Advertisement

Argumentation-Based Coordination in IoT: A Speaking Objects Proof-of-Concept

  • Stefano MarianiEmail author
  • Andrea Bicego
  • Marco Lippi
  • Marco Mamei
  • Franco Zambonelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11874)

Abstract

Coordination of Cyberphysical Systems is an increasingly relevant concern for distributed systems engineering, mostly due to the rise of the Internet of Things vision in many application domains. Against this background, Speaking Objects has been proposed as a vision of future smart objects coordinating their collective perception and action through argumentation. Along this line, in this paper we describe a Proof-of-Concept implementation of the Speaking Objects vision in a smart home deployment.

Keywords

Speaking Objects Internet of Things Argumentation-based Coordination 

References

  1. 1.
    Agrawal, H., Leigh, S.W., Maes, P.: L’evolved: autonomous and ubiquitous utilities as smart agents. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 487–491. ACM, New York (2015)Google Scholar
  2. 2.
    Alcarria, R., Robles, T., Morales, A., Cedeño, E.: Resolving coordination challenges in distributed mobile service executions. Int. J. Web Grid Serv. 10(2/3), 168–191 (2014).  https://doi.org/10.1504/IJWGS.2014.060251CrossRefGoogle Scholar
  3. 3.
    Amgoud, L., Parsons, S.: Agent dialogues with conflicting preferences. In: Meyer, J.-J.C., Tambe, M. (eds.) ATAL 2001. LNCS (LNAI), vol. 2333, pp. 190–205. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45448-9_14CrossRefGoogle Scholar
  4. 4.
    Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010).  https://doi.org/10.1016/j.comnet.2010.05.010CrossRefzbMATHGoogle Scholar
  5. 5.
    Bourzac, K.: Millimeter-scale computers: now with deep-learning neural networks on board, February 2017. https://goo.gl/sciVTC
  6. 6.
    Cano, J., Rutten, E., Delaval, G., Benazzouz, Y., Gurgen, L.: ECA rules for IoT environment: a case study in safe design. In: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops, pp. 116–121, September 2014.  https://doi.org/10.1109/SASOW.2014.32
  7. 7.
    Cheng, B., Zhu, D., Zhao, S., Chen, J.: Situation-aware IoT service coordination using the event-driven SOA paradigm. IEEE Trans. Netw. Serv. Manag. 13(2), 349–361 (2016).  https://doi.org/10.1109/TNSM.2016.2541171CrossRefGoogle Scholar
  8. 8.
    Conti, M., et al.: Looking ahead in pervasive computing: challenges and opportunities in the era of cyber-physical convergence. Pervasive Mobile Comput. 8(1), 2–21 (2012)CrossRefGoogle Scholar
  9. 9.
    Endler, M., Briot, J.P., Silva, F.S.E., De Almeida, V.P., Haeusler, E.H.: An approach for real-time stream reasoning for the Internet of Things. In: 1st International Workshop on Semantic Multimedia Computing (SMC 2017), Proceedings of the 11th IEEE International Conference on Semantic Computing (ICSC 2017), pp. 348–353. IEEE, San Diego, January 2017Google Scholar
  10. 10.
    Fortino, G., Russo, W., Savaglio, C., Shen, W., Zhou, M.: Agent-oriented cooperative smart objects: from IoT system design to implementation. IEEE Trans. Syst. Man Cybern.: Syst. 48(11), 1939–1956 (2018).  https://doi.org/10.1109/TSMC.2017.2780618CrossRefGoogle Scholar
  11. 11.
    Fortino, G., Guerrieri, A., Lacopo, M., Lucia, M., Russo, W.: An agent-based middleware for cooperating smart objects. In: Corchado, J.M., et al. (eds.) PAAMS 2013. CCIS, vol. 365, pp. 387–398. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-38061-7_36CrossRefGoogle Scholar
  12. 12.
    Goumopoulos, C., Kameas, A.: Smart objects as components of UbiComp applications. Int. J. Multimedia Ubiquitous Eng. 4(3), 1–20 (2009)Google Scholar
  13. 13.
    Gupta, C., et al.: ProtoNN: compressed and accurate kNN for resource-scarce devices. In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017), vol. 70, pp. 1331–1340. JMLR.org (2017). http://dl.acm.org/citation.cfm?id=3305381.3305519
  14. 14.
    Kortuem, G., Kawsar, F., Sundramoorthy, V., Fitton, D.: Smart objects as building blocks for the Internet of Things. IEEE Internet Comput. 14(1), 44–51 (2010)CrossRefGoogle Scholar
  15. 15.
    Kumar, A., Goyal, S., Varma, M.: Resource-efficient machine learning in 2 KB RAM for the Internet of Things. In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017), vol. 70, pp. 1935–1944. JMLR.org (2017). http://dl.acm.org/citation.cfm?id=3305381.3305581
  16. 16.
    Lippi, M., Mamei, M., Mariani, S., Zambonelli, F.: Coordinating distributed speaking objects. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 1949–1960, June 2017.  https://doi.org/10.1109/ICDCS.2017.282
  17. 17.
    Lippi, M., Mamei, M., Mariani, S., Zambonelli, F.: An argumentation-based perspective over the social IoT. IEEE IoT J. 5(4), 2537–2547 (2018).  https://doi.org/10.1109/JIOT.2017.2775047CrossRefGoogle Scholar
  18. 18.
    Neto, A.R., et al.: Classifying smart IoT devices for running machine learning algorithms. In: Anais do XLV Seminàrio Integrado de Software e Hardware. SBC, Porto Alegre (2018). https://sol.sbc.org.br/index.php/semish/article/view/3429
  19. 19.
    Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016).  https://doi.org/10.1109/MC.2016.145CrossRefGoogle Scholar
  20. 20.
    Walton, D., Krabbe, E.: Commitment in Dialogue: Basic Concept of Interpersonal Reasoning. State University of New York Press, Albany (1995)Google Scholar
  21. 21.
    Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data (Mobidata 2015), pp. 37–42. ACM, New York (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DISMI – Università di Modena e Reggio EmiliaReggio EmiliaItaly
  2. 2.Apex SrlModenaItaly

Personalised recommendations