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

Empowering citizens' cognition and decision making in smart sustainable cities

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft

A GeoSocial Intelligence Framework for Studying & Promoting Resilience to Seasonal Flooding in Jakarta, Indonesia

PetaJakarta.org is a web-based platform developed to harness the power of social media to gather, sort, and display information about flooding for Jakarta residents in real time. The platform runs on the open source software CogniCity—an OSS platform developed by the SMART Infrastructure Facility, University of Wollongong—which allows data to be collected and disseminated by community members through their location-enabled mobile devices. The project uses a GeoSocial Intelligence Framework to approach the complexity of Jakarta’s entangled hydraulic, hydrological and meteorological systems and thereby converts the noise of social media into knowledge about urban infrastructure and situational conditions related to flooding and inundation. In this paper, PetaJakarta.org co-directors Dr Tomas Holderness, Geomatics Research Fellow at the SMART Infrastructure Facility, Dr Etienne Turpin, Vice-Chancellor’s Postdoctoral Research Fellow at the SMART Infrastructure Facility, and Dr Rohan Wickramasuriyam, GIS Research Fellow at the SMART Infrastructure Facility, will discuss their GeoSocial Intelligence Framework as it applies to their current research in Jakarta. They will also present their preliminary findings from their 2014 Twitter #DataGrant, which has allowed them to develop a correlative analysis between historic social media information, the Jakarta government’s flood maps, and the infrastructure used to manage critical flood emergencies. Finally, they will speculate on several future applications of the CogniCity OSS and suggest how it might be developed to further promote an integrated civic co-management platform with the support of business, industry, government and community organizations

Urban Swarms: A new approach for autonomous waste management

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201

The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment