Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал:
http://elartu.tntu.edu.ua/handle/lib/38346| Назва: | Огляд давачів для сенсорних підсистем розумних міст |
| Інші назви: | Survey of sensors for sensory subsystems of smart cities |
| Автори: | Іваночко, Володимир Андрійович Ivanochko, Volodymyr Andriiovych |
| Приналежність: | ТНТУ ім. І. Пулюя, Факультет комп’ютерно-інформаційних систем і програмної інженерії, Кафедра комп’ютерних наук, м. Тернопіль, Україна |
| Бібліографічний опис: | Іваночко В.А. Огляд давачів для сенсорних підсистем розумних міст: кваліфікаційна робота освітнього рівня „Бакалавр“ „122 - комп’ютерні науки“ / В.А. Іваночко. - Тернопіль : ТНТУ, 2022. - 59 с. |
| Дата публікації: | 20-чер-2022 |
| Дата подання: | 6-чер-2022 |
| Дата внесення: | 28-чер-2022 |
| Країна (код): | UA |
| Місце видання, проведення: | ТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Україна |
| Науковий керівник: | Мацюк, Олександр Васильович |
| Члени комітету: | Микитишин, Андрій Григорович |
| УДК: | 004.62 |
| Теми: | розумні мережі smart grids оцифрування digitization кібербезпека cybersecurity розумне місто smart city розумні давачі smart sensors інтернет речей Internet of Things |
| Короткий огляд (реферат): | Міста мають задовольняти потреби своїх громадян і надавати найкращі послуги. Ідею міста майбутнього зазвичай представляють розумне місто, яке є більш ефективною системою, яка оптимізує свої ресурси та послуги за допомогою моніторингу та комунікаційних технологій. Давачі відіграють важливу роль у системі, оскільки вони збирають відповідну інформацію від міста, мешканців та відповідних комунікаційних мереж, які передають інформацію в режимі реального часу. Використання цих давачів різноманітне, їх застосування можна розділити на шість різних груп: енергія, здоров’я, мобільність, безпека, управління водою та відходами. На основі цих груп у цій роботі представлено аналіз різних давачів, які зазвичай використовуються при створенні розумних міст. Зрештою, цей процес стосується не лише розумної міської інфраструктури, а й, що ще важливіше, того, як ці нові можливості розпізнавання та розробки в області цифровізації покращують якість життя. Cities must meet the needs of their citizens and provide the best services. The idea of the city of the future is usually represented by a smart city, which is a more efficient system that optimizes its resources and services through monitoring and communication technologies. Providers play an important role in the system as they collect relevant information from the city, residents and relevant communication networks that transmit information in real time. The use of these sensors is diverse, and they can be divided into six different groups: energy, health, mobility, security, water and waste management. Based on these groups, this paper presents an analysis of various sensors that are commonly used in the creation of smart cities. Ultimately, this process is not only about smart urban infrastructure, but more importantly, how these new digitization and development opportunities improve the quality of life. |
| Зміст: | Вступ 1 Огляд наукових публікацій 1.1 Розумне місто 1.2 Розумні міста у світі 1.3 Висновок до першого розділу 2 Давачі 2.1 Давачі для моніторингу здоров'я 2.2 Давачі для мобільних додатків 2.3 Давачі безпеки 2.4 Давачі для моніторингу якості води 2.5 Давачі для моніторингу відходів 2.6 Давачі енергоефективності 2.7 Комунікаційні технології 2.8 Додатки 2.9 Проблеми та можливості 3 Безпека життєдіяльності, основи охорони праці 3.1 Надзвичайні ситуації мирного та воєнного часу, їх вплив на безпеку життєдіяльності населення України 3.2 Система менеджменту охорони праці Висновки Перелік використаних джерел |
| URI (Уніфікований ідентифікатор ресурсу): | http://elartu.tntu.edu.ua/handle/lib/38346 |
| Власник авторського права: | © Іваночко Володимир Андрійович, 2022 |
