Smart Waste Management System Research Articles

IoT-Based Smart Waste Management System for Smart Cities: A Synthesized Framework Addressing Security and Interoperability Challenges

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, including lack of real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. While prior studies have investigated IoT applications in SWM, challenges remain in achieving dynamic, integrated, and scalable systems for sustainable urban development. The proposed solution introduces a holistic architecture that enables real-time monitoring of waste bin levels and fire incidents through Waste Bin Level Monitoring Units (BLMUs) equipped with ultrasonic and flame sensors. Data is transmitted via Wi-Fi to a centralized City Command and Control Center (4C), allowing for automated alerts and dynamic route optimization. A dual-platform software suite supports both administrative and operational workflows: a desktop web application and a role-based Android mobile app developed in Flutter, and integrated with Google Cloud Firestore, enabling centralized data management and efficient resource allocation. We validated the system through a working prototype, demonstrating notable contributions including enhanced emergency responsiveness, optimized waste collection routes, and improved stakeholder engagement. This research contributes to the advancement of sustainable urban infrastructure by offering a scalable, data-driven SWM framework grounded in software engineering principles and aligned with smart city objectives. This paper presents an innovative IoT-based Smart Waste Management (SWM) system that addresses key limitations in existing solutions, including insufficient real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design.

Waste management market in Asia-Pacific region: Key insights into growth, competition and value chain dynamics.

This document shows the required format and appearance of a manuscript prepared for SPIE e-journals. The abstract should consist of a single paragraph containing no more than 200 words. It should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (i.e., no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to six keywords should immediately follow, with the keywords separated by commas and ending with a period. The exponential growth in urban populations has significantly increased the amount of waste generated in cities. Traditional waste management practices are increasingly proving inadequate in addressing this rise in municipal solid waste. Overflowing bins, irregular collection schedules, and unclean streets are just a few of the challenges faced by urban administrations. This paper presents an innovative solution in the form of an Internet of Things (IoT)-based Smart Waste Management System (SWMS). The proposed system makes use of advanced technologies including ultrasonic sensors, microcontrollers, GPS modules, and cloud computing platforms to enable real-time monitoring, data collection, and efficient waste disposal planning. The integration of IoT not only ensures timely garbage collection but also optimizes resources, reduces operational costs, and contributes to a cleaner and healthier urban environment. Key Words: IoT, Smart Waste Management, Ultrasonic Sensors, Real-Time Monitoring, Cloud Computing, Urban Sanitation

Waste generation, particularly from organic and inorganic sources, has become a growing environmental issue, especially in culturally unique regions like Bali where traditional offerings contribute to organic waste volumes. Despite regulations such as Gianyar Regency Regulation No. 76 of 2023 mandating source-level separation, on-ground implementation remains inconsistent due to low public awareness and operational limitations. This study addresses the challenge by developing an automated image-based classification system using the ResNet50 deep learning architecture to distinguish between organic and inorganic waste. A total of 200 images were collected 100 per class using smartphone cameras, and the dataset was expanded to 1,400 images through geometric data augmentation techniques such as rotation, flipping, and zooming. Images were resized to 224x224 pixels and evaluated using K-Fold Cross Validation to ensure model stability. The model was trained using transfer learning and tested under two conditions with and without augmentation while optimizing hyperparameters such as learning rates (0.0001 and 0.00001) and optimizers (Adam and SGD). The results demonstrate that augmentation significantly enhanced model performance, with the augmented model achieving an average accuracy of 99.25%, precision of 99.32%, recall of 99.25%, and F1-score of 99.25%, compared to 89.88% accuracy in the non-augmented model. These findings confirm that ResNet50, when combined with geometric augmentation and proper preprocessing, offers a robust, accurate, and scalable solution for waste classification tasks. This research contributes to the advancement of AI-driven environmental technologies and offers a potential framework for smart waste management systems, with future directions including real-time deployment, multi-class classification, and expansion to more diverse and real-world datasets.

Abstract. Effective waste management is one of the major elements of urban sustainability, more so in rapidly growing cities like Dubai. This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. The system has four major components: (1) a mobile field application to add and modify collection points in real time; (2) a route optimization module that minimizes travel distance and CO₂ emissions while accounting for real-world constraints; (3) an interactive dashboard for decision-makers to monitor analytics, visualize routes, and make real-time adjustments; and (4) a navigator app for truck drivers to follow optimized routes seamlessly. Furthermore, the system includes a new adaptive waste demand management module, which dynamically updates the demand for each collection point using real-time usage data, rather than being based on static assumptions of capacity. The effectiveness of the system was tested on a sample of 110 collection bins located in three different areas in Dubai. Preliminary results indicate that route optimization alone has achieved a reduction of 19.1% in CO₂ emissions, and further improvement is expected with full implementation of the adaptive demand management module. The findings highlight the potential of intelligent systems to significantly reduce the environmental and financial costs associated with municipal waste collection, paving the way for scalable deployment in other urban environments.

