Edge Computing: A Comprehensive Survey of Current Initiatives and a Roadmap for a Sustainable Edge Computing Development

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Cite as text

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Edge Computing: A Comprehensive Survey of Current Initiatives and a Roadmap for a Sustainable Edge Computing Development", 
							Author= "Andrea Hamm, Alexander Willner, Ina Schieferdecker", 
							Doi= "https://doi.org/10.30844/wi_2020_g1-hamm", 
							 Abstract= "Edge Computing is a new distributed Cloud Computing paradigm in which computing and storage capabilities are pushed to the topological edge of a network. However, various standards and implementations are promoted by different initiatives. Lead by a reference architecture model for Edge Computing, current initiatives are analyzed by explorative content analysis. Providing two main contributions to the field, we present, first, how current initiatives are characterized, and second, a roadmap for sustainable Edge Computing relating three dimensions of sustainable development to four cross-concerns of Edge Computing. Findings show that most initiatives are internationally organized software development projects; important branches are currently telecom and industrial sectors; most addressed is the network virtualization layer. The roadmap reveals numerous chances and risks of Edge Computing related to sustainable development; such as the use of renewable energies, biases, new business models, increase and decrease of energy consumption, responsiveness, monitoring and traceability.

", 
							 Keywords= "Edge Computing, Computing Paradigm, Industry 4.0, Internet of Things, Sustainability", 
							}
					
Andrea Hamm, Alexander Willner, Ina Schieferdecker: Edge Computing: A Comprehensive Survey of Current Initiatives and a Roadmap for a Sustainable Edge Computing Development. Online: https://doi.org/10.30844/wi_2020_g1-hamm (Abgerufen 26.12.24)

Abstract

Abstract

Edge Computing is a new distributed Cloud Computing paradigm in which computing and storage capabilities are pushed to the topological edge of a network. However, various standards and implementations are promoted by different initiatives. Lead by a reference architecture model for Edge Computing, current initiatives are analyzed by explorative content analysis. Providing two main contributions to the field, we present, first, how current initiatives are characterized, and second, a roadmap for sustainable Edge Computing relating three dimensions of sustainable development to four cross-concerns of Edge Computing. Findings show that most initiatives are internationally organized software development projects; important branches are currently telecom and industrial sectors; most addressed is the network virtualization layer. The roadmap reveals numerous chances and risks of Edge Computing related to sustainable development; such as the use of renewable energies, biases, new business models, increase and decrease of energy consumption, responsiveness, monitoring and traceability.

Keywords

Schlüsselwörter

Edge Computing, Computing Paradigm, Industry 4.0, Internet of Things, Sustainability

