Advertisement

Real-Time Monitoring Data at the Time of the Networks

  • Giacomo ChiesaEmail author
Chapter
  • 185 Downloads
Part of the PoliTO Springer Series book series (PTSS)

Abstract

The chapter analyses some possible application scenarios on an urban scale with repercussions on the building scale deriving from the use of large quantities of real-time data produced by low quality sensors spread throughout the territory. The subject of data on an urban scale aimed at optimising and controlling design is closely related to studies on collective intelligence, techniques and implications due to the management, use and construction of big data, often derived from heterogeneous sources and not only numerical.

Keywords

Indoor air quality Real time data Open hardware monitoring Low cost monitoring Long tail information Big data 

Notes

Acknowledgements

In order to develop this chapter, voluntary use of the knowledge available online was made. A passive questioning of the knowledge disseminated was chosen in order to verify its usefulness. We must thank those who helped build the information present online. Thanks to Pierangelo Stradella for his support with the construction of the ethernet station presented in the chapter.

References

  1. Acquaviva A, Apiletti D, Attanasio A, Baralisi E, Bottaccioli L, Castagnetti FB, Cerquitelli T, Chiusano S, Macii E, Martellacci D, Patti E (2015) Energy signature analysis:knowledge at your fingertips. In: Proceedings of the IEEE International Congress on Big Data (BigData Congress), New York, 27 June–2 July, pp 543–550Google Scholar
  2. Allan A, Bradford K (2013) Distributed network data. O’Reilly, SebastopolGoogle Scholar
  3. Anderson C (2012) Makers: the new industrial revolution. Random House Business Books, LondonGoogle Scholar
  4. Benammar M, Abdaoui A, Ahmad SHM, Touati F, Kadri A (2018) A modular iot platform for real-time indoor air quality monitoring. Sensors 18:18pCrossRefGoogle Scholar
  5. Bernardo G (2011) Guida alla scelta e alla comprensione dei moduli XBee—Rev.1, distribuito gratuitamente da www.robot-italy.com e da www.settorezero.com, e.g. http://www.robot-italy.com/download/xbee/easy_bee.pdf. Last view Apr 2019
  6. Chiesa G (2010) Biomimetica, tecnologia e innovazione per l’architettura. Celid, TorinoGoogle Scholar
  7. Chiesa G (2013a) La città digitale, dai sensori ai modelli: Piattaforme interconnesse per la città del futuro. In: Di Giulio R et al (eds) Strategie di riqualificazione urbana: Rigenerazione e valorizzazione dell’edilizia sociale ad alta densità abitativa del secondo Novecento. Quodlibet, Macerata, pp 110–117Google Scholar
  8. Chiesa G (2013b) M.E.T.R.O. (Monitoring energy and technological real time data for optimization) innovative responsive conception for city futures. Ph.D. Thesis, Politecnico di Torino, TorinoGoogle Scholar
  9. Chiesa G (2014) Data, BigData and smart cities. Considerations and case study on environmental monitoring. Techne 08:81–89Google Scholar
  10. Chiesa G (2017) Explicit-digital design practice and possible areas of implication. Techne 13:236–242Google Scholar
  11. Chiesa G, La Riccia L (2013) Dalla rappresentazione alle rappresentazioni di paesaggi e territori. Planum 27(2)Google Scholar
  12. Chiesa G, Palme M (2018) Assessing climate change and urban heat island vulnerabilities in a built environment. Techne 15:237–245Google Scholar
  13. Chiesa G, Grosso M, Acquaviva A, Makhlouf B, Tumiatti A (2018) Insulation, building mass and airflows—provisional and multi-variable analysis. SMC-Sustainable Mediterranean Construction 8:36–40Google Scholar
  14. Chiesa G, Acquaviva A, Grosso M, Bottaccioli L, Floridia M, Pristeri E, Sanna EM (2019) Parametric optimization of window-to-wall ratio for passive buildings adopting a scripting methodology to dynamic-energy simulation. Sustainability 11:30.  https://doi.org/10.3390/su11113078CrossRefGoogle Scholar
  15. Clarke JA (2011) Excellence Ph.D. course simulation-based CAD: delivering detailed building performance information in real time. Politecnico di Torino, Turin, ItalyGoogle Scholar
  16. Dennis AK (2013) Raspberry Pi home automation with Arduino. Pack publishing, BirminghamGoogle Scholar
  17. Di Justo P, Gertz E (2013) Atmospheric monitoring with Arduino. O’Reilly, SebastopolGoogle Scholar
  18. Faludi R (2011) Building wireless sensor networks. O’Reilly, SebastopolGoogle Scholar
  19. Ferraris M (2003) Ontologia e oggetti sociali. In: Floridi L (ed) Linee di Ricerca. SWIF, pp 269–309. Viewed Jan 2014. www.swif.it/biblioteca/lr
  20. Gertz E, Di Justo P (2012) Environmental monitoring with Arduino. O’Reilly, SebastopolGoogle Scholar
  21. Ginsberg J et al (2009) Detecting influenza epidemics using search engine query data. Nature 457:1012–1015CrossRefGoogle Scholar
  22. Girardin F et al. (2007) Understanding of tourist dynamics from explicitly disclosed location information. In: 4th international symposium on LBS and telecartography. Hong-Kong, ChinaGoogle Scholar
  23. Givoni B (1998) Climate considerations in building and urban design. Wiley, Van Nostrand ReinholdGoogle Scholar
  24. Hashimoto S, Dijkstra R (2004) Chip city. In: Ferré A et al (eds) Verb Connection, Architecture Boogazine. Actar, Barcelona, pp 46–53Google Scholar
  25. Heiselberg P (ed) (2018) Ventilative cooling design guide. IEA EBC Annex 62. Aalborg University Press, AalborgGoogle Scholar
  26. http://www.dfrobot.com. Last view Apr 2019
  27. http://arduino.cc/. Last view Apr 2019
  28. http://blog.iteadstudio.com/. Last view Apr 2019
  29. http://www.digi.com. Last view Apr 2019
  30. http://senseable.mit.edu/. Last view Apr 2019
  31. http://cityform.mit.edu/. Last view Apr 2019
  32. http://netduino.com/. Last view Jan 2014
  33. Janert PK (2011) Data analysis with open source tools. O’Reilly, SebastopolGoogle Scholar
  34. Khabazi M (2009) Algorithmic modelling with grasshopper (Rhino Plug-in). Last view Dec 2010. www.khabazi.com/flux [no longer available], now accessible at https://www.pinterest.it/pin/191543790375951325/. Last view Apr 2019
  35. Larose DT (2005) Discovering knowledge in data. An introduction to data mining. Wiley, HobokenzbMATHGoogle Scholar
  36. Libelium (2012) GASES 2.0 Technical Guide, technical report project Waspmote ver. 2-0.2, 11/2012, Libelium Comunicaciones Distribuidas S.L., last view 2013. http://www.libelium.com/waspmote
  37. Libelium (2015) Agriculture 2.0. Technical guide. Waspmote, v.5.0. Libelium Comunicaciones Distribuidas S.L., ZaragozaGoogle Scholar
  38. Libelium (2016) Events 2.0. Technical guide. Waspmote, v.5.1. Libelium Comunicaciones Distribuidas S.L., ZaragozaGoogle Scholar
  39. Libelium (2018a) Waspmote Plug&Sense! Technical Guide, v8.0. Libelium Comunicaciones Distribuidas S.L., ZaragozaGoogle Scholar
  40. Libelium (2018b) Smart Cities Pro. Technical guide. Waspmote, v7.5. Libelium Comunicaciones Distribuidas S.L., ZaragozaGoogle Scholar
  41. Libelium (2018c) Smart Gases Pro. Technical guide. Waspmote, v7.5. Libelium Comunicaciones Distribuidas S.L., ZaragozaGoogle Scholar
  42. Mayer-Schönberger V, Cukier K (2013) Big data. Una rivoluzione che trasformerà il nostro modo di vivere e già minaccia la nostra libertà. Garzanti, Milano (ed. or. (2013) Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston)Google Scholar
  43. Monk S (2010) 30 Arduino projects for the evil genius. Tab Books, Blue Ridge SummitGoogle Scholar
  44. Myrup LO, Morgan DL (1972) Numerical model of the urban atmosphere: The city-surface interface (vol I). Department of Agricultural Engineering, Department of Water Science and Engineering, University of California, Davis, CA, Oct 1972Google Scholar
