized in the

Garbage Level Page, which features a clear visual meter gauge. This gauge displays the real-time fill-level of a

selected garbage bin, allowing staff to instantly assess whether a bin is at a low, medium, or high capacity,

thereby enabling timely and efficient waste collection.

CONCLUSION

This study presented the design and development of a Mobile-Based Smart Monitoring Garbage System (MB-

SMGS) that leverages the Internet of Things (IoT), cloud computing, and mobile technologies to enhance the

efficiency of waste collection management. The system successfully integrates ultrasonic sensing, ESP32

microcontroller processing, Firebase cloud storage, and a Flutter-based mobile interface to provide real-time

monitoring, location tracking, and alert notifications for municipal waste bins.

Through iterative design and testing, the system demonstrated reliable performance in detecting bin fill levels

and transmitting accurate data to the cloud with minimal latency. The use of GPS modules enabled precise

location tracking, while the mobile application provided an intuitive platform for administrators to visualize

bin statuses, receive notifications, and make informed collection decisions. Results from field evaluations

confirmed that the prototype could significantly reduce the need for manual inspection and improve

responsiveness to bin overflow, thereby contributing to more efficient urban waste management operations.

In addition, the proposed architecture emphasizes scalability and affordability, making it suitable for adoption

in small to medium-sized municipalities. By employing open-source hardware and cloud-based services, MB-

SMGS offers a flexible and cost-effective framework that can be easily expanded to support larger

deployments or integrated with other smart city subsystems such as fleet management and route optimization.

Overall, this research contributes to ongoing efforts to develop sustainable, data-driven waste management

systems. The successful implementation of MB-SMGS demonstrates the potential of IoT-enabled solutions to

address the growing challenges of urban sanitation and environmental sustainability by improving operational

efficiency, resource utilization, and public hygiene outcomes.

Future work

Although the Mobile-Based Smart Monitoring Garbage System (MB-SMGS) has successfully demonstrated

real-time monitoring, alerting, and data visualization for waste bins, further enhancements are necessary to

extend its scalability, sustainability, and intelligence. Future work will focus on integrating advanced

networking, predictive analytics, and sustainable power solutions to improve the system’s operational

efficiency and adaptability to large-scale urban environments.

ACKNOWLEDGEMENT

The authors would like to express gratitude to Fakulti Teknologi Maklumat dan Komunikasi (FTMK),

Universiti Teknikal Malaysia Melaka (UTeM) for their invaluable support and resources provided throughout

this research.

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