References

Referenzen

1. Herh, M.: Korea-Led Edge Computing Technology Recognized as Global Standard, http://www.businesskorea.co.kr/news/articleView.html?idxno=23999, (2018).
2. Keshav, S.: Engineering Approach to Computer Networking, An: ATM Networks, the Internet, and the Telephone Network. Addison-Wesley Professional. Part of the Addison- Wesley Professional Computing Series. (1997).
3. Hu, L., Miao, Y., Wu, G., Hassan, M.M., Humar, I.: iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing. Future Gener. Comput. Syst. 90, 569–577 (2019).
4. Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., Liotta, A.: An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Trans. Ind. Inform. 15, 481–489 (2019).
5. Sayyad Khodashenas, P.S.K., Ruiz, C., Shuaib Siddiqui, M., Ferrer Riera, J.: The role of edge computing in future 5G mobile networks: concept and challenges. In: Markakis, E., Mastorakis, G., Mavromoustakis, C.X., and Pallis, E. (eds.) Cloud and Fog Computing in 5G Mobile Networks: Emerging advances and applications. pp. 349–370. Institution of Engineering and Technology (2017).
6. Nunna, S., Kousaridas, A., Ibrahim, M., Dillinger, M., Thuemmler, C., Feussner, H., Schneider, A.: Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing. In: 2015 12th ITNG Conf. pp. 601–605 (2015).
7. Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. 38, 697–713 (2018).
8. Jeschke, S., Brecher, C., Meisen, T., Özdemir, D., Eschert, T.: Industrial Internet of Things and Cyber Manufacturing Systems. In: Jeschke, S., Brecher, C., Song, H., and Rawat, D.B. (eds.) Industrial Internet of Things. pp. 3–19. Springer International Publishing, Cham (2017).
9. Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M., Yin, B.: Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access. 6, 6505–6519 (2018).
10. Giust, F., Sciancalepore, V., Sabella, D., Filippou, M.C., Mangiante, S., Featherstone, W., Munaretto, D.: Multi-Access Edge Computing: The Driver Behind the Wheel of 5GConnected Cars. IEEE Commun. Stand. Mag. 2, 66–73 (2018).
11. Porambage, P., Okwuibe, J., Liyanage, M., Ylianttila, M., Taleb, T.: Survey on Multi- Access Edge Computing for Internet of Things Realization. IEEE Commun. Surv. Tutor. 20, 2961–2991 (2018).
12. Nardi, B., Tomlinson, B., Patterson, D.J., Chen, J., Pargman, D., Raghavan, B., Penzenstadler, B.: Computing within limits. Commun. ACM. 61, 86–93 (2018).
13. Dao, V., Langella, I., Carbo, J.: From green to sustainability: Information Technology and an integrated sustainability framework. J. Strateg. Inf. Syst. 20, 63–79 (2011).
14. Chen, A.J.W., Boudreau, M., Watson, R.T.: Information systems and ecological sustainability. J. Syst. Inf. Technol. 10, 186–201 (2008).
15. Wissenschaftlicher Beirat der Bundesregierung Globale Umweltveränderungen (WBGU): Towards Our Common Digital Future Summary. (2019).
16. Cisco Systems Inc.: Cisco Visual Networking Index: Forecast and Methodology, 2008– 2013. (2009).
17. Cisco Systems Inc.: Cisco Visual Networking Index: Forecast and Trends, 2017–2022. (2019).
18. Pasek, A.: Managing Carbon and Data Flows: Fungible Forms of Mediation in the Cloud. 15 (2019).
19. Whitehead, B., Andrews, D., Shah, A., Maidment, G.: Assessing the environmental impact of data centres part 1: Background, energy use and metrics. Build. Environ. 82, 151– 159 (2014).
20. Roman, R., Lopez, J., Mambo, M.: Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges. Future Gener. Comput. Syst. 78, 680–698 (2018).
21. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6, 239–242 (2014).
22. Beier, G., Niehoff, S., Renn, O.: Industrie 4.0 – Effizienzwunder oder Ressourcenschleuder?, https://www.iass-potsdam.de/de/blog/2019/22/industrie-40-effizienzwunder-oderressourcenschleuder, last accessed 2019/08/08.
23. Voigt, K.-I., Kiel, D., Müller, J.M., Arnold, C.: Industrie 4.0 aus Perspektive der nachhaltigen industriellen Wertschöpfung. In: springerprofessional.de (2018).
24. Crouch, M., McKenzie, H.: The logic of small samples in interview-based qualitative research. Soc. Sci. Inf. 45, 483–499 (2006).
25. Mayring, P.: Qualitative content analysis: theoretical foundation, basic procedures and software solution. Klagenfurt (2014).
26. Ried, S.: Edge Computing: IoT Edge – Von Gateway bis Machine Learning, (2018).
27. Gall, R.: Energy sources and power management in IoT sensors and edge devices, https://jaxenter.com/energy-sources-power-management-iot-sensors-edge-devices- 145006.html, last accessed 2019/08/09.
28. Suárez-Albela, M., Fernández-Caramés, T.M., Fraga-Lamas, P., Castedo, L.: A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications. Sensors. 17, (2017).
29. Coulouris, G.F., Dollimore, J., Kindberg, T., Blair, G. eds: Distributed systems: concepts and design. Addison-Wesley, Boston, Mass. (2012).
30. Richoz, S., Perez-Uribe, A., Birch, P., Roggen, D.: Benchmarking deep classifiers on mobile devices for vision-based transportation recognition. In: Proceedings of the 2019 ACM UbiComp/ISWC. pp. 803–807. ACM Press, London, United Kingdom (2019).
31. Cossins, D.: Discriminating algorithms: 5 times AI showed prejudice, https://www.newscientist.com/article/2166207-discriminating-algorithms-5-times-aishowed- prejudice/, last accessed 2019/07/23.
32. Chen, C., Cook, D.J., Crandall, A.S.: The user side of sustainability: Modeling behavior and energy usage in the home. Pervasive Mob. Comput. 9, 161–175 (2013).
33. Widmer, R., Oswald-Krapf, H., Sinha-Khetriwal, D., Schnellmann, M., Böni, H.: Global perspectives on e-waste. Environ. Impact Assess. Rev. 25, 436–458 (2005).
34. Cui, J., Forssberg, E.: Mechanical recycling of waste electric and electronic equipment: a review. J. Hazard. Mater. 99, 243–263 (2003).
35. Perkins, D.N., Brune Drisse, M.-N., Nxele, T., Sly, P.D.: E-Waste: A Global Hazard. Ann. Glob. Health. 80, 286–295 (2014).
36. Abercrombie, R., Harries, E., Wharton, R.: Systems change: A guide to what it is and how to do it. (2015).

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