GULP: Solar-Powered Smart Garbage Segregation Bins with SMS Notification and Machine Learning Image Processing

This study intends to build a smartbin that segregates solid waste into its respective bins. To make the waste management process more interesting for the end-users; to notify the utility staff when the smart bin needs to be unloaded; to encourage an environment-friendly smart bin by utilizing renewable solar energy source. The researchers employed an Agile Development approach because it enables teams to manage their workloads successfully and create the highest-quality product while staying within their allocated budget. The six fundamental phases are planning, design, development, test, release, and feedback. The Overall quality testing result that was provided through the ISO/IEC 25010 evaluation which concludes a positive outcome. The overall average was 4.55, which is verbally interpreted as excellent. Additionally, the application can also independently run with its solar energy source. Users were able to enjoy the whole process of waste disposal through its interesting mechanisms. Based on the findings, a compressor is recommended to compress the trash when the trash level reaches its maximum point to create more rooms for more garbage. An algorithm to determine multiple garbage at a time is also recommended. Adding a solar tracker coupled with solar panel will help produce more renewable energy for the smart bin.Comment: 19 pages, 6 figures, International Research Conference on Computer Engineering and Technology Education2023 (IRCCETE 2023

Mobile application to identify recyclable materials

This dissertation proposes a system to help the consumer recycle efficiently. The system is composed by a mobile application that can capture images of waste and classify their category through the usage of a machine learning model. Furthermore, this application can communicate with a server to update the model with new improved versions and also upload the images to the server in order to contribute to the creation of more precise model versions. The system has been validated by a fully working prototype. Although the proof of concept has been achieved, with some types of waste items correctly categorized, the machine learning model produced is not precise enough to be used in real-life scenarios, that is, for any type of waste. The main contributions of this study are a compendium of information in the area of computer vision and machine learning to categorize waste, and a working prototype system that utilizes crowdsourcing and machine learning elements to help the consumer recycle more efficiently.Nesta dissertação é proposto um sistema para ajudar o consumidor a reciclar eficientemente. O sistema é composto por uma aplicação móvel que captura imagens de lixo e classifica a sua categoria usando um modelo de aprendizagem automática. Consegue também comunicar com um servidor para atualizar o modelo com versões melhoradas e enviar as imagens para o servidor para contribuir para a criação de modelos mais precisos. Foi demonstrado através de um protótipo totalmente funcional que o sistema proposto funciona. Algumas imagens de lixo foram categorizadas correctamente, mas o modelo de aprendizagem automática produzido durante este projeto não é preciso o suficiente, em qualquer categoria de lixo, para usar em cenários da vida real. As principais contribuições deste estudo são um compêndio de informação na área de visão de computador e aprendizagem automática para categorizar lixo, e um sistema protótipo funcional que utiliza elementos de contribuição colaborativa e aprendizagem automática para ajudar o consumidor a reciclar mais eficientemente

IOT based on Smart Waste Mangement In Smart Cities

India which having 4,041 cities and towns, to clean the streets, roads and infrastructure of the country. The main motto of the mission is to clean garbage disposal of all the rural and urban areas of the country. This paper describe if wastage dust is filled (or) decompose waste in garbage disposal side garbage status is upload on web-page. Municipality member login that web-page and went particular area clean the wastage. If person did not clean the wastage before two days notification sent higher officers. IOT, controller & sensor are used. Here IOT module is used to control and monitor the waste and the information will be sent to the particular organization and the common man

Role of Modern Technologies and Internet of things in the field of Solid Waste Management

The process of handling solid waste becomes complex and tedious due to the urbanization and industrialization of the most developing and developed countries. These solid waste issues if it is not addressed properly it affects ecosystem and environment. There is a possibility of many health-oriented issues especially during the pandemic period covid-19. Most of the human beings are struggling with respiratory pulmonary diseases, asthma caused by these solid wastes. Most of the governments are also spending huge amount of money for labors, devices and some technologies to tackle these solid waste issues. There is also an opportunity for the government to generate revenue from these solid wastes by properly sorting these waste into recyclable, nonrecyclable and bio-degradable wastes. But when humans are involved in sorting these waste it will cause some diseases and hygienic problems. So,in order to address the above said issues in this work the role of modern technologies, algorithms and some Internet of things (IoT) methods are discussed. Implementing these technologies in the future will save huge amount of money spent by the government for the solid waste management activities

