Skip to main content
SpringerLink
Log in
Book cover

New Advances in the Internet of Things pp 25–44Cite as

Convey Intelligence to Edge Aggregation Analytics

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 715))

Abstract

In Internet of Things (IoT) environments, networks of sensors, actuators, and computing devices are responsible to locally process contextual data, reason and collaboratively support aggregation analytics tasks. We rest on the edge computing paradigm where pushing processing and inference to the edge of the IoT network allows the complexity of analytics to be distributed into many smaller and more manageable pieces and to be physically located at the source of the contextual information it needs to work on. This enables a huge amount of rich contextual data to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized cloud/back-end processing system. We propose a lightweight, distributed, predictive intelligence mechanism that supports communication efficient aggregation analytics within the edge network. Our idea is based on the capability of the edge nodes to perform sensing and locally determine (through prediction) whether to disseminate contextual data in the edge network or to locally re-construct undelivered contextual data in light of minimizing the required communication interaction at the expense of accurate analytics tasks. Based on this decision making, we eliminate data transfer at the edge of the network, thus saving network resources for sensing and receiving data, by exploiting the nature of the captured contextual data. We provide comprehensive experimental evaluation of the proposed mechanism over a real contextual dataset and show the benefits stemmed from its adoption in edge computing environments.

This is a preview of subscription content, 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   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   139.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Notes

  1. 1.

    Double exponential smoothing (Holt-Winters time series smoothing) could be adopted dealing with the same computational complexity.

References

  1. M. Satyanarayanan et al., Edge Analytics in the Internet of Things, in IEEE Pervasive Computing, vol. 14, no. 2. pp. 24–31, Apr-June 2015

    Google Scholar 

  2. The mobile-edge computing initiative, http://www.etsi.org/technologies-clusters/technologies/mobile-edge-computing

  3. I. Stojmenovic, S. Wen, The Fog computing paradigm: scenarios and security issues, in 2014 Federated Conference on Computer Science and Information Systems, Warsaw, 2014, pp. 1–8

    Google Scholar 

  4. S. Yi, C. Li, Q. Li, A survey of fog computing: concepts, applications and issues, in Proceedings of the 2015 Workshop on Mobile Big Data, 2015, pp. 37–42

    Google Scholar 

  5. A. Vulimiri, C. Curino, P.B. Godfrey, T. Jungblut et al., WANalytics: geo-distributed analytics for a data intensive world, in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015, pp. 1087–1092

    Google Scholar 

  6. B. Cheng, A. Papageorgiou, M. Bauer, Geelytics: enabling on-demand edge analytics over scoped data sources, in IEEE International Congress on Big Data (BigData Congress). San Francisco, CA, vol. 2016, pp. 101–108, 2016

    Google Scholar 

  7. R. Ganti, F. Ye, H. Lei, Mobile crowdsensing: current state and future challenges. Commun. Mag. IEEE 49(11), 32–39 (2011)

    Article  Google Scholar 

  8. N. Lane, E. Miluzzo, H. Lu, D. Peebles, T. Choudhury, A. Campbell, A survey of mobile phone sensing. Commun. Mag. IEEE 48(9), 140–150 (2010)

    Article  Google Scholar 

  9. L.M. Oliveira, J.J. Rodrigues, Wireless sensor networks: a survey on environmental monitoring. J. Commun. 6(2), 143–151 (2011)

    Article  Google Scholar 

  10. J. Kang et al. High-fidelity environmental monitoring using wireless sensor networks, Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys ’13) (USA, Article 67, 2013)

    Google Scholar 

  11. A. Awang et al. RIMBAMON: a forest monitoring system using wireless sensor networks, in ICIAS 2007, pp. 1101–1106, 2007

    Google Scholar 

  12. E. Zervas et al., Multisensor data fusion for fire detection. Inf Fusion Elsevier 12(3), 1566–2535 (2011)

    Google Scholar 

  13. C. Anagnostopoulos, S. Hadjiefthymiades, K. Kolomvatsos, Accurate, dynamic, and distributed localization of phenomena for mobile sensor networks. ACM Trans. Sen. Netw. 12, 2, Article 9 (April 2016), 59 pages (2016)

