The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management. The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture. It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was implemented based on the designed architecture of the proposed system. The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally. The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036.
| Published in | Mathematics and Computer Science (Volume 4, Issue 1) |
| DOI | 10.11648/j.mcs.20190401.12 |
| Page(s) | 6-23 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Disposal, Embedded, Feature Extraction, IoT, Pattern Classifier, Smart City
| [1] | I. T., Union. 2014. International Telecommunication Union 2014. Measuring the Information Society Report 2014, ISBN 978-92-61-15291-8, pp. 1-270 https://www.itu.int/en/ITUD/Statistics/Documents/publications/mis2014/MIS2014_without_Annex_4.pdf (accessed 17.07.2016) |
| [2] | T. Nam, T. A. Pardo, Conceptualizing smart city with dimensions of technology, people, and institutions, in: Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, ACM, 2011, pp. 282-291. |
| [3] | M. Roscia, M. Longo, and G. C, Lazaroiu, Smart city by multi-agent systems, in: Proceedings of International Conference on Renewable Energy Research and Applications (ICRERA), IEEE, 2013, pp. 371-376. |
| [4] | A. Ohri, P. Singh, Development of decision support system for municipal solid waste management in India: a review. International Journal of Environmental Sciences, 2010, 1 (4): 440. |
| [5] | E. Muzenda, F. Ntuli, T. J. Pilusa, Waste management, strategies and situation in South Africa: an overview. World Academy of Science, Engineering and Technology, 2012, pp. 1-5. |
| [6] | S. Longhi, D. Marzioni, E. Alidori, G. Di Buo, M. Prist, M. Grisostomi, M. Pirro, Solid waste management architecture using wireless sensor network technology, in: 5th International Conference on New Technologies, Mobility and Security (NTMS), IEEE, 2012, pp. 1-5. |
| [7] | A. Patsakis, R. Venanzio, P. Bellavista, A. Solanas, M. Bouroche, Personalized medical services using smart cities' infrastructures, in: Medical Measurements and Applications (MeMeA), IEEE International Symposium on, IEEE, 2014, pp. 1-5. |
| [8] | A. Domingo, B. Bellalta, M. Palacin, M. Oliver, E. Almirall, Public open sensor data: Revolutionizing smart cities, Technology and Society Magazine, IEEE, 2013, 32 (4): pp.50-56. |
| [9] | W. A. Arbab, F. Ijaz, T. M. Yoon, C. Lee, A USN based Automatic waste collection system, in: Advanced Communication Technology (ICACT), 2012 14th International Conference on, IEEE, 2012, pp. 936-941. |
| [10] | M. A. Al Mamun, M. Hannan, A. Hussain, H. Basri, Wireless sensor network prototype for solid waste bin monitoring with energy efficient sensing algorithm, in: 16th International Conference on Computational Science and Engineering (CSE), IEEE, 2013, pp. 382-387. |
| [11] | M. Jiménez, A. Jiménez, P. Lozada, S. Jiménez, C. Jimenez, Using a Wireless Sensors Network in the Sustainable Management of African Palm Oil Solid Waste, in: Tenth International Conference on Information Technology: New Generations (ITNG), IEEE, 2013, pp. 133-137. |
| [12] | A. Chowdhury, M. U. Chowdhury, RFID-based real-time smart waste management system, in: Telecommunication Networks and Applications Conference, ATNAC Australasian, IEEE, 2007, pp. 175-180. |
| [13] | A. Bashir, S. A. Banday, A. R. Khan, M. Shafi, Concept, Design and Implementation of Automatic Waste Management System, International Journal on Recent and Innovation Trends in Computing and Communication, 2013, 1(7): pp. 604–609. |
| [14] | S. Longhi, D. Marzioni, E. Alidori, G. D. Buò, M. Prist, M. Grisostomi, M. Pirro, Solid waste management architecture using wireless sensor network technology. in: Proceedings of 2012 5th International Conference on New Technologies, Mobility and Security (NTMS), IEEE 2012, pp.1-5. |
| [15] | Y. Glouche, A. Sinha, P. Couderc, A Smart Waste Management with Self-Describing Complex Objects, Publisher, City, 2015. International Journal on Advances in Intelligent Systems, 2013, 8 (1-2): pp1-16. |
| [16] | M. Abbasi, M. Abduli, B. Omidvar, A. Baghvand, Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. International Journal of Environmental Research, 2012, 7 (1): pp.27-38. |
| [17] | E. Livani, R. Nguyen, J. Denzinger, G. Ruhe, S. Banack, A hybrid machine learning method and its application in municipal waste prediction, in: Industrial Conference on Data Mining, Springer, 2013, pp. 166-180. |
| [18] | T. Nuortio, J. Kytöjoki, H. Niska, O. Bräysy, Improved route planning and scheduling of waste collection and transport, Expert systems with applications 2006, 30 (2): pp.223-232. |
| [19] | A. Zanella, N. Bui, A. Castellani, L. Vangelista, M. Zorzi, Internet of things for smart cities, Internet of Things Journal, IEEE, 2014, 1 (1): 22-32. |
| [20] | L. Filipponi, A. Vitaletti, G. Landi, V. Memeo, G. Laura, P. Pucci, Smart city: An event driven architecture for monitoring public spaces with heterogeneous sensors, in: Sensor Technologies and Applications (SENSORCOMM), 2010 Fourth International Conference on, IEEE, 2010, pp. 281-286. |
| [21] | Z. Khan, S. L. Kiani, A cloud-based architecture for citizen services in smart cities, in: Proceedings of the 2012 IEEE/ACM fifth international conference on utility and cloud computing, IEEE Computer Society, 2012, pp. 315-320. |
| [22] | P. Bellini, M. Benigni, R. Billero, P. Nesi, N. Rauch, Km4City ontology building vs data harvesting and cleaning for smart-city services, Journal of Visual Languages & Computing, 201425 (6): pp.827-839. |
| [23] |
Arduino Uno Datasheet specification 2012. |
| [24] |
ATMega 328 Processor Datasheet. |
| [25] | R. Jalali, K. El-Khatib, C. McGregor, Smart city architecture for community level services through the internet of things, in: Intelligence in Next Generation Networks (ICIN), 2015 18th International Conference on, IEEE, 2015, pp. 108-113. |
| [26] | K. Spokas, J. Bogner, J. Chanton, M. Morcet, C. Aran, C. Graff, Y. Moreau-Le Golvan, I. Hebe, Methane mass balance at three landfill sites: What is the efficiency of capture by gas collection systems?, Waste Management, 2006, 26 (5): pp.516-525. |
| [27] |
LM 35 Precision centigrade temperature sensor datasheet. Texas Instrument. |
| [28] | J. S. Atkinson, O. Adetoye, M. Rio, J. E. Mitchell, G. Matich, Your WiFi is leaking: Inferring user behaviour, encryption irrelevant, in: Wireless Communications and Networking Conference (WCNC), IEEE, 2013, pp. 1097-1102. |
| [29] | K. Karimi, G. Atkinson, What the Internet of Things (IoT) needs to become a reality, White Paper, FreeScale and ARM, 2012: pp.1-16. |
| [30] | J. O. Adeyemo, E. Adetiba, O. Olugbara. Smart City Technology based Architecture for Refuse Disposal Management. In: IST-Africa 2016 Conference Proceedings, 2016, pp. 1-8. |
| [31] | A. Doukas, Building Internet of Things with the ARDUINO, CreateSpace Independent, in Proceedling of Sixth International Conference on Innovative Mobile and Internet Services (IMIS) in Ubiquitous Computing, IEEE, 2012, pp. 922-926. |
| [32] |
Maureira MA, Oldenhof D, Teernstra L. ThingSpeak–an API and Web Service for the Internet of Things, 2014. |
| [33] | A. Abayomi, O. O. Olugbara, E. Adetiba, D. Heukelman, Training Pattern Classifiers with Physiological Cepstral Features to Recognise Human Emotion, in: Advances in Nature and Biologically Inspired Computing, Springer, 2016, pp. 271-280. |
| [34] | Y. Indonesia, Principal component analysis combined with first order statistical method for breast thermal images classification, IJCST 2011, 2(2). |
| [35] | H. Cevikalp, Feature extraction techniques in high-dimensional spaces: in: Linear and nonlinear approaches, 2005, Vanderbilt University. |
| [36] | IT. Jolliffe, Rotation of principal components: some comment, Journal of Climatology 1986, 7(5), pp.507-510. |
| [37] | S. Kalkan, F. Wörgötter, N. Krüger, First-order and second-order statistical analysis of 3d and 2d image structure, Network: Computation in Neural Systems, 2007, 18(2), pp.129-160. |
| [38] | N. Aggarwal, R. Agrawal, First and second order statistics features for classification of magnetic resonance brain images, 2012, pp1-8. |
| [39] | R. Plamondon, S. N. Srihari, Online and off-line handwriting recognition: a comprehensive survey, IEEE Transactions. Pattern Analysis and Machine Intelligence, 2000 22, pp. 63–84. |
| [40] | N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge university press, 2000. |
| [41] |
Lippmann, R. P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, |
| [42] | E. Adetiba, O. O. Olugbara, Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features, in: Advances in Nature and Biologically Inspired Computing, Springer, 2016, pp. 271-280. |
| [43] | M.-C. Popescu, V. E. Balas, L. Perescu-Popescu, N. Mastorakis, Multilayer perceptron and neural networks, WSEAS Transactions on Circuits and Systems, 2009, 8 (7): 579-588. |
| [44] | D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning representations by back-propagating errors, 1986, Nature, 323, pp. 533–536 |
| [45] | G. Cybenko, Approximation by superpositions of a sigmoidal function, Mathematics of control, signals and systems, 1992, 2 (4): 303-314. |
| [46] | E. E. Jordaan, Development of robust inferential sensors: industrial applications of support vector machines for regression, in, Technische Universiteit Eindhoven, 2002. |
| [47] | H. Q. Minh, P. Niyogi, Y. Yao, Mercer’s theorem, feature maps, and smoothing, in: International Conference on Computational Learning Theory, Springer, 2006, pp. 154-168. |
| [48] | S. Oyewole, O. Olugbara, E. Adetiba, T. Nepal, Classification of Product Images in Different Color Models with Customized Kernel for Support Vector Machine, Third International Conference on Artificial Intelligence, Modelling and Simulation, 2015, pp. 153 -157. |
| [49] | B. Padmanabhan, 2012. Unified Modeling Language (UML) Overview. Available: http://people.eecs.ku.edu/~saiedian/Teaching/Fa13/810/Readings/UML-diagrams.pdf (Accessed 4 July 2016) |
| [50] | D. Bell, 2003. UML basics: An introduction to the Unified Modeling Language. The Rational Edge. |
| [51] | Z. Lu, D. Szafron, R. Greiner, P. Lu, D. S. Wishart, B. Poulin, J. Anvik, C. Macdonell, R. Eisner, Predicting subcellular localization of proteins using machine-learned classifiers, Bioinformatics, 2004, 20(4): 547–556. PMID: 1499045. |
| [52] | T. Koomen, M. Pol, Test process improvement: a practical step-by-step guide to structured testing, Addison-Wesley Longman Publishing Co., Inc., 1999. |
| [53] | T. G. Dietterich, G. Bakiri, Solving multiclass learning problems via error-correcting output codes, Journal of artificial intelligence research, 1995, 2, pp.263-286. |
| [54] | J. A. Whittaker, What is software testing? And why is it so hard? IEEE software 2000, 17 (1): pp.70-79. |
APA Style
Joke O. Adeyemo, Oludayo O. Olugbara, Emmanuel Adetiba. (2019). Development of a Prototype Smart City System for Refuse Disposal Management. Mathematics and Computer Science, 4(1), 6-23. https://doi.org/10.11648/j.mcs.20190401.12
ACS Style
Joke O. Adeyemo; Oludayo O. Olugbara; Emmanuel Adetiba. Development of a Prototype Smart City System for Refuse Disposal Management. Math. Comput. Sci. 2019, 4(1), 6-23. doi: 10.11648/j.mcs.20190401.12
AMA Style
Joke O. Adeyemo, Oludayo O. Olugbara, Emmanuel Adetiba. Development of a Prototype Smart City System for Refuse Disposal Management. Math Comput Sci. 2019;4(1):6-23. doi: 10.11648/j.mcs.20190401.12
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Download@article{10.11648/j.mcs.20190401.12,
author = {Joke O. Adeyemo and Oludayo O. Olugbara and Emmanuel Adetiba},
title = {Development of a Prototype Smart City System for Refuse Disposal Management},
journal = {Mathematics and Computer Science},
volume = {4},
number = {1},
pages = {6-23},
doi = {10.11648/j.mcs.20190401.12},
url = {https://doi.org/10.11648/j.mcs.20190401.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190401.12},
abstract = {The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management. The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture. It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was implemented based on the designed architecture of the proposed system. The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally. The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036.},
year = {2019}
}
TY - JOUR T1 - Development of a Prototype Smart City System for Refuse Disposal Management AU - Joke O. Adeyemo AU - Oludayo O. Olugbara AU - Emmanuel Adetiba Y1 - 2019/05/15 PY - 2019 N1 - https://doi.org/10.11648/j.mcs.20190401.12 DO - 10.11648/j.mcs.20190401.12 T2 - Mathematics and Computer Science JF - Mathematics and Computer Science JO - Mathematics and Computer Science SP - 6 EP - 23 PB - Science Publishing Group SN - 2575-6028 UR - https://doi.org/10.11648/j.mcs.20190401.12 AB - The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management. The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture. It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was implemented based on the designed architecture of the proposed system. The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally. The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036. VL - 4 IS - 1 ER -
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