| Перелік літератури: | 1. Calzada, I. Metropolitan and city-regional politics in the urban age: Why does “(smart) devolution” matter? Palgrave Commun. 2017, 3, 1–17. 2. Batista e Silva, F.; Freire, S.; Schiavina, M.; Rosina, K.; Marín-Herrera, M.A.; Ziemba, L.; Craglia, M.; Koomen, E.; Lavalle, C. Uncovering temporal changes in Europe’s population density patterns using a data fusion approach. Nat. Commun. 2020, 11, 1–11. 3. Joss, S. Future cities: Asserting public governance. Palgrave Commun. 2018, 4, 1–4. 4. Pellicer, S.; Santa, G.; Bleda, A.L.; Maestre, R.; Jara, A.J.; Skarmeta, A.G. A global perspective of smart cities: A survey. In Proceedings of the 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Taichung, Taiwan, 3–5 July 2013; pp. 439–444. 5. Bai, X.; Nagendra, H.; Shi, P.; Liu, H. Cities: Build networks and share plans to emerge stronger from COVID-19. Nature 2020, 584, 517–520. 6. Staletić, N.; Labus, A.; Bogdanović, Z.; Despotović-Zrakić, M.; Radenković, B. Citizens’ readiness to crowdsource smart city services: A developing country perspective. Cities 2020, 107, 102883. 7. Pranggono, B.; Arabo, A. COVID-19 pandemic cybersecurity issues. Internet Technol. Lett. 2021, 4, 4–9.] 8. He, Y.; Aliyu, A.; Evans, M.; Luo, C. Health care cybersecurity challenges and solutions under the climate of COVID-19: Scoping review. J. Med. Internet Res. 2021, 23, 1–18. 9. Ahad, M.A.; Paiva, S.; Tripathi, G.; Feroz, N. Enabling technologies and sustainable smart cities. Sustain. Cities Soc. 2020, 61, 102301. 10. Moustaka, V.; Theodosiou, Z.; Vakali, A.; Kounoudes, A.; Anthopoulos, L.G. Enhancing social networking in smart cities: Privacy and security borderlines. Technol. Forecast. Soc. Chang. 2019, 142, 285–300. 11. Braun, T.; Fung, B.C.; Iqbal, F.; Shah, B. Security and privacy challenges in smart cities. Sustain. Cities Soc. 2018, 39, 499–507. 12. Kadry, S. Safe drive in smart city. Smart Solut. Future Cities 2016, 1–7. 13. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Group, T.P. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med. 2009, 6, 1–6. 14. Yang, L.; Yan, H.; Lam, J.C. Thermal comfort and building energy consumption implications—A review. Appl. Energy 2014, 115, 164–173. 15. Gunduz, M.Z.; Das, R. Cyber-security on smart grid: Threats and potential solutions. Comput. Netw. 2020, 169, 107094. 16. Zhao, H.X.; Magoulès, F. A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 2012, 16, 3586–3592. 17. Carli, R.; Cavone, G.; Othman, S.B.; Dotoli, M. IoT based architecture for model predictive control of HVAC systems in smart buildings. Sensors 2020, 20, 781. 18. Hancke, G.P.; de Silva, B.d.C.; Hancke, G.P. The role of advanced sensing in smart cities. Sensors 2013, 13, 393–425. 19. Benevolo, C.; Dameri, R.P.; Auria, B.D. Smart Mobility in Smart City. In Empowering Organizations: Enabling Platforms and Artefacts; Springer: Cham, Switzerland, 2016; Volume 11, pp. 13–28. 20. Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of things for smart cities. IEEE Internet Things J. 2014, 1, 22–32. 21. Capdevila, I.; Zarlenga, M.I. Smart City or Smart Citizens? The Barcelona Case J. Strategy Manag. 2015, 8, 266–282. 22. Sovacool, B.K.; Griffiths, S. Culture and low-carbon energy transitions. Nat. Sustain. 2020, 3, 685–693. 23. Yu, D.; Yin, J.; Wilby, R.; Lane, S. Disruption of emergency response to vulnerable populations during floods. Nat. Sustain. 2020, 3, 728–736. 24. Ejaz, W.; Anpalagan, A. Internet of Things for Smart Cities: Technologies, Big Data and Security; Springer: Cham, Switzerland, 2019; pp. 1–15. 25. Okai, E.; Feng, X.; Sant, P. Smart Cities Survey. In Proceedings of the 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, UK, 28–30 June 2018; pp. 1726–1730. 26. Sanchez, L.; Muñoz, L.; Galache, J.A.; Sotres, P.; Santana, J.R.; Gutierrez, V.; Ramdhany, R.; Gluhak, A.; Krco, S.; Theodoridis, E.; et al. SmartSantander: IoT experimentation over a smart city testbed. Comput. Netw. 2014, 61, 217–238. 27. Anthopoulos, L. Smart utopia VS smart reality: Learning by experience from 10 smart city cases. Cities 2017, 63, 128–148. 