    Google Scholar 

  14. S. Nittel, A Survey of geosensor networks: advances in dynamic environmental monitoring. Sensors 9, 5664–5678 (2009)

    Article  Google Scholar 

  15. Yu. Pieter Simoens, P. Pillai Xiao, Z. Chen, K. Ha, M. Satyanarayanan, Scalable crowd-sourcing of video from mobile devices, in Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys ’13) (ACM, New York, NY, USA, 2013), pp. 139–152

    Google Scholar 

  16. G. Xu et al., Applications of wireless sensor networks in marine environment monitoring: a survey. Sensors 14(9), 16932–16954 (2014)

    Article  Google Scholar 

  17. G.W. Eidson et al., The south carolina digital Watershed: end-to-end support for realtime management of water resources, in Proceedings of the 4th International Symposium on Innovations and Real-time Applications of Distributed Sensor Networks (IRADSN 09), vol. 2010 (USA, May 2009)

    Google Scholar 

  18. N. Nguyen et al. A real-time control using wireless sensor network for intelligent energy management system in buildings, in Proceedings of the IEEE Worskshop on Environmental Energy and Structural Monitoring Systems (EESMS 10), pp. 87–92, Sept 2010

    Google Scholar 

  19. K. Kolomvatsos; C. Anagnostopoulos; S. Hadjiefthymiades, Data fusion and type-2 fuzzy inference in contextual data stream monitoring, in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. PP, no. 99, pp. 1–15, June 2016

    Google Scholar 

  20. S.M. McConnell, D.B. Skillicorn, A distributed approach for prediction in sensor networks, in Proceedings of the SIAM International Conference on Data Mining Workshop Sensor Networks, 2005

    Google Scholar 

  21. D. Tulone, S. Madden, An energy-efficient querying framework in sensor networks for detecting node similarities, in Proceedings of the IEEE/ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWiM), 2006

    Google Scholar 

  22. S. Goel, T. Imielinski, Prediction-based monitoring in sensor networks: taking lessons from MPEG. ACM SIGCOMM Comput. Commun. Rev. 31(5), 82–98 (2001)

    Article  Google Scholar 

  23. A. Simonetto, G. Leus, Distributed maximum likelihood sensor network localization, in IEEE Transactions on Signal Processing, vol. 62, no. 6, pp. 1424–1437, 15 Mar 2014

    Google Scholar 

  24. G. Kejela, R.M. Esteves, C. Rong, Predictive Analytics of Sensor Data Using Distributed Machine Learning Techniques, pp. 626–631, 2014

    Google Scholar 

  25. R. Gemulla, E. Nijkamp, P.J. Haas, Y. Sismanis, Large-scale matrix factorization with distributed stochastic gradient descent, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’11) (ACM, New York, NY, USA, 2011), pp. 69–77

    Google Scholar 

  26. C. Anagnostopoulos, S. Hadjiefthymiades, Advanced principal component-based compression schemes for wireless sensor networks. ACM Trans. Sen. Netw. 11, 1, Article 7, 34 pages (2014)

    Google Scholar 

  27. C. Anagnostopoulos, S. Hadjiefthymiades, A. Katsikis, I. Maglogiannis, Autoregressive energy-efficient context forwarding in wireless sensor networks for pervasive healthcare systems. Pers. Ubiquitous Comput. 18(1), 101–114 (2014)

    Google Scholar 

  28. C. Anagnostopoulos, S. Hadjiefthymiades, P. Georgas, PC3: principal component-based context compression. J. Parallel Distrib. Comput. 72(2), 155–170 (2012)

    Google Scholar 

  29. A. Manjeshwar, D.P. Agrawal, TEEN: a routing protocol for enhanced efficiency in wireless sensor networks, in Proceedings of the 15th International Parallel & Distributed Processing Symposium (IPDPS ’01) (IEEE Computer Society, Washington, DC, USA), p. 189

    Google Scholar 

  30. K. Papithasri, M. Babu, Efficient multihop dual data upload clustering based mobile data collection in Wireless Sensor Network, in 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, pp. 1–6, 2016