Effective waste management is essential in India to ensure environmental sustainability and safeguard public health. With rapid urbanization and population growth, the volume of waste generated has surged, overwhelming traditional disposal systems and leading to pollution, ecosystem degradation, and health risks. Conventional methods such as manual sorting, rule-based classification, and exporting waste have long been used but present significant limitations. Manual sorting is laborintensive and error-prone, making it unsuitable for large-scale implementation. Rule-based systems lack flexibility to adapt to the diverse and dynamic nature of waste, while exporting waste raises ethical and environmental concerns due to the risks of mismanagement. To address these challenges, this research utilizes a large-scale dataset containing millions of waste item images to train and evaluate classification models. The study integrates MobileNetV2 and a lightweight convolutional neural network to develop an efficient, automated waste classification system. This system is designed for deployment in smart waste bins and recycling centers, aiming to improve sorting accuracy, increase recycling efficiency, and minimize environmental harm.

Identification and prioritization of barriers to smart waste management systems in the textile and apparel (T&A) industry

The challenge of effective urban sanitation and waste management is growing increasingly complex due to the rapid urbanization, rising population, and the limited resources available for traditional waste management practices. In response to this, the emergence of smart waste management systems leveraging Internet of Things (IoT) and Artificial Intelligence (AI) has gained significant traction in recent years. These innovative technologies promise to revolutionize waste management, making it more efficient, sustainable, and cost-effective. IoT-based smart waste management systems enable real-time monitoring of waste levels, optimizing collection routes, and ensuring timely disposal. Meanwhile, AI-powered algorithms facilitate predictive analysis, resource optimization, and intelligent decision-making processes, enhancing operational efficiency and reducing environmental impact. This paper aims to explore the integration of IoT and AI technologies in the context of smart waste management, focusing on the various approaches that have been implemented globally to address the challenges of urban sanitation. By reviewing the advancements in this field, the paper highlights key trends, challenges, and opportunities that lie ahead. Furthermore, it presents a comprehensive analysis of how these technologies can contribute to the achievement of sustainable urban sanitation, offering actionable insights for policymakers, researchers, and practitioners.

Urban areas in Malaysia, such as Penang, are experiencing rapid growth in waste generation, while traditional methods like landfilling and incineration remain inefficient and unsustainable. These challenges demand smarter, technology-driven solutions. Japan has successfully implemented a Smart Waste Management System (SWMS) using Internet of Things (IoT) technology to optimise collection, enhance recycling, and reduce environmental impact. However, the feasibility of adopting such systems in Malaysia remains uncertain due to differences in infrastructure, policy, and socio-economic conditions. This study aims to assess the potential for adapting Japan’s SWMS to Penang as a representative urban area in Malaysia. The research objectives are to identify key challenges, evaluate the risks involved, and propose strategies for effective implementation. A comparative case study approach was adopted, combining qualitative and quantitative methods. Surveys were distributed to Penang residents to gather public perceptions and awareness levels, while field observations focused on waste collection practices. Additionally, expert interviews were conducted with officials from Majlis Bandaraya Pulau Pinang (MBPP) to explore policy, infrastructure, and operational insights. A SWOT analysis was then applied to evaluate the strengths, weaknesses, opportunities, and threats of integrating smart waste technologies within the local context. The findings are expected to provide practical recommendations for improving Malaysia’s urban waste management through IoT-based solutions, offering a foundation for future policy and infrastructure development.

With the increasing global environmental and social challenges, it is more urgent than ever to implement effective strategies for sustainable development. Environmental, Social and Governance (ESG) criteria are also necessary requirements guiding organizations toward responsible and sustainable practices. However, the multi-dimensionality of the criteria and the uncertainty associated with judgment by an expert makes evaluation and choice of the best ESG-driven strategy a very complex task. The current paper introduces an innovative approach to the assessment of ESG strategies via Fuzzy based multi-criteria decision-making tools. Those selected shall be addressed particularly through the Fuzzy TOPSIS method, considering and ranking seven key strategies driven by ESG focus areas- AI-Powered Predictive Analytics, Renewable Energy Integration, Smart Waste Management Systems, Blockchain for Transparent Governance, AI-Enhanced Workforce and Community Development, Sustainable Supply Chain Optimization and Generative AI for Eco-Friendly Innovation. The results of this assessment indicate that the most popular ESG approach is renewable energy integration, which is in line with the industry’s pivotal role in advancing energy transition and climate action. AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization are closely related, emphasizing the strategic value of data intelligence as well as operational efficiency in improving sustainability practices. These results offer important novel insights about how AI-powered methods might guide environmentally friendly choices in intricate industrial settings. Our method provides a strong and transparent framework for assessing ESG strategies under sustainability by incorporating fuzzy logic into decision-making. The study adds to the rapidly expanding body of research on AI-driven sustainability evaluations, from which companies, policymakers and other stakeholders could gain insight how to enhance their ESG performance as well as advance sustainable development.

