Smart Waste Management Research Articles

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.

Practical Approach for Smart and Circular Cities: Chatbots Used in Waste Recycling

Sustainable development, smart waste management, and circular economy principles are paramount to the significant worldwide trend of smart city-related research and projects. The basic hypothesis of our research is that artificial intelligence (AI)-based IT applications have an increasingly important role in the field of smart cities in terms of issues related to waste management. In our present article, we set out to analyze the characteristics of chatbot applications dedicated to waste recycling in the case of smart cities and propose some innovative ideas to improve the efficiency of such applications. Based on the consultation and analysis of a whole series of chatbot-type applications used to facilitate the recycling activity, we systematically analyze and evaluate five illustrative examples of chatbots employed in the context of material recycling. We provide performance comparisons in a table based on specific relevant criteria. Furthermore, the detailed analysis of these chatbots has led to the idea of improving the performance of this type of application. In this sense, we propose a series of innovative concepts that can be successfully implemented in future chatbots dedicated to the field of the circular economy. Here, we detail the innovative ideas that can promote the circular economy and capitalize on the potential of chatbots in the waste recycling activity. We also identify some possible limitations of these new ideas that we propose to be implemented. As for future research directions, our goal is to develop a chatbot dedicated to improving waste recycling practices within the framework of a smart city. Such innovation holds promise in improving sustainability efforts and fostering environmental stewardship within urban environments.

A survey of smart dustbin systems using the IoT and deep learning

With massive population growth and a shift in the urban culture in smart cities, the constant generation of waste continues to create unsanitary living conditions for city dwellers. Overflowing solid waste in the garbage and the rapid generation of non-degradable solid waste produce a slew of infectious illnesses that proliferate throughout the ecosystem. Conventional solid waste management systems have proved to be increasingly harmful in densely populated areas like smart cities. Also, such systems require real-time manual monitoring of garbage, high labor costs, and constant maintenance. Monitoring waste management on a timely basis and reducing labor costs is scarcely possible, realistically, for a municipal corporation. A Smart Dustbin System (SDS) is proposed that is to be implemented in densely populated urban areas to ensure hygiene. This paper undertakes a comprehensive analysis of the application of smart dustbin systems, following an extensive literature review and a discussion of recent research that is expected to help improve waste management systems. A current SDS used in real-time is implemented with the most recent advances from deep learning, computer vision, and the Internet of Things. The smart dustbin system used in day-to-day life minimizes the overloading of bins, lowers labor costs, and saves energy and time. It also helps keep cities clean, lowering the risk of disease transmission. The primary users of the SDS are universities, malls, and high-rise buildings. The evolution of the SDS over the years with various features and technologies is well analyzed. The datasets used for Smart Waste Management and benchmark garbage image datasets are presented under AI perception. The results of the existing works are compared to highlight the potential limitations of these works.

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.

Prioritization of sustainable approaches for smart waste management of automotive fuel cells of road freight vehicles using the q-rung orthopair fuzzy CRITIC-EDAS method

Fuel cell electric vehicles (FCEVs) are a contemporary technology that is making headway in the expanding automotive industry. To promote FCEV effectiveness, environmentally acceptable limits need to be considered during their whole life cycle. End-of-life automotive fuel cells (AFCs) are one of the major contemporary challenges facing the sustainable business of transportation companies. The paper introduces a hybrid q-rung orthopair fuzzy (q-ROF)-based methodology to handle uncertainties and vagueness. It couples and extends the criteria importance through intercriteria correlation (CRITIC) and evaluation based on distance from average solution (EDAS) methods with q-ROF sets. This research aims to provide essential methodological and practical frameworks, as well as guidelines for improving the effectiveness of engaged FCEVs in transportation companies. The q-ROF CRITIC-EDAS methodology provides a systematic method for prioritizing sustainable approaches for smart waste management of AFCs of road freight vehicles. The incorporation of the real-life case study demonstrates its practical applicability and validity. The introduced q-ROF CRITIC-EDAS model could be extensively applied to the AFC waste management in order to assist stakeholders in making decisions consistent with the sustainability objectives.