    Google Scholar 

  31. H. Jiang, S. Jin, C. Wang, Prediction or not? an energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22(6), 1064–1071 (2011). June

    Article  Google Scholar 

  32. L. Bottou, F.E. Curtis, J. Nocedal, Optimization Methods for Large-Scale Machine Learning, arXiv:1606.04838

  33. M. Dallachiesa, G. Jacques-Silva, B. Gedik, K.-L. Wu, T. Palpanas, Sliding windows over uncertain data streams. Knowl. Inf. Syst. (2014)

    Google Scholar 

  34. K. Patroumpas, T. Sellis, Maintaining consistent results of continuous queries under diverse window specifications. Inf. Syst. 36(1), 42–61 (2011)

    Article  Google Scholar 

  35. D.J. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul, S. Zdonik, Aurora: a new model and architecture for data stream management. VLDB J. 12(2), 120–139 (2003)

    Article  Google Scholar 

  36. D.J. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J.-H. Hwang, W. Lindner, A.S. Maskey, A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing, S. Zdonik, The design of the Borealis stream processing engine, in CIDR, Jan 2005

    Google Scholar 

  37. J. Gray, S. Chaudhuri et al., Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997). Mar

    Article  Google Scholar 

  38. A. Arasu, S. Babu, J. Widom, The CQL continuous query language: semantic foundations and query execution. VLDB J. 15(2), 121–142 (2006). June

    Article  Google Scholar 

  39. K. Patroumpas, T. Sellis, Multi-granular time-based sliding windows over data streams, in 2010 17th International Symposium on Temporal Representation and Reasoning (TIME), pp. 146–53, 2010

    Google Scholar 

  40. K. Patroumpas, T. Sellis, Window specification over data streams, in Proceedings of the International Conference on Current Trends in Database Technology (EDBT’06) (Springer, Berlin, 2006), pp. 445–464

    Google Scholar 

  41. D. Chu, A. Deshpande, J.M. Hellerstein, W. Hong, Approximate data collection in sensor networks using probabilistic models, in Proceedings of the IEEE International Conference on Data Engineering (ICDE), 2006

    Google Scholar 

  42. V.P. Chowdappa, C. Botella, B. Beferull-Lozano, Distributed clustering algorithm for spatial field reconstruction in wireless sensor networks, in IEEE 81st vehicular technology conference (VTC Spring), Glasgow, vol. 2015, pp. 1–6, 2015

    Google Scholar 

  43. C. Anagnostopoulos, T. Anagnostopoulos, S. Hadjiefthymiades, An adaptive data forwarding scheme for energy efficiency in Wireless Sensor Networks, in 5th IEEE International Conference Intelligent Systems (London, 2010), pp. 396–401

    Google Scholar 

  44. J. Durbin, S. Jan Koopman, Time Series Analysis by State Space Methods (Oxford Statistical Science Series, 2012)

    Google Scholar 

  45. S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators B: Chem. 129(2), 750–757, 22 Feb 2008. ISSN 0925-4005

    Google Scholar 

  46. C. Tofallis, A better measure of relative prediction accuracy for model selection and model estimation. J. Oper. Res. Soc. 66(8), 1352–1362

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK

    Natascha Harth & Christos Anagnostopoulos

  2. R&D Repado, B. Georgiou & Souri, Athens, Greece

    Kostas Delakouridis

Authors
  1. Natascha Harth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kostas Delakouridis
    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 Christos Anagnostopoulos .

Editor information

Editors and Affiliations

  1. Machine Intelligence Institute, Iona College, New Rochelle, New York, USA

    Ronald R. Yager

  2. Department of Computer Science , Oviedo University, Oviedo, Spain

    Jordán Pascual Espada

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Harth, N., Delakouridis, K., Anagnostopoulos, C. (2018). Convey Intelligence to Edge Aggregation Analytics. In: Yager, R., Pascual Espada, J. (eds) New Advances in the Internet of Things. Studies in Computational Intelligence, vol 715. Springer, Cham. https://doi.org/10.1007/978-3-319-58190-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58190-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58189-7

  • Online ISBN: 978-3-319-58190-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation