Smart Waste Management System Research Articles

Smart Waste Management System for Smart Cities

A smart waste management system represents aninnovative approach of smart bin by integrating IoT intelligence. The system incorporates sensor-based devices deployed within smart bin to monitor in real time. This bin allows efficient route planning, reducing operational costs and emissions and timely waste collection improves hygiene, reducelittering and enhances overall quality of life. Smart bin collects the data from the sensors, which is then store into the firebase, through the app municipal authorities and the public receives the notifications regarding the bin fill status, safety alerts, and can track location of the bin. It utilizes solar power for sustainable power supply and camera for live streaming. The system provides a web interface to the municipal authority so that they can monitor and clean the garbage bin. The system enhances the public engagement, and contributes to a cleaner and greener environment.

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

Analysis of the world market of waste management

This paper provides an overview of the solid waste management market, which is the object of the study, including the processes of collection, transportation, utilization and recycling, supervision and monitoring of these processes. The volume and dynamics of the global waste management market in 2023 and its forecasting until 2033 are considered. The problem solved in the article is to identify the key factors of influence and interconnection on the development of waste management market components to determine the directions of an effective sustainable development policy. The author analyzes the methods of waste management in different European countries and identifies the key factors influencing the market dynamics: the effects of urbanization, environmental regulations, waste-to-energy technologies, circular economy initiatives, smart waste management systems, increased waste recycling, and the development of green infrastructure. It is determined that the growing awareness of environmental problems and increasing public pressure are leading to an increase in government investment in the development of innovative technologies for effective waste management. It is established that the promotion of recycling infrastructure and the implementation of campaigns aimed at raising awareness among the population leads to an increase in the importance of responsible waste management, the introduction of public-private partnerships in the field of waste management and the development of green infrastructure. The market is analyzed by region, end user, and waste type. According to the regional analysis, North America has the largest market share, but Asia Pacific is showing the fastest growth. North America has a large waste management market due to its developed infrastructure and high level of environmental awareness. However, Asia-Pacific, particularly China and India, is emerging as a growth center due to rapid economic development, population growth, and rapid urbanization, indicating the potential for new waste management markets in this region. According to the analysis by waste type, industrial waste is the largest category, with the electronic segment showing dynamic growth. This analysis allows to forecast the market and trends in the waste management industry, which makes it possible to calculate costs and profits, scale global trends to Ukrainian realities, and offer promising investment opportunities, which is especially important in the times of recovery of Ukraine.

Smart-Waste Management System Based On Arduino and Motion Sensors

Abstract: Smart-Waste Management System involves the collection of the waste generated in separate dustbins that are equipped with Arduino based automated system. The segregation of the waste will be done by the IoT based system; the sensor will detect and segregate the waste into recyclable and non-recyclable waste. As with the increment in population the amount of garbage generation per year is increasing as well. Most of the garbage generated is left untreated and the garbage dumping places and garbage dumpsters are becoming more unhygienic and polluted. After the COVID-19 pandemic many people prefer contact less interaction. Automated-dustbins are the best ways to deal with this situation. The dustbins will be controlled by Arduino components allowing it to open when waste is detected; it will also notify the garbage collector when the dustbin capacity is full and the waste needs to be collected. So thus, the garbage collector need not to go collect the garbage unnecessarily. Secondly, after the waste is collected the wet garbage is being dumped and buried underground whereas the dry garbage needs to be segregated on the basis of recyclable and non-recyclable waste. The waste segregation is based on the sensor and system based on IoT that allows to segregate the waste into categories like wet, metallic, paper-plastics, etc. These will lead to save of fuel and time, at the same time automated system will lead to contactless collection of waste and segregation of waste will help to treat waste properly, classify them into categories and proceed them for further processes. This way the amount of unprocessed and untreated waste will get reduced to some extent; resulting in less pollution

Digital Technologies and Environmental Impact

Information and communication technologies (ICTs) are a collection of devices and services that acquire, transfer, and display data and information digitally. Information and communication technology (ICT) is developing rapidly and is playing an increasingly important role in society. The role of ICT in environment management gains more importance due to its extensive use in various arenas of environment like weather forecasting, systems in the climate change, forest Degradation, hurricanes and typhoons anticipations, smart waste management systems, energy supply efficiency and tourism industry. The impacts of ICT can be seen both positive as well as negative. Thus, ICT plays a crucial role in environmental economics. ICT needs to be use in most effective ways thereby reducing the operational challenges that are directly responsible for production of significant amount of electronic waste generated at the end of the useful lifecycle of an ICT gadget and Green House Gases (GHGs). The collaboration of ICT with environment must focus on reducing energy consumption and carbon emissions from the design, use and disposal of technology products.

Smart Waste Management System Using IoT

Waste collection and management are integrated parts of both city and village life. Lack of an optimized and efficient waste collection system vastly affects public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. The Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate, and self-sufficient. Thus, this paper proposes an IoT-based, efficient waste collection system with smart bins. India generates tons of waste annually. Conventional garbage collection is not efficient since the authorities are not notified until the waste bin is full, and this leads to an overflow of waste material. Efficient waste disposal and collection are essential for a sustainable and clean India. A smart waste management system uses an IoT-based waste bin for collection and monitoring the level of waste inside the bin. The system does real-time monitoring of the waste bins and determines which are to be emptied. The system is implemented using two ultrasonic sensors. One of the ultrasonic sensors detects the level of waste in the bin, and the other detects the person approaching the bin to dispose of the waste. This detection helps with the automatic opening and closing of the lid. A servo motor is connected to the lid, which serves the action of closing and opening the lid. In this system, the level of waste in the bin will be sent to the concerned authorities. Thus, it reduces fuel costs and human labor, making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.

Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

Waste Utilization in Horticulture: An Overview

Waste management in horticulture plays a pivotal role in achieving environmental sustainability and optimizing resource utilization. One of the primary methods for waste utilization is composting, which involves the decomposition of organic waste materials to produce nutrient-rich compost. Compost serves as an excellent soil amendment, enhancing soil fertility and promoting plant growth. Additionally, anaerobic digestion offers a valuable approach for converting organic waste into biogas, a renewable energy source that can be used for heating, electricity generation, and vehicle fuel. Furthermore, fermentation processes can be employed to convert fruit and vegetable waste into valuable products such as animal feed and biofuels. The by-products of fermentation, including yeast and enzymes, have applications in various industries, contributing to the circular economy model. Advancements in precision agriculture and smart waste management systems have revolutionized waste utilization in horticulture. Technologies such as GPS, GIS mapping, wireless sensor networks and big data analytics enable data-driven, decision-making and optimize resource allocation. Precision planting, targeted spraying, automated pruning/harvesting and variable rate irrigation are among the innovative practices facilitated by these technologies, leading to increased productivity and efficiency in horticultural operations. Moreover, waste utilization in horticulture contributes to mitigating environmental pollution and reducing greenhouse gas emissions. By diverting organic waste from landfills and incineration, the emission of methane, a potent greenhouse gas, is minimized. Instead, organic waste is repurposed into valuable resources, thereby closing the loop on waste streams and promoting a circular economy.

Optimal selection and challenges of municipal waste management system using an integrated approach: a case study

