NOA-AID: Network Overlays for Adaptive Information Aggregation, Indexing and Discovery at the Edge

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10659)


This paper presents NOA-AID a network architecture for targeting highly distributed systems, composed of a large set of distributed stream processing devices, aimed at adaptive information indexing, aggregation and discovery in streams of data. The architecture is organized on two layers. The upper layer is aimed at supporting the information discovery process by providing a distributed index structure. The lower layer is mainly devoted to resource aggregation based on epidemic protocols targeting highly distributed and dynamic scenarios, well suited to stream-oriented scenarios. We present a theoretical study on the costs of information management operations, also giving an empirical validation of such findings. Finally, we presented an experimental evaluation of the ability of our solution to be effective and efficient in retrieving meaningful information in streams on a highly-dynamic and distributed scenario.


IoT Stream Adaptivity Network overlay Information aggregation in streams Distributed indexing 


  1. 1.
    Cai, M., Frank, M., Chen, J., Szekely, P.: MAAN: a multi-attribute addressable network for grid information services. J. Grid Comput. 2(1), 3–14 (2004)CrossRefzbMATHGoogle Scholar
  2. 2.
    Chang, R.S., Hu, M.S.: A resource discovery tree using bitmap for grids. Future Gener. Comput. Syst. 26, 29–37 (2010)CrossRefGoogle Scholar
  3. 3.
    Marzolla, M., Mordacchini, M., Orlando, S.: A P2P resource discovery system based on a forest of trees. In: 17th International Workshop on Database and Expert Systems Applications (DEXA 2006), pp. 261–265 (2006)Google Scholar
  4. 4.
    Mordacchini, M., Ricci, L., Ferrucci, L., Albano, M., Baraglia, R.: Hivory: range queries on hierarchical voronoi overlays. In: IEEE Tenth International Conference on Peer-to-Peer Computing (P2P2010). IEEE, pp. 1–10 (2010)Google Scholar
  5. 5.
    Gennaro, C., Mordacchini, M., Orlando, S., Rabitti, F.: Mroute: a peer-to-peer routing index for similarity search in metric spaces. In: Proceedings of the 5th International Workshop on Databases, Information Systems and Peer-to-Peer Computing (DBISP2P 2007), pp. 1–12 (2007)Google Scholar
  6. 6.
    Bruno, R., Conti, M., Mordacchini, M., Passarella, A.: An analytical model for content dissemination in opportunistic networks using cognitive heuristics. In: Proceedings of the 15th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. ACM, pp. 61–68 (2012)Google Scholar
  7. 7.
    Liu, L., Antonopoulos, N., Mackin, S., Xu, J., Russell, D.: Efficient resource discovery in self-organized unstructured peer-to-peer networks. Concurr. Comput. Pract. Exp. 21, 159–183 (2009)CrossRefGoogle Scholar
  8. 8.
    Ruffo, G., Schifanella, R.: A peer-to-peer recommender system based on spontaneous affinities. ACM Trans. Internet Technol. 9, 4:1–4:34 (2009)CrossRefGoogle Scholar
  9. 9.
    Baraglia, R., Dazzi, P., Guidi, B., Ricci, L.: Godel: Delaunay overlays in P2P networks via gossip. In: IEEE 12th International Conference on Peer-to-Peer Computing (P2P). IEEE, pp. 1–12 (2012)Google Scholar
  10. 10.
    Mordacchini, M., Dazzi, P., Tolomei, G., Baraglia, R., Silvestri, F., Orlando, S.: Challenges in designing an interest-based distributed aggregation of users in P2P systems. In: 2009 International Conference on Ultra Modern Telecommunications & Workshops, ICUMT 2009. IEEE, pp. 1–8 (2009)Google Scholar
  11. 11.
    Danelutto, M., Dazzi, P., et al.: A Java/Jini framework supporting stream parallel computations. In: PARCO, pp. 681–688 (2005)Google Scholar
  12. 12.
    Lulli, A., Ricci, L., Carlini, E., Dazzi, P., Lucchese, C.: Cracker: crumbling large graphs into connected components. In: 2015 IEEE Symposium on Computers and Communication (ISCC). IEEE, pp. 574–581 (2015)Google Scholar
  13. 13.
    Falchi, F., Gennaro, C., Zezula, P.: Nearest neighbor search in metric spaces through content-addressable networks. Inf. Process. Manag. 43(3), 665–683 (2007)CrossRefGoogle Scholar
  14. 14.
    Pirrò, G., Talia, D., Trunfio, P.: A DHT-based semantic overlay network for service discovery. Future Gener. Comput. Syst. 28(4), 689–707 (2012)CrossRefGoogle Scholar
  15. 15.
    Guerraoui, R., Sidath, B., Kermarrec, A., Fessant, F.L., Huguenin, K., Rivière, E.: GosSkip, an efficient, fault-tolerant and self organizing overlay using gossip-based construction and skip-lists principles. In: Sixth IEEE International Conference on Peer-to Peer Computing, 2006 Ratnasamy, pp. 12–22 (2001)Google Scholar
  16. 16.
    Crespo, A., Garcia-Molina, H.: Semantic overlay networks for P2P systems. In: Moro, G., Bergamaschi, S., Aberer, K. (eds.) AP2PC 2004. LNCS (LNAI), vol. 3601, pp. 1–13. Springer, Heidelberg (2005). CrossRefGoogle Scholar
  17. 17.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (2005)Google Scholar
  18. 18.
    Zhu, Y., Hu, Y.: Efficient semantic search on DHT overlays. J. Parallel Distrib. Comput. 67(5), 604–616 (2007)CrossRefzbMATHGoogle Scholar
  19. 19.
    Baraglia, R., Dazzi, P., Mordacchini, M., Ricci, L.: A peer-to-peer recommender system for self-emerging user communities based on gossip overlays. J. Comput. Syst. Sci. 79(2), 291–308 (2013)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Baraglia, R., Dazzi, P., Mordacchini, M., Ricci, L., Alessi, L.: GROUP: a gossip based building community protocol. In: Balandin, S., Koucheryavy, Y., Hu, H. (eds.) NEW2AN/ruSMART -2011. LNCS, vol. 6869, pp. 496–507. Springer, Heidelberg (2011). CrossRefGoogle Scholar
  21. 21.
    Carlini, E., Dazzi, P., Mordacchini, M., Ricci, L.: Toward community-driven interest management for distributed virtual environment. In: an Mey, D., et al. (eds.) Euro-Par 2013. LNCS, vol. 8374, pp. 363–373. Springer, Heidelberg (2014). CrossRefGoogle Scholar
  22. 22.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the International Confernece on Very Large Data Bases, pp. 518–529 (1999)Google Scholar
  23. 23.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)CrossRefGoogle Scholar
  24. 24.
    Bentivogli, L., Forner, P., Magnini, B., Pianta, E.: Revising wordnet domains hierarchy: semantics, coverage, and balancing. In: Proceedings of COLING 2004 Workshop on Multilingual Linguistic Resources, pp. 101–108 (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.ISTI–CNRPisaItaly
  2. 2.IIT–CNRPisaItaly

Personalised recommendations