Asia pacific journal of information systems

  • 제32권4호
  • /
  • Pages.916-944
  • /
  • 2022
  • /
  • 2288-5404(pISSN)
  • /
  • 2288-6818(eISSN)
한국경영정보학회 (The Korea Society of Management Information Systems)
권호 표지
DOI QR코드

DOI QR Code

Artificial Intelligence as a Vehicle for Innovation: Literature Review and Bibliometric Study

  • 투고 : 2022.07.13
  • 심사 : 2022.10.18
  • 발행 : 2022.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial Intelligence has been a conceptual area for several decades. It has been studied extensively through experiments by the Information Systems community. When Information Systems supported with Information Technology became all pervasive in business and other allied areas, gradually the advancements in Artificial Intelligence also emerged as innovations across domains. Artificial Intelligence by definition is expected to substitute Human Intelligence, thereby making a huge space for innovation. In fact, all processes effected by human intelligence are liable to be replaced by AI which in itself is a massive innovation space. This paper will study the publication's repository (Scopus and Google Scholar from 1983 till 2021) in the area of Artificial Intelligence and innovation, then analyze the trend to gain insight into the evolution of AI as a vehicle for innovation.

키워드

참고문헌

  1. Adner, R., and Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure of technological interdependence affects firm performance in new technology generations. Strategic Management Journal, 31(3), 306-333. https://doi.org/10.1002/smj.821
  2. Agrawal. A., Gans, J. S., and Goldfarb, A. (2018). Exploring the impact of artificial intelligence: Prediction versus judgment (No. 24626). NBER Working Paper, NBER Working Paper, Cambridge, MA. https://doi.org/10.3386/w24626
  3. Akter, S., McCarthy, G., Sajib, S., Michael, K., Dwivedi, Y. K., D'Ambra, J., and Shen, K. N. (2021). Algorithmic bias in data-driven innovation in the age of AI. International Journal of Information Management, 60, 102387.
  4. Allam, S. (2016). The Impact of artificial intelligence on innovation: An exploratory analysis. International Journal of Creative Research Thoughts (IJCRT), 4(4), 810-814.
  5. Andriole, S. J. (2017). Five myths about digital transformation. MIT Sloan Management Review, 58(3), 1-22.
  6. Andriopoulos, C., and Lewis, M. W. (2009). Exploitation-exploration tensions and organizational ambidexterity: Managing paradoxes of innovation. Organization Science, 20(4), 696-717. https://doi.org/10.1287/orsc.1080.0406
  7. Ananny, M., and Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989.
  8. Asheim, B. T., and Coenen, L. (2005). Knowledge bases and regional innovation systems: Comparing nordic clusters. Research Policy, 34(8), 1173-1190. https://doi.org/10.1016/j.respol.2005.03.013
  9. Baden-Fuller, C., and Haefliger, S. (2013). Business models and technological innovation. Long Range Planning, 46(6), 419-426. https://doi.org/10.1016/j.lrp.2013.08.023
  10. Baker, H. K., Pandey, N., Kumar, S., and Haldar, A. (2020). A bibliometric analysis of board diversity: Current status, development, and future research directions. Journal of Business Research, 108, 232-246. https://doi.org/10.1016/j.jbusres.2019.11.025
  11. Barocas, S., and Selbst, A. D. (2016). Big data's disparate impact. Calif. L. Rev., 104, 671.
  12. Barro, S., and Davenport, T. H. (2019). People and machines: Partners in innovation. MIT Sloan Management Review, 60(4), 22-28.
  13. Bastug, E., Bennis, M., Medard, M., and Debbah, M. (2017). Toward interconnected virtual reality: Opportunities, challenges, and enablers. IEEE Communications Magazine, 55(6), 110-117.
  14. Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1-13. https://doi.org/10.1080/1369118X.2016.1216147
  15. Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S., and Rickne, A. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37(3), 407-429. https://doi.org/10.1016/j.respol.2007.12.003
  16. Bergek, A., Jacobsson, S., and Sanden, B. A. (2008). 'Legitimation' and 'development of positive externalities': Two key processes in the formation phase of technological innovation systems. Technology Analysis & Strategic Management, 20(5), 575-592. https://doi.org/10.1080/09537320802292768
  17. Bergek, A., Hekkert, M., Jacobsson, S., Markard, J., Sanden, B., and Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51-64. https://doi.org/10.1016/j.eist.2015.07.003
  18. Biggs, D., De Ville, B., and Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics, 18(1), 49-62. https://doi.org/10.1080/02664769100000005
  19. Binz, C., Truffer, B., and Coenen, L. (2014). Why space matters in technological innovation systems-Mapping global knowledge dynamics of membrane bioreactor technology. Research Policy, 43(1), 138-155. https://doi.org/10.1016/j.respol.2013.07.002
  20. Birkinshaw, J., Hamel, G., and Mol, M. J. (2008). Management innovation. Academy of management Review, 33(4), 825-845.
  21. Bohr, A., and Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25-60). Academic Press.
  22. Brewer, A., Sloan, N., and Landers, T. L. (1999). Intelligent tracking in manufacturing. Journal of Intelligent Manufacturing, 10(3), 245-250. https://doi.org/10.1023/A:1008995707211
  23. Brynjolfsson, E., Hu, Y., and Smith, M. D. (2003). Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Science, 49(11), 1580-1596. https://doi.org/10.1287/mnsc.49.11.1580.20580
  24. Brynjolfsson, E., and McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
  25. Brynjolfsson, E., and McAfee, A. (2017). The business of artificial intelligence: what it can - and cannot - do for your organization. Harvard Business Review.
  26. Burrell, J. (2016). How the machine 'thinks': Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 20539517 15622512.
  27. Burstrom, T., Parida, V., Lahti, T., and Wincent, J. (2021). AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research. Journal of Business Research, 127, 85-95. https://doi.org/10.1016/j.jbusres.2021.01.016
  28. Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., and Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243. https://doi.org/10.1016/j.bushor.2019.12.002
  29. Carayannis, E. G., Alexander, J., and Ioannidis, A. (2000). Leveraging knowledge, learning, and innovation in forming strategic government-university-industry (GUI) R&D partnerships in the US, Germany, and France. Technovation, 20(9), 477-488. https://doi.org/10.1016/S0166-4972(99)00162-5
  30. Carlsson, B., and Stankiewicz, R. (1991). On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1(2), 93-118. https://doi.org/10.1007/BF01224915
  31. Carneiro, G., Nascimento, J. C., and Freitas, A. (2011). The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Transactions on Image Processing, 21(3), 968-982. https://doi.org/10.1109/TIP.2011.2169273
  32. Coenen, L., and Lopez, F. J. D. (2010). Comparing systems approaches to innovation and technological change for sustainable and competitive economies: an explorative study into conceptual commonalities, differences and complementarities. Journal of Cleaner Production, 18(12), 1149-1160.
  33. Dai, Y., Xu, D., Maharjan, S., Qiao, G., and Zhang, Y. (2019). Artificial intelligence empowered edge computing and caching for internet of vehicles. IEEE Wireless Communications, 26(3), 12-18.
  34. Danneels, E. (2002). The dynamics of product innovation and firm competences. Strategic Management Journal, 23(12), 1095-1121. https://doi.org/10.1002/smj.275
  35. Davenport, T. H., and Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  36. Dellermann, D., Fliaster, A., and Kolloch, M. (2017). Innovation risk in digital business models: The German energy sector. Journal of Business Strategy, 38(5), 35-43. https://doi.org/10.1108/JBS-07-2016-0078
  37. Dilsizian, S. E., and Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current Cardiology Reports, 16(1), 441.
  38. Doz, Y. L., and Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2-3), 370-382. https://doi.org/10.1016/j.lrp.2009.07.006
  39. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., and Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296.
  40. Duan, Y., Edwards, J. S., and Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.
  41. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., and Galanos, V. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  42. Feng, Y., Zhu, Q., and Lai, K. H. (2017). Corporate social responsibility for supply chain management: A literature review and bibliometric analysis. Journal of Cleaner Production, 158, 296-307. https://doi.org/10.1016/j.jclepro.2017.05.018
  43. Fu, Y., Yang, H. H., Doan, K. N., Liu, C., Wang, X., and Quek, T. Q. (2020). Effective cache-enabled wireless networks: An artificial intelligence-and recommendation-oriented framework. IEEE Vehicular Technology Magazine.
  44. Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, 31(8-9), 1257-1274. https://doi.org/10.1016/S0048-7333(02)00062-8
  45. Gillespie, T. (2014). The relevance of algorithms. Media Technologies: Essays on Communication, Materiality, and Society, 167(2014), 167.
  46. Geng, D., Feng, Y., and Zhu, Q. (2020). Sustainable design for users: A literature review and bibliometric analysis. Environmental Science and Pollution Research, 27(24), 29824-29836. https://doi.org/10.1007/s11356-020-09283-1
  47. Haefner, N., Wincent, J., Parida, V., and Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda✰. Technological Forecasting and Social Change, 162, 120392.
  48. Haenlein, Michael, and Andreas Kaplan (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
  49. He, Z. L., and Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481-494. https://doi.org/10.1287/orsc.1040.0078
  50. Helfat, C. E., and Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting from innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391-1399. https://doi.org/10.1016/j.respol.2018.01.019