Rapid urbanization and population growth have intensified the challenges of waste management across the globe. Traditional waste management systems often lack the intelligence and efficiency to handle modern waste generation patterns. In response, the integration of big data analytics and machine learning (ML) technologies into waste management has emerged as a transformative approach. This paper explores how smart waste management systems utilize real-time data collection, predictive analytics, and intelligent decision-making to enhance waste collection, reduce operational costs, and support environmental sustainability. The study highlights various applications of ML, including waste sorting, route optimization, and predictive maintenance, supported by big data platforms. It further discusses the challenges, such as data privacy, interoperability, and infrastructure limitations, and offers future directions for research and implementation. By leveraging digital intelligence, smart waste management represents a vital step toward achieving cleaner, smarter, and more sustainable cities.

Rapid urbanization and population growth have significantly increased the volume of waste generated daily, putting immense pressure on traditional waste management systems. Inefficient collection processes, overflowing bins, and lack of timely monitoring contribute to environmental pollution and health hazards. To address these challenges, this paper presents a Smart Waste Management System utilizing Internet of Things (IoT) technology to automate and optimize the waste collection process. The system employs sensor-equipped garbage bins, primarily using ultrasonic sensors, to detect the fill level of each bin. These sensors send real-time data to a centralized cloud server through wireless communication modules. The collected data is analysed to determine optimal collection times and routes, thereby reducing fuel consumption, operational costs, and unnecessary trips by waste collection vehicles. Additionally, a mobile and web-based application interface is developed for both municipal staff and users to monitor bin status, receive alerts, and manage collection schedules efficiently. The system ensures timely waste disposal, prevents bin overflow, and contributes to a cleaner, healthier environment. Integration of GPS tracking and route planning enhances decision-making and resource allocation. This IoT-based approach not only streamlines the overall waste management workflow but also supports the vision of smart, sustainable cities. The proposed system demonstrates high scalability, cost- effectiveness, and the potential for real-world deployment in urban and semi-urban areas. keywords—Smart Waste Management, Internet of Things (IoT), Real-Time Monitoring, Ultrasonic Sensors, Cloud Computing, Route Optimization, Environmental Sustainability.

Urbanization and population growth have exacerbated municipal solid waste (MSW) challenges, necessitating intelligent solutions. Dhanbad, a mining-intensive city in India, faces unique waste management challenges due to coal sludge contamination, rapid urbanization, and inefficient disposal practices. A case study in Mumbai demonstrated a 35% reduction in fuel consumption and 28% faster collection times. The system architecture merges edge computing, LoRaWAN communication, and predictive analytics, while advanced recycling incorporates AI-based material classification. This study proposes an IoT-enabled smart waste management system tailored for Dhanbad Municipal Corporation (DMC), integrating real-time bin monitoring, AI-driven hazardous waste detection, and GIS-GPS route optimization. Deploying methane/CO₂ sensors and hybrid algorithms, the system reduces collection costs by 32% and improves recycling accuracy by 48%, addressing coal-dominated waste streams. Challenges include initial costs and data security, yet scalability and integration with smart city ecosystems offer transformative potential. The pilot study of 15 wards has proposed and detailed strategy have been formulated based on intensive literature and field survey. Analogues study shows 40% fewer overflow incidents and 22% lower fuel consumption. The implementation framework emphasizes phased rollout, stakeholder training, and integration with Swachh Bharat Mission 2.0 guidelines.

Public awareness of the importance of separating organic and inorganic waste and waste management is minimal, so there is a lack of public knowledge to distinguish between organic and inorganic waste. In addition, the slow transportation of garbage from the Temporary Disposal Site (TPS) to the Final Disposal Site (TPA) with a slow garbage truck can cause accumulation, so height monitoring that can be accessed remotely is needed. Based on these problems, it is necessary to have Organic and Inorganic Waste Detection tools and Internet of Things (IoT)-based altitude monitoring using infrared, capacitive, and inductive proximity sensors to distinguish between the two types of waste. This tool also monitors garbage height using ultrasonic sensors and the Ublox Neo 6mv2 GPS Module connected to the NodemCU ESP32 microcontroller to determine the longitude and latitude location point values. The altitude information will be sent to the Google Firebase server using WiFi and displayed on the App created using the MIT App Inventor.

