Skip to main content

RaESS: Reliable-and-Efficient Statistical Spreading Data Fusion Mechanism in Wireless Sensor Network

  • Conference paper
  • First Online:
Software Engineering Trends and Techniques in Intelligent Systems (CSOC 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 575))

Included in the following conference series:

  • 922 Accesses

Abstract

Ensuring continued sustainable communication characteristics are still questionable fact to be obtained by existing data fusion techniques. We have reviewed existing studies to find more scope towards reliability. This paper has presented a novel model called as RaESS or Reliable-and-Efficient Statistical Spreading Data Fusion Mechanism which mainly aims to achieve higher number of unique transmission and lower utilization of resources. We introduced Degree of Information that compliments to increase reliable transmission while minimizing packet drops. Compared to existing technique, proposed technique shows reduced energy consumption and enhanced communication performance (data delivery ratio, delay, algorithm processing time).

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

Access this chapter

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. Rehmani, M.H., Pathan, A.-S.K.: Emerging Communication Technologies Based on Wireless Sensor Networks: Current Research and Future Applications. CRC Press-Computers, Boca Raton (2016)

    Book  Google Scholar 

  2. Behmann, F., Wu, K.: Collaborative Internet of Things (C-IoT): for Future Smart Connected Life and Business. Wiley, Chichester (2015)

    Book  Google Scholar 

  3. Kamila, N.K.: Handbook of Research on Wireless Sensor Network Trends, Technologies, and Applications. IGI Global (2016)

    Google Scholar 

  4. Fahmy, H.M.A.: Wireless Sensor Networks: Concepts, Applications, Experimentation and Analysis. Springer, Singapore (2016)

    Book  Google Scholar 

  5. Mahmoud, M.S., Xia, Y.: Networked Filtering and Fusion in Wireless Sensor Networks. CRC Press, New York (2014)

    Book  Google Scholar 

  6. Castanedo, F.: A review of data fusion techniques. Sci. World J. (2013). Hindawi Publishing Corporation

    Google Scholar 

  7. Sidek, O., Quadri, S.A.: A review of data fusion models and systems. Int. J. Image Data Fusion 3(1), 3–21 (2012). Taylor & Francis

    Article  Google Scholar 

  8. Braca, P., Goldhahn, R., Ferri, G., LePage, K.D.: Distributed information fusion in multistatic sensor networks for underwater surveillance. IEEE Sens. J. 16(11), 4003–4014 (2016)

    Article  Google Scholar 

  9. Jayasri, B.S., Raghavendra Rao, G.: Need For Energy Efficient Data Fusion in Wireless Sensor Networks. Int. J. Eng. Res. Technol. (IJERT) 3(1), January 2014

    Google Scholar 

  10. Jayasri, B.S., Rao, G.R.: Reviewing the research paradigm of techniques used in data fusion in WSN. In: IEEE International Conference in Computing and Communications Technologies (ICCCT), pp. 83–88, 26–27 February 2015

    Google Scholar 

  11. Liu, L., Luo, G., Qin, K., Zhang, X.: An algorithm based on logistic regression with data fusion in wireless sensor networks. EURASIP J. Wirel. Commun. Networking (2017). Springer

    Google Scholar 

  12. Chen, Y.L., et al.: Inexpensive multimodal sensor fusion system for autonomous data acquisition of road surface conditions. IEEE Sens. J. 16(21), 7731–7743 (2016)

    Article  Google Scholar 

  13. Farias, R.C., Cohen, J.E., Comon, P.: Exploring multimodal data fusion through joint decompositions with flexible couplings. IEEE Trans. Sig. Process. 64(18), 4830–4844 (2016)

    Article  MathSciNet  Google Scholar 

  14. Habib, C., Makhoul, A., Darazi, R., Salim, C.: Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Trans. Ind. Inf. 12(6), 2342–2352 (2016)

    Article  Google Scholar 

  15. Baccarelli, E., et al.: Green multimedia wireless sensor networks: distributed intelligent data fusion, in-network processing, and optimized resource management. IEEE Wirel. Commun. 21(4), 20–26 (2014)

    Article  MathSciNet  Google Scholar 

  16. Yassine, A., Nasser, Y., Awad, M., Uguen, B.: Hybrid positioning data fusion in heterogeneous networks with critical hearability. EURASIP J. Wirel. Commun. Networking (2015)

    Google Scholar 

  17. Zhang, Z.J., Lai, C.F., Chao, H.C.: A green data transmission mechanism for wireless multimedia sensor networks using information fusion. IEEE Wirel. Commun. 21(4), 14–19 (2014)

    Article  Google Scholar 

  18. Larios, D.F., Barbancho, J., Rodríguez, G., Sevillano, J.L., Molina, F.J., León, C.: Energy efficient wireless sensor network communications based on computational intelligent data fusion for environmental monitoring. IET Commun. 6(14), 2189–2197 (2012)

    Article  Google Scholar 

  19. Neves, P.A.C.S., Rodrigues, J.J.P.C., Lin, K.: Data fusion on wireless sensor and actuator networks powered by the zensens system. IET Commun. 5(12), 1661–1668 (2011)

    Article  Google Scholar 

  20. Tan, R., Xing, G., Liu, B., Wang, J., Jia, X.: Exploiting data fusion to improve the coverage of wireless sensor networks. IEEE/ACM Trans. Networking 20(2), 450–462 (2012)

    Article  Google Scholar 

  21. Nemati, S., Malhotra, A., Clifford, G.: Data fusion for improved respiration rate estimation. EURASIP J. Adv. Sig. Process. 2010, 926305 (2010)

    Article  Google Scholar 

  22. Yue, Y., Fan, H., Li, J., Qin, Q.: Large-scale mobile wireless sensor network data fusion algorithm. In: 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, pp. 1–5 (2016)

    Google Scholar 

  23. Jayasri, B.S., Raghavendra Rao, G.: EEDF: energy efficient data fusion supportive of virtual multipath propagation in WSN. Int. J. Appl. Eng. Research (IJAER) 10(86) (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. S. Jayasri .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Jayasri, B.S., Raghavendra Rao, G. (2017). RaESS: Reliable-and-Efficient Statistical Spreading Data Fusion Mechanism in Wireless Sensor Network. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57141-6_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57140-9

  • Online ISBN: 978-3-319-57141-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics