Skip to main content
SpringerLink
Log in

A Deep Learning Model for Data Synopses Management in Pervasive Computing Applications

  • Conference paper
  • First Online:
Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 284))

Abstract

Pervasive computing involves the placement of processing units and services close to end users to support intelligent applications that will facilitate their activities. With the advent of the Internet of Things (IoT) and the Edge Computing (EC), one can find more room for placing services at various points in the interconnection of the aforementioned infrastructures. Of significant importance is the processing of the collected data to provide analytics and knowledge. Such processing can be realized upon the EC nodes that exhibit increased computational capabilities compared to IoT devices. An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models towards the provision of the desired analytics. Nodes become the hosts of geo-distributed datasets formulated by the reports of IoT devices. Upon the datasets, a number of queries/tasks can be executed either locally or remotely. Queries/tasks can be offloaded for performance reasons to deliver the most appropriate response. However, an offloading action should be carefully designed being always aligned with the data present to the hosting node. In this paper, we present a model to support the cooperative aspect in the EC infrastructure. We argue on the delivery of data synopses distributed in the ecosystem of EC nodes making them capable to take offloading decisions fully aligned with data present at every peer. Nodes exchange their data synopses to inform their peers. We propose a scheme that detects the appropriate time to distribute the calculated synopsis trying to avoid the network overloading especially when synopses are frequently extracted due to the high rates at which IoT devices report data to EC nodes. Our approach involves a deep learning model for learning the distribution of calculated synopses and estimate future trends. Upon these trends, we are able to find the appropriate time to deliver synopses to peer nodes. We provide the description of the proposed mechanism and evaluate it based on real datasets. An extensive experimentation upon various scenarios reveals the pros and cons of the approach by giving numerical results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aggarwal, C., Han, J., Wang, J., Yu, P.: A framework for clustering evolving data streams. In: VLDB Conference, pp. 81–92 (2003)

    Google Scholar 

  2. Aggarwal, C., Han, J., Wang, J., Yu, P.: On-demand classification of data streams. In: ACM KDD Conference, pp. 503–508 (2004)

    Google Scholar 

  3. Aggarwal, C., Yu, P.: A survey of synopsis construction in data streams. In: Aggarwal, C. (ed.) Data Streams, Models and Algorithms. Springer, Heidelberg (2007)

    Google Scholar 

  4. Alon, N., Gibbons, P., Matias, Y., Szegedy, M.: Tracking joins and self joins in limited storage. In: ACM PODS Conference, pp. 10–20 (1999)

    Google Scholar 

  5. Amrutha, S., et al.: Data dissemination framework for IoT based applications. Indian J. Sci. Technol. 9(48), 1–5 (2016)

    Article  Google Scholar 

  6. Anagnostopoulos, C., Kolomvatsos, K.: An intelligent, time-optimized monitoring scheme for edge nodes. J. Netw. Comput. Appl. 148 (2019). https://doi.org/10.1016/j.jnca.2019.102458

  7. Anglano, C., Canonico, M., Guazzone, M.: Profit-aware resource management for edge computing systems. In: 1st International Workshop on Edge Systems, Analytics and Networking, pp. 25–30 (2018)

    Google Scholar 

  8. Babcock, B., Datar, M., Motwani, R.: Load shedding techniques for data stream systems. In: Workshop on Management and Processing of Data Streams (2003)

    Google Scholar 

  9. Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: PODS, pp. 1–16 (2002)

    Google Scholar 

  10. Bellavista, P., Corradi, A., Foschini, L., Scotece, D.: Differentiated service/data migration for edge services leveraging container characteristics. IEEE Access 7 (2019)

    Google Scholar 

  11. Bhardwaj, K., Agrawal, P., Gavrilovska, A., Schwan, K.: AppSachet: distributed app delivery from the edge cloud. In: 7th International Conference Mobile Computing, Applications, and Services, pp. 89–106 (2015)

    Google Scholar 

  12. Chakrabarti, K., Garofalakis, M., Rastogi, R., Shim, K.: Approximate query processing with wavelets. VLDB J. 10(2–3), 199–223 (2001)

    Article  Google Scholar 

  13. Charikar, M., Chen, K., Farach-Colton, M.: Finding frequent items in data streams. In: ICALP (2002)

    Google Scholar 

  14. Cherrueau, R.A., Lebre, A., Pertin, D., Wuhib, F., Soares, J.: Edge computing resource management system: a critical building block!. In: USENIX Workshop on Hot Topics in Edge Computing, Initiating the debate via OpenStack, pp. 1–6 (2018)

    Google Scholar 

  15. Chu, D., Deshpande, A., Hellerstein, J., Hong, W.: Approximate data collection in sensor networks using probabilistic models. In: 22nd International Conference on Data Engineering (ICDE 06) (2006)

    Google Scholar 

  16. Cormode, G., Muthukrishnan, S.: What’s hot and what’s not: tracking most frequent items dynamically. In: ACM PODS Conference (2005). https://doi.org/10.1145/1061318.1061325

  17. Dobra, A., Garofalakis, M.N., Gehrke, J., Rastogi, R.: Sketch-based multi-query processing over data streams. In: EDBT Conference (2004). https://doi.org/10.1007/978-3-540-24741-8_32

  18. Gehrke, J., Korn, F., Srivastava, D.: On computing correlated aggregates over continual data streams. In: SIGMOD Conference (2001). https://doi.org/10.1145/375663.375665

  19. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  20. Hagras, H.: A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE TFS 12 (2004). https://doi.org/10.1109/TFUZZ.2004.832538

  21. Karanika, A., Oikonomou, P., Kolomvatsos, K., Loukopoulos, T.: A demand-driven, proactive tasks management model at the edge. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2020)

    Google Scholar 

  22. Kolomvatsos, K.: A proactive uncertainty driven model for data synopses management in pervasive applications. In: 6th IEEE International Conference on Data Science and Systems (DSS), Fiji, 14–16 December (2020)

    Google Scholar 

  23. Kolomvatsos, K.: An intelligent scheme for assigning queries. Appl. Intell. 48(9), 2730–2745 (2017). https://doi.org/10.1007/s10489-017-1099-5

    Article  Google Scholar 

  24. Kolomvatsos, K.: A distributed, proactive intelligent scheme for securing quality in large scale data processing. Computing 101, 1687–1710 (2019)

    Article  Google Scholar 

  25. Kolomvatsos, K., Anagnostopoulos, C.: An intelligent edge-centric queries allocation scheme based on ensemble models. ACM Trans. Internet Technol. (2020). https://doi.org/10.1145/3417297

  26. Kolomvatsos, K., Anagnostopoulos, C.: A probabilistic model for assigning queries at the edge. Computing 102, 865–892 (2020)

    Article  MathSciNet  Google Scholar 

  27. Kolomvatsos, K., Anagnostopoulos, C.: Multi-criteria optimal task allocation at the edge. Futur. Gener. Comput. Syst. 93, 358–372 (2019)

    Article  Google Scholar 

  28. Kolomvatsos, K., Anagnostopoulos, C., Hadjiefthymiades, S.: Data fusion & type-2 fuzzy inference in contextual data stream monitoring. IEEE Trans. Syst. Man Cybern.: Syst. PP(99), 1–15 (2016)

    Google Scholar 

  29. Kolomvatsos, K., Anagnostopoulos, C., Koziri, M., Loukopoulos, T.: Proactive & Time-Optimized Data Synopsis Management at the Edge. IEEE Trans. Knowl. Data Eng. (IEEE TKDE) (2020). https://doi.org/10.1109/TKDE.2020.3021377

  30. Kolomvatsos, K., Anagnostopoulos, C., Marnerides, A., Ni, Q., Hadjiefthymiades, S., Pezaros, D.: Uncertainty-driven ensemble forecasting of QoS in software defined networks. In: 22nd IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece (2017)

    Google Scholar 

  31. Lakshmi, K.P., Reddy, C.R.K.: A survey on different trends in data streams. In: IEEE International Conference on Networking and Information Technology (2010). https://doi.org/10.1109/ICNIT.2010.5508473

  32. Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: VLDB Conference (2002)

    Google Scholar 

  33. Martin, R., Vahdat, A., Culler, D., Anderson, T.: Effects of communication latency, overhead, and bandwidth in a cluster architecture. In: 4th Annual International Symposium on Computer Architecture (1997). https://doi.org/10.1145/384286.264146

  34. Mendel, J.M.: Type-2 fuzzy sets and systems: an overview. IEEE Comput. Intell. Mag. 2(1) (2007). https://doi.org/10.1109/MCI.2007.380672

  35. Mendel, J.M.: Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Prentice-Hall, Upper Saddle River (2001)

    MATH  Google Scholar 

  36. Mesiar, R., Kolesarova, A., Calvo, T., Komornikova, M.: A review of aggregation functions. Studies in Fuzziness and Soft Computing (2008). https://doi.org/10.1007/978-3-540-73723-0_7

  37. Muthukrishnan, S.: Data streams: algorithms and applications. In: 14th Annual ACM-SIAM Symposium on Discrete Algorithms (2003)

    Google Scholar 

  38. Najam, S., Gilani, S., Ahmed, E., Yaqoob, I., Imran, M.: The role of edge computing in Internet of Things. IEEE Commun. Mag. (2018). https://doi.org/10.1109/MCOM.2018.1700906

    Article  Google Scholar 

  39. Novák, V., Perfilieva, I., Močkoř, J.: Mathematical Principles of Fuzzy Logic. Kluwer Academic, Dordrecht (1999)

    Book  Google Scholar 

  40. Sardellitti, S., Scutari, G., Barbarossa, S.: Joint optimisation of radio and computational resources for multicell mobile-edge computing. IEEE Trans. Signal Inf. Process. Netw. 1(2), 89–103 (2015)

    Google Scholar 

  41. Savolainen, P., et al.: Spaceify: a client-edge-server ecosystem for mobile computing in smart spaces. In: International Conference on Mobile Computing & Networking, pp. 211–214 (2013)

    Google Scholar 

  42. Schweller, R., Gupta, A., Parsons, E., Chen, Y.: Reversible sketches for efficient and accurate change detection over network data streams. In: Internet Measurement Conference Proceedings, pp. 207–212 (2004)

    Google Scholar 

  43. Shekhar, S., Gokhale, A.: Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (2017)

    Google Scholar 

  44. Simoens, P., Xiao, Y., Pillai, P., Chen, Z., Ha, K., Satyanarayanan, M.: Scalable crowd-sourcing of video from mobile devices. In: 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 139–152 (2013)

    Google Scholar 

  45. Tatbul, N., Zdonik, S.: A subset-based load shedding approach for aggregation queries over data streams. In: 32nd International Conference on Very Large Data Bases, Seoul, Korea (2006)

    Google Scholar 

  46. Vandeput, N.: Data Science for Supply Chain Forecast (2018). Independently Published

    Google Scholar 

  47. Wang, N., Varghese, B., Matthaiou, M., Nikolopoulos, D.: ENORM: a framework for edge node resource management. IEEE Trans. Serv. Comput. (2017). https://doi.org/10.1109/TSC.2017.2753775

  48. Yao, Y., Cao, Q., Vasilakos, A.V.: EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In: IEEE International Conference on Mobile Ad-Hoc and Sensor Systems, pp. 182–190 (2013)

    Google Scholar 

  49. Zhou, A., Wang, S., Li, J., Sun, Q., Yang, F.: Optimal mobile device selection for mobile cloud service providing. J. Supercomput. 72(8), 3222–3235 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Telecommunications, University of Thessaly, Papasiopoulou 2-4, 35131, Lamia, Greece

    Panagiotis Fountas & Kostas Kolomvatsos

  2. School of Computing Science, University of Glasgow, Lilybank Gardens 17, Glasgow, G12 8RZ, UK

    Christos Anagnostopoulos

Authors
  1. Panagiotis Fountas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kostas Kolomvatsos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christos Anagnostopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Panagiotis Fountas .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fountas, P., Kolomvatsos, K., Anagnostopoulos, C. (2021). A Deep Learning Model for Data Synopses Management in Pervasive Computing Applications. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_44

Download citation

Publish with us

Policies and ethics

Search

Navigation