ABSTRACT Efficient waste management is crucial for maintaining a clean and sustainable environment in Indian cities. Patna faces significant waste management challenges due to rapid population growth and urbanization. This has resulted in increased waste generation, straining the current waste management system. The state of Bihar ranks poorly in waste-management-based Environmental Performance, highlighting the need for improvement. Although around 4281.27 TPD of garbage is generated, only 4013.55 TPD is collected. Insufficient information regarding waste processing, landfilling, and technology adoption exposes critical gaps in solid waste management, including limited landfill facilities, inadequate technology implementation, and inadequate monitoring. Effective waste management is crucial for ensuring the sustainability and quality of life in Patna. To address these challenges, the implementation of Information and Communication Technologies (ICTs) and the Internet of Things (IoT) in waste management is proposed. This study focuses on evaluating the most suitable smart waste management system for Patna Municipal Corporation. By employing an MCDM model with fuzzy logic-based TFN, the authors identified the optimal MWM alternative based on economic, technical, environmental, and social criteria. Findings unequivocally establish the IoT-based Door-to-Door system (Ʌ1) as the most effective and economical solution for MWM in urban settings like Patna, characterized by a proximity coefficient (PCi) = 0.942809. Furthermore, Social acceptance (ӄ2) emerged as the most influential criterion with a weight of 0.19375. This system not only promises enhanced operational efficiency for municipalities but also offers significant environmental and social benefits, including waste reduction, improved public health, and heightened community engagement. A sensitivity analysis was conducted to validate the robustness of the proposed method. Notably, the IoT-based system demonstrated a higher sensitivity to cost changes, affirming its superiority in adapting to economic considerations. This insight is pivotal for decision-makers prioritizing cost efficiency in waste management solutions.

Smart Waste Management System for Smart & Sustainable City of Indonesia’s New State Capital: A Literature Review

Urbanization in Indonesia has led to increased waste generation, posing significant challenges to sustainability. Until now, the method for collecting and transporting waste in Indonesia is still done manually. Nusantara, the New State Capital of Indonesia, aims to address these challenges through the adoption of a Smart Waste Management System (SWMS), leveraging IoT and ICT technologies. This paper reviews the current state of technology research in this field. The proposed SWMS framework integrates various types of sensors for waste monitoring, categorizing, and real-time tracking. This system enhances operational efficiency by triggering waste pickup when bin fill levels exceed a set threshold and directing waste pickup vehicles equipped with GPS and surveillance systems to specific locations. Managed by a central management system accessible via web or mobile applications, this framework exemplifies an innovative approach to waste management in Nusantara, aligning with the city’s vision of becoming a smart and sustainable urban area. The paper concludes that integrating IoT and ICT in waste management is crucial for achieving sustainable urban environments. The successful implementation of SWMS in Nusantara could serve as a model for other cities globally.

Multi-layer Multi-purpose Smart Waste Management System Using the Internet of Things (IOT)

Multi-layer Multi-purpose Smart Waste Management System Using the Internet of Things (IOT)

Optimising Routes Scheduling of Garbage Truck for Waste Collection using Artificial Bee Colony Algorithm

The issue of waste generation has attracted significant public attention in today’s societies, not only because waste production is increasing quantitatively but also because some products and components are becoming more complex. The growing problem of municipal solid waste management has become the most serious environmental issue. Population growth has resulted in the increase in the generation of solid waste, which has become a serious issue that must be resolved. Optimised route planning is one of the most important components of a smart waste management system. Route planning is a technological approach to determining the shortest routes through optimisation. The route optimisation problem commonly referred to as the vehicle routing problem (VRP), has been thoroughly studied to find the best possible route for a vehicle. This study aims to find the optimal routes and reduce the distance travelled by the garbage truck when collecting waste using Artificial Bee Colony algorithm. ABC algorithm is an optimization method based on the clever behaviour of honey bee swarms. The artificial bee colony in the ABC algorithm is divided into three groups which are employed bees, onlookers and scouts. To provide neighbouring solutions that are guaranteed to be possible, swap operator is used. Results show that by using 10 trucks in total as the vehicle, the optimal solution produced a total of optimal distance for 94.9542 km. The findings of this study fulfilled the intended objectives which are to find the optimal route and shortest distance for the vehicles.

