Wireless Personal Communications

, Volume 107, Issue 1, pp 95–119 | Cite as

Space-Based Data Centres: A Paradigm for Data Processing and Scientific Investigations

  • A. A. PeriolaEmail author
  • M. O. Kolawole


Terrestrial data centres have played and continue to play important role in data processing and usage for different applications. With increasing demand for data storage, access speed and implementation, reliance on further hosting of terrestrial data centres becomes problematic in densely populated regions. This leads to exploring space as hosting centres, and when properly configured, would enable cognitive computing for different applications, as well as allowing satellite networks integration. In this paper, the design of this space entity, the description of how it is integrated in satellite networks and additional applications, and the comparative cost of terrestrial and space siting of data centres is discussed. It also formulates the opportunity cost of using land for hosting data centres using a multi-factor exponential cost model and latency of space applications. Simulation shows that the use of space-based data centres (SBDC) reduces land costs for other purposes by a minimum and maximum of 3.3% and 66.3% on average, respectively. The use of SBDC also reduces the latency by 45.7% on average. The paper also implies that the use of SBDC may contribute to searching for extra-terrestrial intelligence and may trigger alien intelligence exhibition on the basis of alien external excitation hypothesis.


Satellite applications Cloud computing Land costs Latency Alien intelligence detection 