With the advancement of Artificial Intelligence (AI), traditional waste management systems can be transformed to provide real-time monitoring, enabling more efficient and cost-effective waste management. This research aims to develop a smart waste management system utilizing a TensorFlow based deep learning model for real-time object detection and waste classification. The proposed system incorporates a waste segregation bin with compartments for materials such as metal, plastic, and paper. Object detection and classification is implemented using TensorFlow’s pre-trained models, trained with a dataset of waste images to generate a frozen inference graph. The detection is performed through a camera for accurate waste classification. Additionally, a Machine Learning-based program is developed to classify images from CCTV cameras to identify clean and unclean streets. Trained on hundreds of labelled images, the model can Analyse new inputs and classify them accordingly. When unclean streets are detected, an automatic email alert is sent to the respective authorities, ensuring timely action. This innovative approach addresses the inefficiencies of traditional waste management systems, which operate on fixed schedules, and provides a scalable solution for real-time street cleanliness monitoring. Keywords: Smart Wast Management, AI Garbage Detection, Real-time Monitoring, Garbage Alert, Waste Detection, Real-time Alerts

Used Cooking Oil (UCO) has significant potential for biodiesel production recycling; however, its recycling is limited by the absence of smart waste management systems specifically designed for UCO. This article proposes a blockchain-based smart UCO collection platform aimed at UCO collection with real-time tracking. The platform features system architecture, UCO detection and classification modules, and a fuzzy inference system (FIS)--based decision support module for estimating UCO collection potential and calculating collection thresholds for smart garbage bins (SGBs). The proposed platform integrates various technologies and tools, including blockchain, IoT sensors, smart bins, cryptocurrency micropayments, gamification elements, and machine learning. A case study from Antalya is presented to showcase the FIS-based decision support module and the computation of collection thresholds. To the best of the authors' knowledge, this platform is the most comprehensive smart recycling solution presented in the literature for UCO, enhancing public awareness, increasing interaction, and motivating recycling through financial incentives.

The fast pace of urbanization and consumption has made effective waste management a key challenge. This study presents a smart waste monitoring and management system designed to enhance waste segregation, improve operational efficiency, and promote sustainability.With the use of IoT-enabled sensors, I-based classification, and real-time monitoring, the system facilitates automated waste identification and also disposal with minimal manual intervention. The advanced sensors allow real-time evaluation of waste types, while automated systems ensure correct segregation. Intelligent monitoring elements offer round-the-clock that updates the bin status, avoids overflow and simplifies collection operations. Remote communication modules provide real-time alerts by allowing for prompt intervention and optimal resource deployment. This flexible and scalable solution is well suited to different environments, including cities, institutions, and business premises. By employing data-driven decision-making, predictive analysis, and automation, the system lends support to a more efficient and environmentally friendly waste management solution, in accordance with global sustainability endeavours

As urban populations rapidly expand, the efficient management of waste is becoming a critical challenge for cities worldwide. Integration of Information Technology Infrastructure (TI) as part of "Smart City" provides transformative solutions to optimize waste collection processes, isolation and elimination. By leveraging technologies like the Internet of Things (IoT), sensor networks, data analytics, and cloud computing, smart waste management systems can monitor fill levels in waste bins in real-time, optimize collection routes, facilitate waste segregation at source. This paper explores the potential of IT infrastructure in smart cities for waste management, delving into key technologies like smart waste bins equipped with sensors, intelligent routing algorithms for waste collection vehicles, data-driven decision-making platforms, and citizen centric applications that promote responsible waste disposal behaviours. IoT-enabled waste bins: The utilization of sensors embedded in waste bins to monitor fill levels, triggering alerts for collection when nearing capacity, thereby reducing unnecessary collection trips and optimizing truck routes.

In the current situation, we frequently observe that the trash cans or dust cans that are located in public spaces in cities are overflowing due to an increase in the amount of waste produced each day. We propose to construct a "Smart Waste Management System utilising IoT" to prevent this because it leads to unclean conditions for people and produces a terrible stench in the surrounding area, which spreads some deadly diseases & human illness. In the proposed system, there are numerous trash cans scattered throughout the city or campus. Each trash can has a low-cost embedded device that tracks the level of the trash cans, and each one will also have a unique ID that makes it simple to determine which trash can is full. The device will broadcast the level and the supplied unique ID when the level hits the threshold limit. The concerned authorities can view these facts online from their location and take fast action to clean the trash cans. Keywords: Waste Management , Solar energy , IOT technology , smart dust bin etc.