Railway Waste Management

Abstract: A smart waste management system is of great necessity looking at the current increase in waste produced every year. This is a very unorganized sector where no such precautions are taken for proper waste disposal. Railways face difficulty in managing the waste produced inside the coaches. In order to help the government and protect nature from corrosive nonbiodegradable waste this waste management system was designed. In this project Arduino UNO was the main processor used along with sensors like inductive proximity sensor and soil moisture sensor. The main principle of the project was based on the information processed by the sensors and effective segregation of the waste individual bins to store the waste produced. A main support is placed which helps in holding the complete structure together

Assessing the Measurement Model for Source-Separating Waste for Recycling under a Proposed Smart Waste Management Scheme in Shah Alam, Malaysia

Due to rapid urbanization, solid waste management (SWM) is a major challenge in Malaysia, hence the need to sustainably manage it. Compared with other states, Selangor produces the highest volume of domestic waste. Most of the state’s waste is generated in Shah Alam City. This condition is expected to worsen because the population of Shah Alam is projected to rise by 2.5% from 2018 to 2035. This situation will increase the demand for resources, production, and consumption, increasing the volume of waste generated in Shah Alam. Hence, the pressing necessity to advance from the current traditional waste management practices to a more sustainable SWM system has been identified as a key target in Shah Alam’s 2025–2030 plans. The Smart Waste Management System (SWMS) has been identified as a novel approach to dealing with the absence of route optimization, real-time information exchange, and the consequent increase in waste management costs. All of these elements have characterized the current traditional households’ SWM. However, because this method is novel, there is a dearth of knowledge on the appropriate measurement model for evaluating the dimension of households’ intention to recycle waste through source separation as well as measuring the determinants of such a pro-environmental intention under the new SWMS. Thus, confirmatory factor analysis (CFA) was carried out to verify the factorial structure of the variables, relying on the Theory of Planned Behavior (TPB) based on the structural dimensions identified in prior exploratory factor analysis (EFA). The study found support for the use of TPB as a relevant framework for modeling the intention for source separation and its determinants under SWMS.

Artificial Intelligence in Sustainable Development of Municipal Solid Waste Management

Abstract: The rapid urbanization and population growth in developing countries have resulted in increased generation of municipal solid waste, leading to environmental degradation and health hazards. Traditional waste management practices have become unsustainable and inefficient in addressing these challenges. This study proposes a smart waste management system that utilizes advanced technologies such as Artificial Intelligence and Internet of Things to find an optimized position for placing the dustbins using neural network; optimize route for garbage collectors using genetic algorithm; segregate waste into categories of materials and their re-use. The system also includes a waste pick-up and incentivization mechanism that invokes theories such as "Tragedy of commons" and "Invisible hands" to drive societal change. The aim is to not only advance technical aspects but also societal and environmental aspects, making future generations more sustainable.

IoT-Based Waste Segregation with Location Tracking and Air Quality Monitoring for Smart Cities

Massive human population, coupled with rapid urbanization, results in a substantial amount of garbage that requires daily collection. In urban areas, garbage often accumulates around dustbins without proper disposal at regular intervals, creating an unsanitary environment for humans, plants, and animals. This situation significantly degrades the environment. To address this problem, a Smart Waste Management System is introduced in this paper, employing machine learning techniques for air quality level classification. Furthermore, this system safeguards garbage collectors from severe health issues caused by inhaling harmful gases emitted from the waste. The proposed system not only proves cost-effective but also enhances waste management productivity by categorizing waste into three types: wet, dry, and metallic. Ultimately, by leveraging machine learning techniques, we can classify air quality levels and garbage weight into distinct categories. This system is beneficial for improving the well-being of individuals residing in close proximity to dustbins, as it enables constant monitoring and reporting of air quality to relevant city authorities.

Development of Solar Smart Waste Management System Using IOT

Abstract: 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.