28. Lu, D.; Tian, Y.; Liu, V.Y.; Zhang, Y. The performance of the smart cities in China-A comparative study by means of self-organizing maps and social networks analysis. Sustainability 2015, 7, 7604–7621. 29. Joo, Y.M.; Tan, T.B. Smart cities in Asia: An introduction. In Smart Cities in Asia: Governing Development in the Era of Hyper-Connectivity; Edward Elgar Publishing: Cheltenham, UK, 2020; pp. 1–17. 30. Useche, M.P.; Carlos, N.S.J.; Vilafañe, C. Medellin (Colombia): A Case of Smart City. In Proceedings of the 7th International Conference on Theory and Practice of Electronic Governance ICEGOV ’13, Seoul, Korea, 22–25 October 2013; pp. 231–233. 31. Gaffney, C.; Robertson, C. Smarter than Smart: Rio de Janeiro’s Flawed Emergence as a Smart City. J. Urban Technol. 2018, 25, 47–64. 32. Yigitcanlar, T.; Kamruzzaman, M. Smart Cities and Mobility: Does the Smartness of Australian Cities Lead to Sustainable Commuting Patterns? J. Urban Technol. 2019, 26, 21–46. 33. Niculescu, A.I.; Wadhwa, B. Smart cities in south east Asia: Singapore concepts-An HCI4D perspective. In Proceedings of the ASEAN CHI Symposium’15, Seoul, Korea, 18–23 April 2015; pp. 20–23. 34. Endesa, F. Smart Cities. Available online: https://www.fundacionendesa.org/ es/recursos/a201908-smart-city (accessed on 13 May 2022). 35. Cities, B. Bright Cities. Available online: https://www.brightcities.city/ (accessed on 13 May 2022). 36. IMD Real Learning Real Impact; SCO Observatory. Smart City Index 2020. Available online: https://www.imd.org/smart-city-observatory/smart-city-index/ (accessed on 13 May 2022). 37. Wu, L.; Zhi, Y.; Sui, Z.; Liu, Y. Intra-urban human mobility and activity transition: Evidence from social media check-in data. PLoS ONE 2014, 9, e97010. 38. Chen, S.; Hu, J.; Shi, Y.; Peng, Y.; Fang, J.; Zhao, R.; Zhao, L. Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G. IEEE Commun. Stand. Mag. 2017, 1, 70–76. 39. Chang, C.Y.; Chien, L.C.; Chang, Y.H.; Kuo, E.C.; Hwang, Y.S. A smart public security strategy: The New Taipei City Technology defense plan. Procedia Comput. Sci. 2019, 159, 1715–1719. 40. Baig, Z.A.; Szewczyk, P.; Valli, C.; Rabadia, P.; Hannay, P.; Chernyshev, M.; Johnstone, M.; Kerai, P.; Ibrahim, A.; Sansurooah, K.; et al. Future challenges for smart cities: Cyber-security and digital forensics. Digit. Investig. 2017, 22, 3–13. 41. Anagnostopoulos, T.; Zaslavsky, A.; Kolomvatsos, K.; Medvedev, A.; Amirian, P.; Morley, J.; Hadjieftymiades, S. Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey. IEEE Trans. Sustain. Comput. 2017, 2, 275–289. 42. Aktemur, I.; Erensoy, K.; Kocyigit, E. Optimization of Waste Collection in Smart Cities with the use of Evolutionary Algorithms. In Proceedings of the 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications HORA 2020, Ankara, Turkey, 26–27 June 2020; pp. 1–8. 43. Pardini, K.; Rodrigues, J.J.; Diallo, O.; Das, A.K.; de Albuquerque, V.H.C.; Kozlov, S.A. A smart waste management solution geared towards citizens. Sensors 2020, 20, 2380. 44. de Mattos, W.D.; Gondim, P.R. M-Health Solutions Using 5G Networks and M2M Communications. IT Prof. 2016, 18, 24–29. 45. Mustapa, M.A.; Abu Bakar, M.H.; Mustapha Kamil, Y.; Syahir, A.; Mahdi, M.A. Bio-Functionalized Tapered Multimode Fiber Coated with Dengue Virus NS1 Glycoprotein for Label Free Detection of Anti-Dengue Virus NS1 IgG Antibody. IEEE Sens. J. 2018, 18, 4066–4072. 46. Zheng, Y.; Rundell, A. Biosensor immunosurface engineering inspired by B-cell membrane-bound antibodies: Modeling and analysis of multivalent antigen capture by immobilized antibodies. IEEE Trans. NanoBiosci. 2003, 2, 14–25. 47. Sekitani, T.; Someya, T. Stretchable organic integrated circuits for large-area electronic skin surfaces. MRS Bull. 2012, 37, 236–245. 48. Kim, Y.S.; Lee, J.; Ameen, A.; Shi, L.; Li, M.; Wang, S.; Ma, R.; Jin, S.H.; Kang, Z.; Huang, Y.; et al. Multifunctional Epidermal Electronics Printed Directly Onto the Skin. Adv. Mater. 2013, 25, 2773–2778. 49. Ko, H.; Stoykovich, M.; Song, J.; Malyarchuk, V.; Choi, W.; Yu, C.J.; Geddes, J.; Xiao, J.; Wang, S.; Huang, Y.; et al. A hemispherical electronic eye camera based on compressible silicon optoelectronics. Nature 2008, 454, 748–753. 50. Wang, Y.; Yang, R.; Shi, Z.; Zhang, L.; Shi, D.X.; Wang, E.; Zhang, G. Super-Elastic Graphene Ripples for Flexible Strain Sensors. ACS Nano 2011, 5, 3645–3650. 51. Pan, L.J.; Chortos, A.; Yu, G.; Wang, Y.; Isaacson, S.; Allen, R.; Shi, Y.; Dauskardt, R.; Bao, Z. An ultra-sensitive resistive pressure sensor based on hollow-sphere microstructure induced elasticity in conducting polymer film. Nat. Commun. 2014, 5, 3002. 52. Chen, Y.; Lu, B.; Chen, Y.; Feng, X. Biocompatible and ultra-flexible inorganic strain sensors attached to skin for long-term vital signs monitoring. IEEE Electron. Device Lett. 2016, 37, 496–499. 53. Zhang, Y.; Black, A.; Wu, N.; Cui, Y. Comparison of “Dry Sensing” and “Wet Sensing” of a Protein With a Graphene Sensor. IEEE Sens. Lett. 2018, 2, 1–4. 54. Yazdi, E.; Willig, A.; Pawlikowski, K. On channel adaptation in IEEE 802.15.4 mobile body sensor networks: What can be Gained? In Proceedings of the IEEE International Conference on Networks, ICON, Singapore, Singapore, 12–14 December 2012; pp. 262–267. 55. Liao, Y.; Leeson, M.S.; Higgins, M.D. Flexible quality of service model for wireless body area sensor networks. Healthc. Technol. Lett. 2016, 3, 12–15. 56. Al-Turjman, F.; Lemayian, J.P. Intelligence, security, and vehicular sensor networks in internet of things (IoT)-enabled smart-cities: An overview. Comput. Electr. Eng. 2020, 87, 106776. 57. Fanti, M.P.; Mangini, A.M.; Rotunno, G.; Fiume, G.; Favenza, A.; Gaetani, M. A Cloud Computing Architecture for Eco Route Planning of Heavy Duty Vehicles. In Proceedings of the 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Bavaria, Germany, 20–24 August 2018; pp. 730–735. 58. Difilippo, G.; Fanti, M.P.; Fiume, G.; Mangini, A.M.; Monsel, N. A Cloud Optimizer for Eco Route Planning of Heavy Duty Vehicles. In Proceedings of the 2018 IEEE Conference on Decision and Control (CDC), Miami, FL, USA, 17–19 December 2018; pp. 7142–7147. 59. Koehl, A. Urban transport and COVID-19: Challenges and prospects in low- and middle-income countries. Cities Health 2020, 1–6. 60. Hanbyul, S.; Ki-Dong, L.; Shinpei, Y.; Ying, P.; Philippe, S. LTE Evolution for Vehicle-to-Everything Services. IEEE Commun. Mag. 2016, 54, 22–28.] 61. Wang, J.; Shao, Y.; Ge, Y.; Yu, R. A survey of vehicle to everything (V2X) testing. Sensors 2019, 19, 334. 62. Toglaw, S.; Aloqaily, M.; Alkheir, A.A. Connected, Autonomous and Electric Vehicles: The Optimum Value for a Successful Business Model. In Proceedings of the 2018 Fifth International Conference on Internet of Things: Systems, Management and Security, Valencia, Spain, 15–18 October 2018; pp. 303–308. 63. Ozatay, E.; Onori, S.; Wollaeger, J.; Ozguner, U.; Rizzoni, G.; Filev, D.; Michelini, J.; Di Cairano, S. Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution. IEEE Trans. Intell. Transp. Syst. 2014, 15, 2491–2505. 64. Fanti, M.P.; Mangini, A.M.; Favenza, A.; Difilippo, G. An Eco-Route planner for heavy duty vehicles. IEEE/CAA J. Autom. Sin. 2021, 8, 37–51. 65. Zhang, J.; Lu, Y.; Lu, Z.; Liu, C.; Sun, G.; Li, Z. A new smart traffic monitoring method using embedded cement-based piezoelectric sensors. Smart Mater. Struct. 2015, 24, 1–8. 66. Hussein, A.; García, F.; Armingol, J.M.; Olaverri-Monreal, C. P2V and V2P communication for pedestrian warning on the basis of autonomous vehicles. In Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Rio de Janeiro, Brazil, 1–4 November 2016; pp. 2034–2039. 67. Ghazal, B.; Elkhatib, K.; Chahine, K.; Kherfan, M. Smart traffic light control system. In Proceedings of the 3rd International Conference on Electrical, Electronics, Computer Engineering and Their Applications, EECEA 2016, Beirut, Lebanon, 21–23 April 2016; pp. 140–145. 68. Ahas, R.; Aasa, A.; Yuan, Y.; Raubal, M.; Smoreda, Z.; Liu, Y.; Ziemlicki, C.; Tiru, M.; Zook, M. Everyday space–time geographies: Using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. Int. J. Geogr. Inf. Sci. 2015, 29, 2017–2039. 69. Cuenca-Jara, J.; Terroso-Saenz, F.; Valdes-Vela, M.; Gonzalez-Vidal, A.; Skarmeta, A.F. Human mobility analysis based on social media and fuzzy clustering. In Proceedings of the Global Internet of Things Summit GIoTS 2017, Geneva, Switzerland, 1–6 June 2017; pp. 1–6. 70. Fukuzaki, Y.; Murao, K.; Mochizuki, M.; Nishio, N. Statistical analysis of actual number of pedestrians for Wi-Fi packet-based pedestrian flow sensing. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015, Osaka, Japan, 7–11 September 2015; pp. 1519–1526. 71. El Mahrsi, M.K.; Come, E.; Oukhellou, L.; Verleysen, M. Clustering Smart Card Data for Urban Mobility Analysis. IEEE Trans. Intell. Transp. Syst. 2017, 18, 712–728. 72. Vlahogianni, E.I.; Kepaptsoglou, K.; Tsetsos, V.; Karlaftis, M.G. A Real-Time Parking Prediction System for Smart Cities. J. Intell. Transp. Syst. Technol. Plan. Oper. 2016, 20, 192–204. 73. Zhao, J.; Xu, H.; Liu, H.; Wu, J.; Zheng, Y.; Wu, D. Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transp. Res. Part C Emerg. Technol. 2019, 100, 68–87. 74. Walter, M.; Eilebrecht, B.; Wartzek, T.; Leonhardt, S. The smart car seat: Personalized monitoring of vital signs in automotive applications. Pers. Ubiquitous Comput. 2011, 15, 707–715. 75. Rahimian, P.; O’Neal, E.E.; Zhou, S.; Plumert, J.M.; Kearney, J.K. Harnessing Vehicle-to-Pedestrian (V2P) Communication Technology: Sending Traffic Warnings to Texting Pedestrians. Hum. Factors 2018, 60, 833–843. 76. Roccotelli, M.; Nolich, M.; Fanti, M.P.; Ukovich, W. Internet of things and virtual sensors for electromobility. Internet Technol. Lett. 2018. 77. Fanti, M.P.; Mangini, A.M.; Roccotelli, M.; Nolich, M.; Ukovich, W. Modeling Virtual Sensors for Electric Vehicles Charge Services. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 7–10 October 2018; pp. 3853–3858. 78. Fanti, M.P.; Mangini, A.M.; Roccotelli, M. An Innovative Service for Electric Vehicle Energy Demand Prediction. In Proceedings of the 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), Prague, Czech Republic, 19 June–2 July 2020; Volume 1, pp. 880–885. 79. Human Security Unit of the United Nation. Human Security Handbook an Integrated Approach for the Realization of the SDG’s; United Nations: New York, NY, USA, 2016; pp. 1–47. 80. Maye, D. ’smart food city’: Conceptual relations between smart city planning, urban food systems and innovation theory. City Cult. Soc. 2019, 16, 18–24. 81. Alipio, M.I.; Dela Cruz, A.E.M.; Doria, J.D.A.; Fruto, R.M.S. On the design of Nutrient Film Technique hydroponics farm for smart agriculture. Eng. Agric. Environ. Food 2019, 12, 315–324. 82. Matindoust, S.; Baghaei-Nejad, M.; Abadi, M.H.S.; Zou, Z.; Zheng, L.R. Food quality and safety monitoring using gas sensor array in intelligent packaging. Sens. Rev. 2016, 36, 169–183. 83. Lu, L.; Zhu, Z.; Hu, X. Hybrid nanocomposites modified on sensors and biosensors for the analysis of food functionality and safety. Trends Food Sci. Technol. 2019, 90, 100–110. 84. Zhou, J.; Cao, Z.; Dong, X.; Vasilakos, A.V. Security and Privacy for Cloud-Based IoT: Challenges. IEEE Commun. Mag. 2017, 55, 26–33. 85. Habibzadeh, H.; Soyata, T.; Kantarci, B.; Boukerche, A.; Kaptan, C. Sensing, communication and security planes: A new challenge for a smart city system design. Comput. Netw. 2018. 86. Ray, P.P.; Dash, D.; Kumar, N. Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and future directions. Comput. Commun. 2020, 160, 111–131. 87. Keshta, I.; Odeh, A. Security and privacy of electronic health records: Concerns and challenges. Egypt. Inform. J. 2020. 88. Kaku, K. Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia. Int. J. Disaster Risk Reduct. 2019, 33, 417–432. 89. Clark, N.E. Towards a standard licensing scheme for the access and use of satellite earth observation data for disaster management. Acta Astronaut. 2017, 139, 325–331. 90. Liu, L.; fan Li, C.; kun Sun, X.; Zhao, J. Event alert and detection in smart cities using anomaly information from remote sensing earthquake data. Comput. Commun. 2020, 153, 397–405. 91. Miyata, E.; Miyata, H.; Fukasawa, E.; Kakizaki, K.; Abe, H.; Katsumata, M.; Sato, M. A Hybrid semiconductor radiation detectors using conductive polymers. Nucl. Instrum. Methods Phys. Res. Sect. Accel. Spectrometers. Detect. Assoc. Equip. 2020, 955, 1–7. 92. Tanioka, Y.; Gusman, A.R. Near-field tsunami inundation forecast method assimilating ocean bottom pressure data: A synthetic test for the 2011 Tohoku-oki tsunami. Phys. Earth Planet. Inter. 2018, 283, 82–91. 93. Henríquez, B.P. Information Technology: The Unsung Hero of Market-Based Environmental Policies. 2004. Available online: Error! Hyperlink reference not valid. (accessed on 1 May 2022). 94. de Chalendar, J.A.; Taggart, J.; Benson, S.M. Tracking emissions in the US electricity system. Proc. Natl. Acad. Sci. USA 2019, 116, 25497–25502. 95. Badii, C.; Bilotta, S.; Cenni, D.; Difino, A.; Nesi, P.; Paoli, I.; Paolucci, M. High Density Real-Time Air Quality Derived Services from IoT Networks. Sensors 2020, 20, 5435. 96. Henríquez, B.P. Environmental Commodities Markets and Emissions Trading: Towards a Low-Carbon Future; Routledge: London, UK, 2012. 97. Pérez Henríquez, B. Handbook on the Resource Nexus; Chapter California Innovations @ the Water Energy Nexus (WEN); Routledge: London, UK, 2018; p. 18.] 98. Yan, K.; Zhang, Y.; Yan, Y.; Xu, C.; Zhang, S. Fault diagnosis method of sensors in building structural health monitoring system based on communication load optimization. Comput. Commun. 2020, 159, 310–316. 99. Ayyildiz, C.; Erdem, H.E.; Dirikgil, T.; Dugenci, O.; Kocak, T.; Altun, F.; Gungor, V.C. Structure Health Monitoring Using Wireless Sensor Networks on Structural Elements. Ad Hoc Netw. 2019, 82, 68–76, Erratum to 2020, 105, 68–76. 100. Smart street lighting system: A platform for innovative smart city applications and a new frontier for cyber-security. Electr. J. 2016, 29, 28–35. 101. Laufs, J.; Borrion, H.; Bradford, B. Security and the smart city: A systematic review. Sustain. Cities Soc. 2020, 55, 102023. 102. Ross, A.; Banerjee, S.; Chowdhury, A. Security in smart cities: A brief review of digital forensic schemes for biometric data. Pattern Recognit. Lett. 2020, 138, 346–354. 103. Mezzera, L.; Carminati, M.; Di Mauro, M.; Turolla, A.; Tizzoni, M.; Antonelli, M. A 7-Parameter Platform for Smart and Wireless Networks Monitoring On-Line Water Quality. In Proceedings of the 25th IEEE International Conference on Electronics Circuits and Systems, ICECS 2018, Bordeaux, Gironde, France, 9–12 December 2019; pp. 709–712. 104. Gonçalves, R.; Soares, J.J.; Lima, R.M. An IoT-based framework for smartwater supply systems management. Future Internet 2020, 12, 114. 105. Mohd Ismail, M.I.; Dziyauddin, R.A.; Salleh, N.A.A.; Muhammad-Sukki, F.; Bani, N.A.; Izhar, M.A.M.; Latiff, L.A. A review of vibration detection methods using accelerometer sensors for water pipeline leakage. IEEE Access 2019, 7, 51965–51981. 106. Kodali, R.K.; Sarjerao, B.S. A low cost smart irrigation system using MQTT protocol. In Proceedings of the IEEE International Symposium on Technologies for Smart Cities TENSYMP 2017, Kochi, Kerala, India, 14–16 July 2017; pp. 1–5. 107. Mamun, K.A.; Islam, F.R.; Haque, R.; Khan, M.G.; Prasad, A.N.; Haqva, H.; Mudliar, R.R.; Mani, F.S. Smart Water Quality Monitoring System Design and KPIs Analysis: Case Sites of Fiji Surface Water. Sustainability 2019, 11, 7110. 108. Quadar, N.; Chehri, A.; Jeon, G.; Ahmad, A. Smart water distribution system based on IoT networks, a critical review. In Smart Innovation, Systems and Technologies; Springer: Singapore, 2021; Volume 189, pp. 293–303. 109. Kulkarni, P.; Farnham, T. Smart City Wireless Connectivity Considerations and Cost Analysis: Lessons Learnt from Smart Water Case Studies. IEEE Access 2016, 4, 660–672. 110. de Oliveira, K.V.; Esgalha Castelli, H.M.; José Montebeller, S.; Prado Avancini, T.G. Wireless Sensor Network for Smart Agriculture using ZigBee Protocol. In Proceedings of the 2017 IEEE First Summer School on Smart Cities (S3C), Natal, Brazil, 6–11 August 2017; pp. 61–66. 111. Lopes, S.F.; Pereira, R.M.; Lopes, S.O.; Coutinho, M.; Malheiro, A.; Fonte, V. Yet a smarter irrigation system. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Springer Cham: Cham, Switzerland, 2020; Volume 323, pp. 337–346. 112. Martínez, R.; Vela, N.; el Aatik, A.; Murray, E.; Roche, P.; Navarro, J.M. On the use of an IoT integrated system for water quality monitoring and management in wastewater treatment plants. Water 2020, 12, 1096. 113. Simitha, K.M.; Subodh Raj, M.S. IoT and WSN Based Water Quality Monitoring System. In Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, Coimbatore, Tamil Nadu, India, 12–14 June 2019; pp. 205–210. 114. Pasika, S.; Gandla, S.T. Smart water quality monitoring system with cost-effective using IoT. Heliyon 2020. 115. Yang, W.; Wei, X.; Choi, S. A Dual-Channel, Interference-Free, Bacteria-Based Biosensor for Highly Sensitive Water Quality Monitoring. IEEE Sens. J. 2016, 16, 8672–8677. 116. Chen, Y.; Han, D. Water quality monitoring in smart city: A pilot project. Autom. Constr. 2018, 89, 307–316. 117. Esmaeilian, B.; Wang, B.; Lewis, K.; Duarte, F.; Ratti, C.; Behdad, S. The future of waste management in smart and sustainable cities: A review and concept paper. Waste Manag. 2018, 81, 177–195. 118. Folianto, F.; Low, Y.S.; Yeow, W.L. Smartbin: Smart waste management system. In Proceedings of the 2015 IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2015, Singapore, 7–9 April 2015; pp. 1–2.] 119. Medvedev, A.; Fedchenkov, P.; Zaslavsky, A.; Anagnostopoulos, T.; Khoruzhnikov, S. Waste Management as an IoT-Enabled Service in Smart Cities. In Proceedings of the 15th International Conference, NEW2AN 2015, and 8th Conference, ruSMART 2015, St. Petesburg, Russia, 26–28 August 2015; pp. 104–115. 120. Bharadwaj, a.S.; Rego, R.; Chowdhury, A. IoT Based Solid Waste Management System. In Proceedings of the 2016 IEEE Annual India Conference (INDICON), Bangalore, Karnataka, India, 16–18 December 2016; pp. 1–6. 121. Abdullah, N.; Alwesabi, O.A.; Abdullah, R. IoT-based smart waste management system in a smart city. In Advances in Intelligent Systems and Computing; Springer International Publishing: Cham, Switzerland, 2019; Volume 843, pp. 364–371. 122. Aiswatha, J.; Pankajakshan, A.; Nair, A.M.; Taha, A.B. Garbage monitoring robot. Proc. AIP Conf. 2020. 123. Lozano, Á.; Caridad, J.; De Paz, J.F.; González, G.V.; Bajo, J. Smart waste collection system with low consumption LoRaWAN nodes and route optimization. Sensors 2018, 18, 1465. 124. Jagtap, S.; Gandhi, A.; Bochare, R.; Patil, A.; Shitole, A. Waste Management Improvement in Cities using IoT. In Proceedings of the 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control, PARC 2020, Mathura, Uttar Pradesh, India, 28–29 February 2020; pp. 382–385. 125. Kang, K.D.; Kang, H.; Ilankoon, I.M.; Chong, C.Y. Electronic waste collection systems using Internet of Things (IoT): Household electronic waste management in Malaysia. J. Clean. Prod. 2020, 252, 119801. 126. Tripathi, D.K.; Dubey, S.; Agrawal, S.K. Survey on IOT based smart waste bin. In Proceedings of the 2020 IEEE 9th International Conference on Communication Systems and Network Technologies, CSNT 2020, Gwalior, India, 10–12 April 2020; pp. 140–144. 127. Memon, S.K.; Shaikh, F.K.; Mahoto, N.A.; Memon, A.A. IoT based smart garbage monitoring & collection system using WeMos & Ultrasonic sensors. In Proceedings of the 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 30–31 January 2019; pp. 1–6. 128. Raj, J.R.; Rajula, B.I.P.; Tamilbharathi, R.; Srinivasulu, S. AN IoT Based Waste Segreggator for Recycling Biodegradable and Non-Biodegradable Waste. In Proceedings of the 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, Coimbatore, Tamil Nadu, India, 6–7 March 2020; pp. 928–930. 129. De Paz, J.F.; Bajo, J.; Rodríguez, S.; Villarrubia, G.; Corchado, J.M. Intelligent system for lighting control in smart cities. Inf. Sci. 2016, 372, 241–255. 