  51. Hilb, M. (2020). Toward artificial governance? The role of artificial intelligence in shaping the future of corporate governance. Journal of Management and Governance, 24(4), 851-870.
  52. Huang, M. H., and Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459
  53. Jacobides, M. G., Cennamo, C., and Gawer, A. (2018). Towards a theory of ecosystems. Strategic Management Journal, 39(8), 2255-2276.
  54. Jones, R. M., Laird, J. E., Nielsen, P. E., Coulter, K. J., Kenny, P., and Koss, F. V. (1999). Automated intelligent pilots for combat flight simulation. AI Magazine, 20(1), 27-27.
  55. Jordan, M. I., and Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415
  56. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
  57. Kak, S. C. (1996). Can we define levels of artificial intelligence?. Journal Of Intelligent Systems, 6(2), 133-144.
  58. Kakatkar, C., Bilgram, V., and Fuller, J. (2020). Innovation analytics: Leveraging artificial intelligence in the innovation process. Business Horizons, 63(2), 171-181.
  59. Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., and Sethi, A. (2017). A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Transactions on Medical Imaging, 36(7), 1550-1560. https://doi.org/10.1109/TMI.2017.2677499
  60. Kuo, T. C., Huang, S. H., and Zhang, H. C. (2001). Design for manufacture and design for 'X': Concepts, applications, and perspectives. Computers & Industrial Engineering, 41(3), 241-260. https://doi.org/10.1016/S0360-8352(01)00045-6
  61. Lan, L., Liu, Y., and Feng Lu, W. (2018). Learning from the past: uncovering design process models using an enriched process mining. Journal of Mechanical Design, 140(4), 041403. http://dx.doi.org/10.1115/1.4039200
  62. Lee, I., and Lee, K. (2015). The internet of things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), 431-440. https://doi.org/10.1016/j.bushor.2015.03.008
  63. Lee, J., Suh, T., Roy, D., and Baucus, M. (2019). Emerging technology and business model innovation: The case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
  64. Leslie, D. (2020). Tackling COVID-19 through responsible AI innovation: Five steps in the right direction. Harvard Data Science Review (Special Issue 1). https://doi.org/10.1162/99608f92.4bb9d7a7
  65. Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., and Zhang, H. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175-183. https://doi.org/10.1109/MWC.2017.1600304WC
  66. Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C., Baxter, S. L., Liu, G., ... and Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature Medicine, 25(3), 433-438. https://doi.org/10.1038/s41591-018-0335-9
  67. Lichtenthaler, U. (2018). Beyond artificial intelligence: Why companies need to go the extra step. Journal of Business Strategy, 41(1), 19-26. https://doi.org/10.1108/JBS-05-2018-0086
  68. Lu, H., Li, Y., Chen, M., Kim, H., and Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368-375. https://doi.org/10.1007/s11036-017-0932-8
  69. Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6, 1-29. https://doi.org/10.1080/23270012.2019.1570365
  70. March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71-87 https://doi.org/10.1287/orsc.2.1.71
  71. Markard, J., and Truffer, B. (2008). Technological innovation systems and the multi-level perspective: Towards an integrated framework. Research Policy, 37(4), 596-615. https://doi.org/10.1016/j.respol.2008.01.004
  72. Markard, J., Raven, R., and Truffer, B. (2012). Sustainability transitions: An emerging field of research and its prospects. Research Policy, 41(6), 955-967. https://doi.org/10.1016/j.respol.2012.02.013
  73. Massa, L., Tucci, C. L., and Afuah, A. (2017). A critical assessment of business model research. Academy of Management Annals, 11(1), 73-104. https://doi.org/10.5465/annals.2014.0072
  74. McCullagh, P., Matzkanin, K. T., Shaw, S. D., and Maldonado, M. (1993). Motivation for participation in physical activity: A comparison of parent-child perceived competencies and participation motives. Pediatric Exercise Science, 5(3), 224-233. https://doi.org/10.1123/pes.5.3.224
  75. McGrath, R. G. (2010). Business models: A discovery driven approach. Long Range Planning, 43(2-3), 247-261. https://doi.org/10.1016/j.lrp.2009.07.005
  76. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., and Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
  77. Minsky, M. (1982). Semantic information processing.
  78. Morris, M., Schindehutte, M., and Allen, J. (2005). The entrepreneur's business model: toward a unified perspective. Journal of Business Research, 58(6), 726-735. https://doi.org/10.1016/j.jbusres.2003.11.001
  79. Muhlroth, C., and Grottke, M. (2020). Artificial intelligence in innovation: How to spot emerging trends and technologies. IEEE Transactions on Engineering Management, 69(2), 493-510. https://doi.org/10.1109/TEM.2020.2989214