Implementing smart waste management system for a sustainable circular economy in the textile industry

In recent years, global supply chains (SCs) have envisioned redesigning their sourcing, production, and distribution processes due to sustainable production and consumption progression. This has led to the concept of a zero-waste circular economy (CE), which is gaining increasing attention in both developed and emerging economies. The textile industry, in particular, has a significant environmental impact due to the use of toxic chemicals and the production of toxic waste throughout the clothing value chain. While the textile industry in emerging economies is increasingly attempting to transition to a CE model by implementing Smart Waste Management Systems (SWMS), challenges persist due to the complex industry structure involving numerous stakeholders, changing consumer behavior, and lack of strong standards. To address this significant issue, this study addresses challenges faced by the textile industry in adopting SWMS, specifically in the context of an emerging economy. To systematically identify the significant challenges and analyze strategies to address these challenges, a research framework using a hybrid approach, namely the grey Analytical Network Process (ANP), has been developed. The study uses a case study of the textile industry in Bangladesh to validate the effectiveness of the framework. The findings suggest efficient monitoring and control of waste recovery is the most critical step in adopting SWMS. This study offers detailed and comparative insights into the framework of implementing SWMS in the textile industry. Additionally, this research is the first to undertake an analytical decision approach to map challenges and strategies in implementing SWMS. The study outcomes will be beneficial for industry practitioners to ensure efficient monitoring and control of waste recovery and help the industry move towards a sustainable CE.

IoT based smart waste management system in aspect of COVID-19

The rapid evolution of the IoT has led to various research challenges for improving smart city applications. Owing to the characteristics and virtues of IoT services, waste management has emerged as a prominent issue in today's society. An undiscerning illegal eviction of waste, lack of waste disposal and management systems, and inept waste management policies have resulted in severe health and environmental challenges. Based on an integrative review, the proposed technique provides insight into the potential of smart cities and associated communities in assisting waste management initiatives. This study has referred to the existing waste management issues in urban areas and proposed an IoT-based smart waste management system of India in aspects of COVID-19 afflicted houses. Our system intends to improve waste management by making regular environmental sterility and making COVID situations more convenient. The proposed framework ensures a solution for efficiently handling waste generated in urban areas, focusing on the interaction among concessioners and waste generators to monitor the unfilled level of bins. This proposal offers dynamic waste collection scheduling and route optimization while achieving quality of service.

An Intelligent Waste Management Application Using IoT and a Genetic Algorithm–Fuzzy Inference System

The Internet of Things (IoT) is being used to create new applications for smart cities. Waste management is one issue that requires various IoT components for assistance, such as RFIDs and sensors. An efficient and innovative waste collection system is required to minimize investment, operational, and expenditure costs. In this paper, the novel idea is to develop an intelligent waste management model for smart cities using a hybrid genetic algorithm (GA)–fuzzy inference engine. The system can read, collect, and process information intelligently using a fuzzy inference engine that decides dynamically how to manage a waste collection. The aim of this model is to enhance its correctness and robustness, primarily, in addition to reducing errors that arise due to working conditions. GA is used for optimization to determine the best combination of rules for the fuzzy inference system (FIS). A Mamdani model is used to estimate waste management. The proposed model uses sensors to collect vital information, and FIS is trained using fuzzy logic to determine the probability that the smart bin is nearly full. The primary issue with the traditional genetic algorithm is that during the execution of the algorithm, there is a possibility of essential gene loss. The essential gene loss refers to information relevant to location, details regarding waste filling parameters, etc., which may lead to efficiency or accuracy loss. This problem is overcome by integrating fuzzy logic with a genetic algorithm to identify crucial genes by preserving the FIS interpretability. Our system uses cost-effective, small-size sensors and ensures this solution is reproducible. The Proteus simulator is used for experiments, and satisfactory results are obtained. Overall accuracy, precision, and recall of 95.44%, 96.68%, and 93.96% are obtained in the proposed model. Classification of recyclable items is also performed, and accuracy is determined for every item, resulting in the minimization of resource waste. The cost of manual interpretation is minimized in the intelligent smart waste management system in comparison to the traditional approach, as shown in the experiments.