Data Driven Waste Management in Smart Cities

Bekreftelse fra programsansvarlig på at det holder kun med engelsk sammendrag. Grunnet masteroppgaven er skrevet på engelsk.Waste management is a critical issue worldwide. One of the major challenges in waste management is the efficient collection and transportation of waste from the source to the disposal facility. Research shows that systematic adoption of data-driven technologies (e.g. Machine Learning and Internet-of-Things) can assist public utilities (Kommune) by a) improving the waste collection management process, and b) minimizing the total incurred cost (Misra et al., 2018; Komninos, 2007). Thus, in this work, we show that systematic adoption of data-driven techniques can significantly improve the waste collection process and minimize the incurred cost to public utilities. In order to perform experiments, we generated a synthetic dataset motivated by a real-life urban environment. Also, we aimed to present different approaches to cost-benefit analysis in the targeted scenario. Our study shows that the systematic use of Internet-of-Things-based smart garbage bins, smart transportation algorithms, and Machine Learning can significantly reduce the total incurred cost of public utilities operating in this space

Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

[EN] Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.This research work was funded by the Ministry of Education and the Deanship of Scientific Research, Najran University. Kingdom of Saudi Arabia, under code number NU/ESCI/19/001.Hussain, A.; Draz, U.; Ali, T.; Tariq, S.; Glowacz, A.; Irfan, M.; Antonino Daviu, JA.... (2020). Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies. 13(15):1-22. https://doi.org/10.3390/en13153930S1221315Lionetto, M. G., Guascito, M. R., Caricato, R., Giordano, M. E., De Bartolomeo, A. R., Romano, M. P., … Contini, D. (2019). Correlation of Oxidative Potential with Ecotoxicological and Cytotoxicological Potential of PM10 at an Urban Background Site in Italy. Atmosphere, 10(12), 733. doi:10.3390/atmos10120733Wiedinmyer, C., Yokelson, R. J., & Gullett, B. K. (2014). Global Emissions of Trace Gases, Particulate Matter, and Hazardous Air Pollutants from Open Burning of Domestic Waste. Environmental Science & Technology, 48(16), 9523-9530. doi:10.1021/es502250zYan, F., Zhu, F., Wang, Q., & Xiong, Y. (2016). Preliminary Study of PM2.5 Formation During Municipal Solid Waste Incineration. Procedia Environmental Sciences, 31, 475-481. doi:10.1016/j.proenv.2016.02.054Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., & Pan, Y. (2006). Adverse health effects of outdoor air pollutants. Environment International, 32(6), 815-830. doi:10.1016/j.envint.2006.03.012Gollakota, A. R. K., Gautam, S., & Shu, C.-M. (2020). Inconsistencies of e-waste management in developing nations – Facts and plausible solutions. Journal of Environmental Management, 261, 110234. doi:10.1016/j.jenvman.2020.110234Anitha, A. (2017). Garbage monitoring system using IoT. IOP Conference Series: Materials Science and Engineering, 263, 042027. doi:10.1088/1757-899x/263/4/042027Sirsikar, S., & Karemore, P. (2015). Review Paper on Air Pollution Monitoring system. IJARCCE, 218-220. doi:10.17148/ijarcce.2015.4147Tavares Neto, R. F., & Godinho Filho, M. (2013). Literature review regarding Ant Colony Optimization applied to scheduling problems: Guidelines for implementation and directions for future research. Engineering Applications of Artificial Intelligence, 26(1), 150-161. doi:10.1016/j.engappai.2012.03.011Ali, T., Irfan, M., Alwadie, A. S., & Glowacz, A. (2020). IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering, 45(12), 10185-10198. doi:10.1007/s13369-020-04637-wSilva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697-713. doi:10.1016/j.scs.2018.01.053Gutierrez, J. M., Jensen, M., Henius, M., & Riaz, T. (2015). Smart Waste Collection System Based on Location Intelligence. Procedia Computer Science, 61, 120-127. doi:10.1016/j.procs.2015.09.170Carbon Monoxide Dangers in the Boiler Room www.pmmag.com/articles/97528-carbonmonoxide-danger-in-the-boiler-roomDe Vito, S., Massera, E., Piga, M., Martinotto, L., & Di Francia, G. (2008). On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sensors and Actuators B: Chemical, 129(2), 750-757. doi:10.1016/j.snb.2007.09.060Guiry, J., van de Ven, P., & Nelson, J. (2014). Multi-Sensor Fusion for Enhanced Contextual Awareness of Everyday Activities with Ubiquitous Devices. Sensors, 14(3), 5687-5701. doi:10.3390/s140305687Ali, T., Draz, U., Yasin, S., Noureen, J., shaf, A., & Zardari, M. (2018). An Efficient Participant’s Selection Algorithm for Crowdsensing. International Journal of Advanced Computer Science and Applications, 9(1). doi:10.14569/ijacsa.2018.090154Ali, T., Noureen, J., Draz, U., Shaf, A., Yasin, S., & Ayaz, M. (2018). Participants Ranking Algorithm for Crowdsensing in Mobile Communication. ICST Transactions on Scalable Information Systems, 5(16), 154476. doi:10.4108/eai.13-4-2018.15447

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in tim

Smart waste bin monitoring using IoT for sustainable biomedical waste management.

Suboptimal management of healthcare waste poses a significant concern that can be effectively tackled by implementing Internet of Things (IoT) solutions to enhance trash monitoring and disposal processes. The potential utilisation of the Internet of Things (IoT) in addressing the requirements associated with biomedical waste management within the Kaduna area was examined. The study included a selection of ten hospitals, chosen based on the criterion of having access to wireless Internet connectivity. The issue of biomedical waste is significant within the healthcare sector since it accounts for a considerable amount of overall waste generation, with estimates ranging from 43.62 to 52.47% across various facilities. Utilisation of (IoT) sensors resulted in the activation of alarms and messages to facilitate the prompt collection of waste. Data collected from these sensors was subjected to analysis to discover patterns and enhance the overall efficiency of waste management practices. The study revealed a positive correlation between the quantity of hospital beds and the daily garbage generated. Notably, hospitals with a higher number of beds were observed to generate a much greater amount of waste per bed. Hazardous waste generated varies by hospital, with one hospital leading in sharps waste (10.98 kgd-1) and chemical waste (21.06 kgd-1). Other hospitals generate considerable amounts of radioactive waste (0.60 kgd-1 and 0.50 kgd-1), pharmaceuticals, and genotoxic waste (16.19 kgd-1), indicating the need for specialised waste management approaches. The study sheds light on the significance of IoT in efficient waste collection and the need for tailored management of hazardous waste

A Smart-bin prototype for in-house waste management

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityA healthy environment is imperative to a healthy and happy community. Clean and well monitored environments are a key need in human habitable environments. In-House management and monitoring of waste is a key aspect in achieving this. The existing system of using waste monitoring personnel to regularly check and empty filled dustbins, the process has been prone to delays or neglect. Additionally, due to different frequency of usage of dustbins in different areas, routine checks which are based on time crevices is inefficient because a dustbin might get filled early and may need immediate attention or there might not be any need of a routine check for a long period of time. This makes present system resource expensive and ineffectual, as overflowing, stinking dustbins become more of a problem than a solution. This study presents a solution about the Smart-Bin Prototype for In-House Waste Management which integrates the idea of IoT with Wireless Sensor Networks. Arduino ATmega328P Microcontroller is used to interface the sensor system and the IoT hub. Dustbins in an area are embedded with low power and low cost smart ultrasonic and gas sensors that are connected to an IoT device, which acts as a central hub for all bins. The bins transmit their gas content and its fill-level status to the central hub which relays this data to a cloud platform. The cloud platform further pushes the data to the client app a GUI (Web or mobile) in which the current gas content and their state (filled or not) is displayed. The client app GUI can be used by the waste management personnel and data can be used to plan their routine check. This timely garbage monitoring would optimize resources, reduce cases of neglect and is easy to adopt