130. International Energy Agency. CO2 Emissions From Fuel Combustion—Overview 2017; IEA: Washington, DC, USA, 2017; ISBN 978-92-64-27819-6.] 131. Mogro, A.; Huertas, J. Assessment of the effect of using air conditioning on the vehicle’s real fuel consumption. Int. J. Interact. Des. Manuf. 2021, 15, 271–285. 132. Quirama, L.F.; Giraldo, M.; Huertas, J.I.; Jaller, M. Driving cycles that reproduce driving patterns, energy consumptions and tailpipe emissions. Transp. Res. Part D Transp. Environ. 2020, 82, 102294. 133. Giraldo, M.; Huertas, J.I. Real emissions, driving patterns and fuel consumption of in-use diesel buses operating at high altitude. Transp. Res. Part D Transp. Environ. 2019, 77, 21–36. 134. Geng, Y.; Ji, W.; Wang, Z.; Lin, B.; Zhu, Y. A review of operating performance in green buildings: Energy use, indoor environmental quality and occupant satisfaction. Energy Build. 2019, 183, 500–514. 135. Suryadevara, N.K.; Mukhopadhyay, S.C.; Kelly, S.D.T.; Gill, S.P.S. WSN-based smart sensors and actuator for power management in intelligent buildings. IEEE/ASME Trans. Mechatron. 2015, 20, 564–571. 136. Ejaz, W.; Muhammad, N.; Shahid, A.; Jo, M. Efficient Energy Management for Internet of Things in Smart Cities. IEEE Commun. Mag. 2016, 55, 84–91. 137. Ploennigs, J.; Ahmed, A.; Hensel, B.; Stack, P.; Menzel, K. Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating. Adv. Eng. Inform. 2011, 25, 688–698. 138. Li, H.; Hong, T.; Lee, S.H.; Sofos, M. System-level key performance indicators for building performance evaluation. Energy Build. 2020, 209, 109703. 139. Kumar, A.; Singh, A.; Kumar, A.; Singh, M.K.; Mahanta, P.; Mukhopadhyay, S.C. Sensing Technologies for Monitoring Intelligent Buildings: A Review. IEEE Sens. J. 2018, 18, 4847–4860. 140. Akhter, F.; Khadivizand, S.; Siddiquei, H.R.; Alahi, M.E.E.; Mukhopadhyay, S. Iot enabled intelligent sensor node for smart city: Pedestrian counting and ambient monitoring. Sensors 2019, 19, 3374. 141. Jo, O.; Kim, Y.K.; Kim, J. Internet of Things for Smart Railway: Feasibility and Applications. IEEE Internet Things J. 2018, 5, 482–490.] 142. Xiong, J.; Li, F.; Zhao, N.; Jiang, N. Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network. Sensors 2014, 14, 7209–7228. 143. International Energy Agency, I. International Energy Agency. Available online: https://www.iea.org/ (accessed on 15 May 2022). 144. Lau, S.P.; Merrett, G.V.; Weddell, A.S.; White, N.M. A traffic-aware street lighting scheme for Smart Cities using autonomous networked sensors. Comput. Electr. Eng. 2015, 45, 192–207. 145. Schaffers, H.; Komninos, N.; Pallot, M.; Trousse, B.; Nilsson, M.; Oliveira, A. Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation. Proc. Future Internet Conf. 2011, 6656, 431–446. 146. Higuera, J.; Hertog, W.; Perálvarez, M.; Polo, J.; Carreras, J. Smart lighting system ISO/IEC/IEEE 21451 compatible. IEEE Sens. J. 2015, 15, 2595–2602. 147. Kelly, S.D.T.; Suryadevara, N.K.; Mukhopadhyay, S.C. Towards the implementation of IoT for environmental condition monitoring in homes. IEEE Sens. J. 2013, 13, 3846–3853. 148. Idwan, S.; Mahmood, I.; Zubairi, J.A.; Matar, I. Optimal Management of Solid Waste in Smart Cities using Internet of Things. Wirel. Person. Commun. 2020, 110, 485–501. 149. Huang, H.; Gong, T.; Ye, N.; Wang, R.; Dou, Y. Private and Secured Medical Data Transmission and Analysis for Wireless Sensing Healthcare System. IEEE Trans. Ind. Inform. 2017, 13, 1227–1237. 150. Cao, B.; Ge, Y.; Kim, C.W.; Feng, G.; Tan, H.; Li, Y. An Experimental Study for Inter-User Interference Mitigation in Wireless Body Sensor Networks. Sens. J. IEEE 2013, 13, 3585–3595. |
| Тип вмісту: | Bachelor Thesis |
| Розташовується у зібраннях: | 122 — Компʼютерні науки (бакалаври) |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| 2022_KRB_SN-41_Ivanochko_V_A.pdf | 1,99 MB | Adobe PDF | Переглянути/відкрити |
Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.
Інструменти адміністратора