  1. 1.
    Gupta, R. K. (2016). Communications satellite RF payload technologies evolution: A system perspective. In Asia pacific microwave conference, 5–9 December 2016, New Delhi, India.Google Scholar
  2. 2.
    Kolawole, M. O. (2013). Satellite communication engineering (2nd ed.). New York: CRC Press.Google Scholar
  3. 3.
    Hofman, A., Glein, R., Frank, L., Wansch, R., & Heuberger, A. (2017). Reconfigurable on-board processing for flexible satellite communication systems using FPGA. Topical workshop on internet of space, 15–18 January 2017, Phoenix, AZ, USA.
  4. 4.
    Niles, L. (2018). Largest flock of earth imaging satellites launch into orbit from space station. In K. Ranney (Ed.) [Online]. Accessed 16 Feb 2018.
  5. 5.
    Rast, M., Schwehm, G., & Attena, E. (1999). Payload mass trends for earth observation and space exploration satellites. ESA Bulletin, 97, 1–5.Google Scholar
  6. 6.
    Yao, Y. (2016). Analysis of platform and payload integrated design technology for optical remote sensing satellites. In 3rd International symposium of space optical instruments and applications, Beijing, China, 26–29 June, 2016, pp. 9–22.Google Scholar
  7. 7.
    Hsu, J. (2018). 20,000 Leagues under the cloud. IEEE spectrum [Online]. Accessed 20 Feb 2018.
  8. 8.
    Cutler, B., Fowers, S., Kramer, J., & Peterson, E. (2017). Dunking the data centre. IEEE Spectrum, 54(3), 26–31.CrossRefGoogle Scholar
  9. 9.
    Krein, P. T. (2017). Data centre challenges and their power electronics. CPSS Transactions on Power Electronics and Applications, 2(1), 39–46.CrossRefGoogle Scholar
  10. 10.
    Reddy, V. D., Setz, B., Rao, G. S. V. R. K., Gangadharan, G. R., & Aiello, M. (2017). Metrics for sustainable data centres. IEEE Transactions in Sustainable Computing, 2(3), 290–303.CrossRefGoogle Scholar
  11. 11.
    Cabriol, N. A. (2016). Alien mindscapes—A perspective on the search for extra-terrestrial intelligence. Astrobiology, 16(9), 661–676.CrossRefGoogle Scholar
  12. 12.
    Hare, T. M., Skinner, J. A., Fortezzo, C. M., Tanaka, K. L., & Nava, R. A. (2012). The astrogeology mapping, remote-sensing, cartography, technology and research. In 43rd Lunar and planetary science conference, March 19–23, 2012, Woodlands, Texas.Google Scholar
  13. 13.
    Forgan, D. H. (2018). Exoplanet transits as the foundation of an interstellar communications network [Online]. Accessed 15 January 2018.
  14. 14.
    Koulikova, Y., & Almaty, L. R. (2017). Inmarsat global express [Online].!!PDF-E.pdf. Accessed 17 Dec 2017.
  15. 15.
  16. 16.
    Kasturirangan, K. Space odyssey—A down to earth perspective [Online].
  17. 17.
    Maggitti, P. G., Smith, K. G., & Katila, R. (2013). The complex search process of invention. Research Policy, 42, 90–102.CrossRefGoogle Scholar
  18. 18.
    Neufeld, M. J. (2018). The Von Braun Paradigm and NASA’s long term planning for human spaceflight [Online]. Accessed 15 Feb 2018.
  19. 19.
    Angeletti, P., Lisi, M., & Tognolatti, P. (2014). Software defined radio: A key technology for flexibility and reconfigurability in space applications. In IEEE metrology for aerospace, Benevento, Italy, 29–30 May 2014,
  20. 20.
    Budroweit, J., & Koelpin, A. (2017). Design Challenges of a highly integrated SDR platform for multi-band spacecraft applications in radiation environments. In IEEE global communications conference, 4–8 December 2017, Singapore, pp. 9–12.Google Scholar
  21. 21.
    Schwartz, A. (2017). Is the future of data centres in space? Futurist forum [Online]. Accessed 16 September 2017.
  22. 22.
    Creese, P. (2018). IBM/spacebelt white paper: The greater cloud dimension of secure data in low earth orbit [Online]. Accessed 28 March 2018.
  23. 23.
    Chatlani, S. (2017). The last frontier: Data centers reach for outer space [Online]. Accessed 12 December 2017.
  24. 24.
    Jakhu, R. S., & Pelton, J. N. (2014). The development of small satellite systems and technologies’, small satellites and their regulation (pp. 13–20). Berlin: Springer.CrossRefGoogle Scholar
  25. 25.
    Lai, B., de La, E., Blanco, R., Behrans, J. R., Green, E. K., Picard, A. J., & Balakrishnan, A. (2017). Global trends in small satellite, July 2017.
  26. 26.
    Carter, J. (2018). Air launch- low cost small satellite launch [Online]. Accessed 11 Feb 2018.
  27. 27.
    Bridges, C. P., Kenyon, S., Shaw, P., Simons, E., Visagie, L., Theodorus, T., Yeomans, B., Parsons, J., Lappas, V., Underwood, C., Jason, S., Mellor, D., Navarathinam, N., Wellstead, P., Schofield, A., Linehen, R., Ars, J. B., Dyer, B., Liddle, D., & Sweeting, M. N. (2013). A baptism of fire: The strand-1 nanosatellite’ small satellite conference, Utah, August 2013, pp. 1–12.Google Scholar
  28. 28.
    NASA. (2018). Phonesat the smartphone nanosatellite [Online]. Accessed 19 February 2018.
  29. 29.
    Jackel, S., & Scholz, B. (2015). Utilizing artificial intelligence to achieve a robust architecture for future robust spacecraft. In IEEE aerospace conference, 7–14 March 2015, Big Sky MT, USA.
  30. 30.
    Hein, A. M. (2018). Artificial intelligence probes for interstellar exploration and colonization [Online]. Accessed 12 February 2018.
  31. 31.
    Wright, J. T., & Oman-Reagan, M. P. (2017). Visions of human futures in space and SETI. International Journal of Astrobiology, pp. 1–12 [Online]. Accessed 13 Feb 2018.
  32. 32. Scholar
  33. 33.
  34. 34.
    Periola, A. A., & Falowo, O. E. (2016). Intelligent cognitive radio models for enhancing future radio astronomy observations. Advances in Astronomy, 2016, 1–15.CrossRefGoogle Scholar
  35. 35.
    Garcia, A. L., Zangrando, L., Sgravatto, M., Llorans, V., Valtero, S., Zaccoho, V., Bagnasco, S., Taneja, S., Pra, S. D., Salomoni, D., & Donvito, G. (2018). Improved cloud resource allocation: How INDICO-datacloud is overcoming the current limitations in cloud schedulers [Online]. Accessed 13 Feb 2018.
  36. 36.
    Brown, E. (2015). Computing at full capacity, MIT Press, July 31, 2015 [Online]. Accessed 12 Feb 2018.Google Scholar
  37. 37.
    Cauwenberghs, G. (2013). Reverse engineering the cognitive brain. In Proceedings of the national academy of sciences, Vol. 110, No. 39, 24 September 2013, pp. 15512–15513.Google Scholar
  38. 38.
    Detorakis, G., Sheik, S., Augustine, C., Paul, S., Pedroni, B. V., Duh, N., Krichnar, J., Cauwenberghs, G., & Neftci, E. (2017). Neural and synaptic array transceiver: A brain-inspired computing framework for embedded learning [Online]. Accessed 11 Dec 2017.
  39. 39.
    Huang, J., Leng, M., & Parker, M. (2013). Demand functions in decision modelling: A comprehensive survey and research directions. Decision Sciences, 44(3), 557–609.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical, Electronics and Computer EngineeringBells University of TechnologyOtaNigeria
  2. 2.Jolade Strategic Environmental and Engineering ConsultantsMoorabbinAustralia

Personalised recommendations