  80. Negro, S. O., Suurs, R. A., and Hekkert, M. P. (2008). The bumpy road of biomass gasification in the Netherlands: Explaining the rise and fall of an emerging innovation system. Technological Forecasting and Social Change, 75(1), 57-77. https://doi.org/10.1016/j.techfore.2006.08.006
  81. Osterwalder, A., Pigneur, Y., and Tucci, C. L. (2005). Clarifying business models: Origins, present, and future of the concept. Communications of the Association for Information Systems, 16(1), 1.
  82. Paschen, U., Pitt, C., and Kietzmann, J. (2020). Artificial intelligence: Building blocks and an innovation typology. Business Horizons, 63(2), 147-155. https://doi.org/10.1016/j.bushor.2019.10.004
  83. Prem, E. (2019). Artificial intelligence for innovation in Austria. Technology Innovation Management Review, 9(12), 5-15. http://doi.org/10.22215/timreview/1287
  84. Porter, M. E., and Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
  85. Raisch, S., Birkinshaw, J., Probst, G., and Tushman, M. L. (2009). Organizational ambidexterity: Balancing exploitation and exploration for sustained performance. Organization Science, 20(4), 685-695. https://doi.org/10.1287/orsc.1090.0428
  86. Rochet, J. C., and Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990-1029. https://doi.org/10.1162/154247603322493212
  87. Reim, W., Astrom, J., and Eriksson, O. (2020). Implementation of artificial intelligence (AI): A roadmap for business model innovation. AI, 1(2), 11.
  88. Sabatier, V., Mangematin, V., and Rousselle, T. (2010). From recipe to dinner: business model portfolios in the European biopharmaceutical industry. Long Range Planning, 43(2-3), 431-447. https://doi.org/10.1016/j.lrp.2010.02.001
  89. Serrano-Gotarredona, T., Valle, M., Conti, F., and Li, H. (2020). Introduction to the special issue on the 2nd IEEE international conference on artificial intelligence circuits and systems (AICAS 2020). IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(4), 403-405. https://doi.org/10.1109/JETCAS.2020.3040581
  90. Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., and Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14, 4-15. https://doi.org/10.1109/RBME.2020.2987975
  91. Sjodin, D., Parida, V., Palmie, M., and Wincent, J. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574-587.
  92. Tang, X., Li, X., Ding, Y., Song, M., and Bu, Y. (2020). The pace of artificial intelligence innovations: Speed, talent, and trial-and-error. Journal of Informetrics, 14(4), 101094.
  93. Teece, D. J., Pisano, G., and Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
  94. Teece, D. J. (2018a). Profiting from innovation in the digital economy: Enabling technologies, standards, and licensing models in the wireless world. Research Policy, 47(8), 1367-1387. https://doi.org/10.1016/j.respol.2017.01.015
  95. Teece, D. J. (2018b). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007
  96. Tweedale, J., Ichalkaranje, N., Sioutis, C., Jarvis, B., Consoli, A., and Phillips-Wren, G. (2007). Innovations in multi-agent systems. Journal of Network and Computer Applications, 30(3), 1089-1115. https://doi.org/10.1016/j.jnca.2006.04.005
  97. Vargo, S. L., and Lusch, R. F. (2016). Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), 5-23. https://doi.org/10.1007/s11747-015-0456-3
  98. Verganti, R., Vendraminelli, L., and Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
  99. Wang, Y., Zhang, N., and Zhao, X. (2020). Understanding the determinants in the different government AI adoption stages: Evidence of local government chatbots in China. Social Science Computer Review, 40(2), 534-554. https://doi.org/10.1177/0894439320980132
  100. Yablonsky, S. (2019). Multidimensional data-driven artificial intelligence innovation. Technology Innovation Management Review, 9(12), 16-28.
  101. Yams, N. B., Richardson, V., Shubina, G. E., Albrecht, S., and Gillblad, D. (2020). Integrated AI and innovation management: the beginning of a beautiful friendship. Technology Innovation Management Review, 10(11), 5-18. http://doi.org/10.22215/timreview/1399
  102. Yao, L., Zhang, J., Liu, J., Zhu, L., Ding, X., Chen, D., Wu, H., Lu, Z., Zhou, W., Zhang, L., Xu, B., Hu, S., Zheng, B, Yang, Y., and Yu, H. (2021). A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound. EBioMedicine, 65, 103238.
  103. Yigitcanlar, T., Kankanamge, N., Regona, M., Ruiz Maldonado, A., Rowan, B., Ryu, A., Desouza, K. C., Corchado, J. M., Mehmood, R., and Li, R. Y. M. (2020). Artificial intelligence technologies and related urban planning and development concepts: How are they perceived and utilized in Australia?. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 187.
  104. Wladawsky-Berger, I. (2019). The current state of ai adoption. Wall Street Journal, 08th Oct, 1.
  105. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., and Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738-1762. https://doi.org/10.1109/JPROC.2019.2918951