  45. Neumann G, Noda T, Kawaoka Y (2009) Emergence and pandemic potential of swine-origin H1N1 influenza virus. Nature 459:931–939CrossRefGoogle Scholar
  46. Nielsen M (2012) Le nuove vie della scoperta scientifica. Come l’intelligenza collettiva sta cambiando la scienza. Einaudi, Torino (or. ed. (2012) Reinventing discovery: the new era of networked science. Princeton University Press, Princeton)Google Scholar
  47. Nowak DJ (1994) Air pollution removal by Chicago’s urban forest. In: McPherson EG et al. (eds) Chicago’s urban forest ecosystem. Results of the Chicago urban forest climate project. Forest Service, US Department of Agriculture, NE, pp 63–81Google Scholar
  48. Odifreddi (1994) Epistemologia e Ontologia virtuali. In: Cenacolo Interdipartimentale di Torino, 27 May 1994, Torino, ItalyGoogle Scholar
  49. Osello A, Acquaviva A, Del Giudice M, Patti E, Rapetti N (2016) District information models. The DIMMER project: BIM tools for the urban scale. In: Pagani R, Chiesa G (eds) Urban data. Tools and methods towards the algorithmic city. FrancoAngeli, Milano, pp 231–261Google Scholar
  50. Payne A (2011) Interactive Prototyping. An introduction to physical computing using Arduino, Grasshopper, and Firefly. Last view Apr 2019. http://fireflyexperiments.com/resources/
  51. Premeaux E, Evans B (2011) Arduino projects to save the world. Apress, Springer Science distribution, New YorkCrossRefGoogle Scholar
  52. Richardshon M, Wallace S (2013) Getting started with Raspberry Pi. O’Reilly, SebastopolGoogle Scholar
  53. Riley M (2012) Programming your home. Automate with Arduino, Android, and your computer. The Pragmatic Programmers, Dallas & RaleighGoogle Scholar
  54. Rivoltella PC (ed) (2010) Ontologia della comunicazione educative. Metodo, ricerca, formazione. Vita e pensiero, MilanoGoogle Scholar
  55. Rosenthal J (2012) Special report: international banking. Big data crunching the numbers. The Economist, 19 May 2012, pp 7–8. Last view Apr 2019. http://www.economist.com/node/21554743 and http://media.economist.com/sites/default/files/sponsorships/MM152/20120519_international_banking_HSBC.pdf
  56. Santamouris M (ed) (2001) Energy and climate in the urban built environment. James and James, LondonGoogle Scholar
  57. Santamouris M (2011) Heat Island research in Europe: The state of the art. Adv Build Energy Res 1(1):123–150CrossRefGoogle Scholar
  58. Santamouris M, Haddad S, Fiorito F, Osmond P, Ding L, Prasad DK, Zhai X (2017) Urban heat island and overheating characteristics in Sydney, Australia. An analysis of multiyear measurements. Sustainability, 9(5): 21 pCrossRefGoogle Scholar
  59. Segarant T (2007) Programming collective intelligence. O’Reilly, SebastopolGoogle Scholar
  60. Shirky C (2010) Surplus Cognitivo. Creatività e generosità nell’era digitale. Codice Edizioni, Torino (or. ed. (2010) Cognitive surplus: creativity and generosity in a connected age. Penguin Group, London)Google Scholar
  61. Vico F, Lucà R (2005) DATASCAPES urbani. Conference ASITA 9° Conferenza Nazionale, Catania 15–18 Nov 2005Google Scholar
  62. Wang S (2010) Intelligent buildings and building automation. Spon Press, LondonGoogle Scholar
  63. Warden P (2011) Big data glossary. O’Reilly, SebastopolGoogle Scholar
  64. Weinberger D (2012) La stanza intelligente. La conoscenza come proprietà della rete. Codice edizioni, Torino (or. ed. (2011) Too big to know: rethinking knowledge now that the facts aren’t the facts, experts are everywhere, and the smartness person in the room is the room. Basic Book, New York)Google Scholar
  65. Yang J, Santamouris M (2018) Editorial. Urban heat island and mitigation technologies in Asian and Australian Cities—impact and mitigation. Urban Science 2:6 pCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dipartimento di Architettura e Design (DAD)Politecnico di TorinoTurinItaly

Personalised recommendations