AIWLO-WMO Model Based an Artificial Intelligence to Smart Waste Management for Smart Cities Environment

Smart waste management is one of the world's massive challenges, either in the advanced or started economies. Regularly, the technologies of artificial intelligence obtained recognition in presenting different computer techniques to solving smart waste challenge. The smart waste management of specified problems, events and doubts and partial statistics were capable for AI. Although this task did operate lots of findings, very few assessments proved the impact of AI to determine numerous difficulties of intelligent organization of waste. Perfect assessment of waste amount and condition is explained to Smart waste management technique development and model. Currently, heavy quantity of waste resources has become substantial enhanced with increasing population. Appropriate management of waste resources becomes necessary to decrease environmental degradation and recover value of life in smart cities. Smart Waste management supports to gather and heal waste resources from society. Suitable arrangement of waste objects needs the model of automated waste category models based on artificial intelligence and image processing-based methods. The aim of this paper, an automated artificial intelligence with world larva optimization supports waste management and organization (AIWLO-WMO) model is proposed for smart cities environment. The proposed model mainly develops a RetinaNet built object finding segment to recognize the presence of smart waste objects in the views. To better the category operation, Adagrad optimizer has used. To confirming the invented outcome of the AIWLO-WMO method, complete testing is performed on normal dataset and the found rates implied the authority of AIWLO-WMO model throughout other methods with expanded accuracy of 99.62%

Using Drone Technology Smart Waste Management in Reducing Municipal Solid Waste and Enhancing Society Recycling Rate

Fast industrial development and expansion in several portions of the earth have controlled to a rush in waste production. Presently, over 50 percent of the world’s population has established into smart cities. Near 2030, an expected 60 percent of the population will move from rural to urban zones, on behalf of up to 1.5 billion populations. The town population is established to rise considerably by 2045 to 6 billion; by 2050, it is predictable that seven out of 10 populations will be living within town zones. Last 30 years, the Kingdom of Saudi Arabia has verified financial growth and expansion rates corresponding by consistent directs of intake. It has indicated to an exponential raise in the capacity of municipal solid waste. Constant if the mass segment of this waste is recyclable, the inclination of residential to retain in the recycling of waste has so far constructed a minor influence. The objective of paper, smart waste management is a new frontier for local authorities assisting in reducing municipal solid waste and improving community recycling rate. We must solve this problem by driving Drone Technology, manufacturing and their established approaches are redefined. Unmanned Aerial Vehicles (UAVs) have released their capacity in the Smart Waste Management industry still again and have confirmed to be the most efficient, virtual tool. We have driven a drone program smart waste management to provide exact and real-time data of your smart waste’s progression and throughput. Drones are not only seeking your waste management, but also save the environment. The objective of zero waste is to decrease and finally destroy smart waste and smart waste management.

Optimizing Urban Sustainability: A Smart Waste Management System with Arduino Technology

Optimizing Urban Sustainability: A Smart Waste Management System with Arduino Technology

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)

Flutter Based Android Application for Smart Waste Management System

This abstract introduces a Flutter-based Android application designed for efficient waste management through QR-based dustbin location tracking and alerting. The application aims to revolutionize waste disposal by leveraging QR codes to identify and monitor dustbin locations. Users can scan QR codes placed on designated bins, enabling real-time tracking and status updates. The system generates alerts when bins reach capacity, notifying authorities for timely cleaning interventions. Additionally, the application integrates a unique feature for waste material sales, connecting users with recyclers. This Recycler Management System promotes sustainability by streamlining the recycling process and fostering collaboration between waste producers and recyclers. Overall, the Flutter Android application provides a comprehensive solution for smart waste management, incorporating tracking, alerts, and a marketplace for waste material transactions. In summary, the proposed Flutter-based application addresses key challenges in waste management through an innovative approach. By combining QR technology, real-time tracking, and a marketplace for waste material, the system not only facilitates efficient cleaning operations but also contributes to sustainable practices by promoting recycling. This holistic approach aligns with contemporary environmental priorities, offering a user-friendly and integrated solution for optimizing waste disposal processes

Waste management in the context of digitalization and the circular economy development

The study focuses on the analysis of existing approaches to waste management in different countries, taking account of digitalization and the circular economy development. The authors’ main objective was to define strategic guidelines for ensuring the formation of a waste management system by means of determining the level of achieving the waste management goals by various countries and studying their best practices in this field. The authors applied a clustering method to group the chosen countries based on the level of their indicators in waste management, digitalization, economic growth and achieving sustainable goals. In addition, cluster analysis was used while conducting the research. The study resulted in separating four clusters of the countries depending on their level of the selected indicators in 2022. The results of the research suggest that infrastructure and support of the state are essential factors in successful waste management. The authors offered some recommendations to facilitate the development of smart waste management by the states based on circularization, digitalization, interaction and regulatory support.

From data to value in smart waste management: Optimizing solid waste collection with a digital twin-based decision support system

The importance of waste management, including collection, separation, recovery, and recycling, increases with the growing amount of waste. Technological innovations such as smart connected products, the Internet of Things, and digital twins are driving the development of smart management systems. Investments in necessary product-service systems are justified by cost savings and improved service quality, especially in affluent societies like Switzerland. However, there is a trade-off between cost savings and service quality that raises the question of optimal balance. Using a Swiss municipality as an example, this paper models the trade-off between cost savings and service quality using waste bin sensor modules. Simulation results demonstrate the impact of cost savings on service quality reduction and that substantial cost savings are possible without a service quality compromise. We also introduce a digital process twin as a decision support system that is able to leverage a growing database. These results contribute to research, firstly through the field study with 98 waste bins equipped with fill level sensor modules, secondly through the model-based analysis of the trade-off between cost savings and service quality, and thirdly by conceptualizing a digital twin-based decision support system. The results further contribute to practice, firstly by providing benchmarks for implementing similar systems in other municipalities without having to create their own simulations, secondly by presenting an innovative key performance indicator to measure service quality, and thirdly with a model that can be used for simulations to determine the individual optimum between costs and service quality.

A Smart Waste Management System Framework Using IoT and LoRa for Green City Project

Waste management is a pressing concern for society, requiring substantial labor resources and impacting various social aspects. Green cities strive for achieving a net zero-carbon footprint, including efficient waste management. The waste management system deals with three problems that are interrelated: a) the timely checking of the status of bins to prevent overflow; b) checking the precise location of bins; and c) finding the optimal route to the filled bins. The existing systems fail to satisfy all three problem areas with a single solution. To track the overflow of the bin, the proposed model uses ultrasonic sensors, which are complemented with LoRa to transmit the exact location of the bins in a real-time environment. The existing models are not that efficient at calculating the exact bin-filled status along with the precise location of the bins. The Floyd-Warshall algorithm in the proposed model optimizes waste collection using the Floyd-Warshall algorithm to determine the shortest path. Leveraging low-cost IoT technologies, specifically LoRa modules for data transfer, our solution offers benefits such as simplicity, affordability, and ease of replacement. By employing the Floyd-Warshall algorithm with a time complexity of O (n^3), our method efficiently determines the most optimal waste pickup route, saving time and resources. This study presents a smart waste management solution utilising Arduino UNO microcontrollers, ultrasonic sensors, and LoRaWAN to measure waste levels accurately. The proposed strategy aims to create clean and pollution-free cities by addressing the problem of waste distribution caused by poor collection techniques.

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

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review.

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

Key Factors Affecting Smart Building Integration into Smart City: Technological Aspects

This research presents key factors influencing smart building integration into smart cities considering the city as a technological system. This paper begins with an overview of the concept of smart buildings, defining their features and discussing the technological advancements driving their development. The frameworks for smart buildings are presented, emphasizing energy efficiency, sustainability, automation, and data analytics. Then, the concept of a smart city and the role of digitalization in its development is explored. The conceptual framework of smart building into a smart city is presented, contributing to understanding the complex process of integrating smart buildings into smart cities. Further research delves into the factors influencing the integration of smart buildings into smart cities, focusing on energy, mobility, water, security systems, and waste management infrastructure domains. Each thematic area is examined, highlighting the importance of integration and the associated challenges and opportunities, based on research in the literature and the analysis of case studies. This enables the identification of 26 factors influencing integration and the synthesis of findings. The findings indicate that the successful integration of smart buildings into smart cities requires attention to multiple factors related to smart energy, smart mobility, smart water, smart security, and smart waste management infrastructures. The results obtained from this research provide valuable insights into the factors influencing smart building integration into a smart city from a technological perspective, enabling stakeholders to make informed decisions and develop strategies paving the way for sustainable, resilient, and efficient urban environments.

Pembimbingan siswa SD sekitar lokasi pantai wisata Kastela untuk smart berplastik

The issue of increasing waste quantities, which are directly proportional to the human population in a region, has become a significant concern within the context of environmental sustainability. Urban areas, being centers of education and economy, often face challenges in managing the daily volume of waste generated. The accumulation of such waste has adverse impacts on the environmental quality, including water, soil, and air. One proposed solution to address this problem is to enhance the understanding of waste management from an early age to adulthood. This engagement focuses on providing elementary school students with an understanding of smart waste management through interactive and play-based approaches. The methods employed in this engagement include lectures, demonstrations, and explorations. The engagement involves 30 students from elementary schools, selected using a purposive sampling technique. The findings demonstrate that the interactive and play-based approach effectively enhances students' understanding and environmental awareness. Furthermore, the students are able to implement initial steps in waste management, such as segregating organic and inorganic waste. The successful implementation of this engagement has yielded positive outcomes in increasing students' awareness and skills in waste management. The interactive and play-based approach has proven to be effective in instilling a meaningful understanding of responsible and sustainable waste management. It is hoped that activities of this nature can be continually enhanced and expanded in an effort to create a community that is more environmentally conscious.

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.