Engineering Micro-intelligence at the Edge of CPCS: Design Guidelines

  • Roberta CalegariEmail author
  • Giovanni Ciatto
  • Enrico Denti
  • Andrea Omicini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)


The Intelligent Edge computing paradigm is playing a major role in the design and development of Cyber-Physical and Cloud Systems (CPCS), extending the Cloud and overcoming its limitations so as to better address the issues related with the physical dimension of data—and therefore of the data-aware intelligence (such as context-awareness and real-time responses). Despite the proliferation of research works in this area, a well-founded software engineering approach specifically addressing the distribution of intelligence sources between the Edge and the Cloud is still missing. In this paper we propose some general criteria along with a coherent set of guidelines to follow in the design of distributed intelligence within CPCS, suitably exploiting Edge and Cloud paradigms to effectively enable data intelligence and accounting for both symbolic and sub-symbolic approaches to reasoning. Then, we exploit the notion of micro-intelligence as situated intelligence for Edge computing, promoting the idea of intelligent environment embodying rational processes meant to complement the cognitive process of individuals in order to reduce their cognitive workload and augment their cognitive capabilities. In order to demonstrate the general applicability of our guidelines, we propose Situated Logic Programming (SLP) as the conceptual framework for delivering micro-intelligence in CPCS, and Logic Programming as a Service (LPaaS) as its reference architecture and technological embodiment.


Design guidelines CPCS Micro-intelligence LPaaS Situated Logic Programming Edge intelligence 


  1. 1.
    Ananthanarayanan, G., et al.: Real-time video analytics: the killer app for edge computing. IEEE Comput. 50(10), 58–67 (2017). Scholar
  2. 2.
    Calegari, R.: Micro-intelligence for the IoT: logic-based models and technologies. Ph.D. thesis, Alma Mater Studiorum-Università di Bologna, Bologna, Italy (2018).
  3. 3.
    Calegari, R., Ciatto, G., Mariani, S., Denti, E., Omicini, A.: Logic programming in space-time: the case of situatedness in LPaaS. In: Cossentino, M., Sabatucci, L., Seidita, V. (eds.) 19th Workshop “From Objects to Agents” (WOA 2018), CEUR Workshop Proceedings, vol. 2215, pp. 63–68. Sun SITE Central Europe, RWTH Aachen University, June 2018.
  4. 4.
    Calegari, R., Ciatto, G., Mariani, S., Denti, E., Omicini, A.: LPaaS as micro-intelligence: enhancing IoT with symbolic reasoning. Big Data Cogn. Comput. 2(3) (2018). Scholar
  5. 5.
    Calegari, R., Denti, E., Mariani, S., Omicini, A.: Logic programming as a service. Theor. Pract. Logic Program. 18(3–4), 1–28 (2018). Scholar
  6. 6.
    Calegari, R., Denti, E., Mariani, S., Omicini, A.: Logic programming as a service in multi-agent systems for the Internet of Things. Int. J. Grid Util. Comput. 10(4), 344–360 (2019). Scholar
  7. 7.
    Chen, M., Li, W., Fortino, G., Hao, Y., Hu, L., Humar, I.: A dynamic service migration mechanism in edge cognitive computing. ACM Trans. Internet Technol. 19(2) (2019). Scholar
  8. 8.
    Cicirelli, F., Guerrieri, A., Mercuri, A., Spezzano, G., Vinci, A.: ITEMa: a methodological approach for cognitive edge computing IoT ecosystems. Future Gener. Comput. Syst. 92, 189–197 (2019). Scholar
  9. 9.
    Clark, A., Chalmers, D.J.: The extended mind. Analysis 58(1), 7–19 (1998). Scholar
  10. 10.
    Erl, T.: Service-Oriented Architecture: Concepts, Technology, and Design. Prentice Hall/Pearson Education International, Upper Saddle River (2005).
  11. 11.
    Esteva, M., de la Cruz, D.D.L., Rosell, B., Arcos, J.L.A., Rodríguez-Aguilar, J.A., Cuní, G.: Engineering open multi-agent systems as electronic institutions. In: 19th National Conference on Artifical Intelligence (AAAI 2004), pp. 1010–1011. AAAI Press (2004).
  12. 12.
    Hollan, J., Hutchins, E., Kirsh, D.: Distributed cognition: toward a new foundation for human-computer interaction research. ACM Trans. Comput.-Hum. Interact. 7(2), 174–196 (2000). Scholar
  13. 13.
    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). Scholar
  14. 14.
    Logic Programming as a Service (LPaaS) (2018).
  15. 15.
    Mariani, S., Omicini, A.: TuCSoN on cloud: an event-driven architecture for embodied/disembodied coordination. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds.) ICA3PP 2013. LNCS, vol. 8286, pp. 285–294. Springer, Cham (2013). Scholar
  16. 16.
    Omicini, A., Calegari, R.: Injecting (micro)intelligence in the IoT: logic-based approaches for (M)MAS. In: Lin, D., Ishida, T., Zambonelli, F., Noda, I. (eds.) MMAS 2018. LNCS (LNAI), vol. 11422, pp. 21–35. Springer, Cham (2019). Scholar
  17. 17.
    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. Informatics 15(1), 481–489 (2019). Scholar
  18. 18.
    Rauch, E., Linder, C., Dallasega, P.: Anthropocentric perspective of production before and within industry 4.0. Comput. Ind. Eng. (In press).
  19. 19.
    Rosenberg, D., Boehm, B., Wang, B., Qi, K.: Rapid, evolutionary, reliable, scalable system and software development: the resilient agile process. In: 2017 International Conference on Software and System Process (ICSSP 2017), pp. 60–69. ACM (2017).
  20. 20.
    Tang, B., Chen, Z., Hefferman, G., Pei, S., Wei, T., He, H., Yang, Q.: Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Trans. Ind. Inf. 13(5), 2140–2150 (2017). Scholar
  21. 21.
    Um, J.-S.: Futurology and future prospect of drone CPS. Drones as Cyber-Physical Systems, pp. 257–274. Springer, Singapore (2019). Scholar
  22. 22.
    Waschull, S., Bokhorst, J., Molleman, E., Wortmann, J.: Work design in future industrial production: transforming towards cyber-physical systems. Comput. Ind. Eng. (In press).

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dipartimento di Informatica – Scienza e Ingegneria (DISI)Alma Mater Studiorum–Università di BolognaBolognaItaly
  2. 2.Dipartimento di Informatica – Scienza e Ingegneria (DISI)Alma Mater Studiorum–Università di BolognaCesenaItaly

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