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A hybrid soft computing: SGP clustering methodology for enhancing network lifetime in wireless multimedia sensor networks

  • P. X. BrittoEmail author
  • S. Selvan
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Abstract

The sensor network that contains sensor nodes equipped with cameras, microphones and other sensors producing multimedia content is known as wireless multimedia sensor network (WMSN). In WMSN, battery energy and network lifetime are the real requirements of research for the transmission of multimedia information needs more energy. This research paper proposes a soft computing-based approach for optimizing the cluster head selection process to decide upon the optimal path of transmission, and SGP approach is used for cluster formation. A three-layer ANN model trained using back propagation has been proposed in this paper, and comparative analysis is performed among existing techniques such as LEACH, Q-LEACH, C-LEACH and SGP algorithms. It is found from the observations that proposed neural hybrid approach exhibits superior performance while exhibiting marginal improvement over SGP due to the training process where the error is minimized.

Keywords

Spectral graph partitioning Wireless multimedia sensor networks Eigenvalues and eigenvectors Artificial neural networks Back propagation learning 

List of symbols

\( p \)

Probability of node to be a cluster head

\( node\_distance \left( i \right) \)

Distance of the ith node from base station

\( S\left( i \right) \cdot xd, \;S\left( i \right) \cdot yd \)

Location of the ith node

\( sink \cdot x, sink \cdot y \)

Location of the base station

\( S\left( i \right) \cdot E \)

Energy of the ith node

\( {\text{ETX}} \)

Transmit energy

\( {\text{EDA}} \)

Data aggregation energy

\( E_{\text{fs}} ,E_{\text{amp}} \)

Transmit amplifier energy

\( r_{ \hbox{max} } \)

Maximum number of rounds

\( E_{\text{avg}} \)

Average energy of the nodes

\( N \)

Number of nodes

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.

References

  1. Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30:2826–2841CrossRefGoogle Scholar
  2. Ahlawat A, Malik V (2013) An extended vice cluster selection approach to improve V-leach protocol in WSN. Inn: 3rd International conference on advanced computing and communication technologies. IEEE.Google Scholar
  3. Akyildiz IF, Melodia T, Chowdhury KR (2006) A survey on wireless multimedia sensor networks. Comput Netw 51(921):960Google Scholar
  4. Akyildiz IF, Melodia T, Chowdury KR (2007a) Wireless multimedia sensor networks: a survey. IEEE Wirel Commun 14:32–39CrossRefGoogle Scholar
  5. Akyildiz IF, Melodia T, Chowdhury KR (2007b) A survey on wireless multimedia sensor networks. Comput Netw 51(4):921–960CrossRefGoogle Scholar
  6. Akyildiz IF, Melodia T, Chowdhury KR (2008) Wireless multimedia sensor networks: applications and testbeds. Proc IEEE 96(1588):1605Google Scholar
  7. Alaei M, Barcelo-Ordinas JM (2010) A method for clustering and cooperation in wireless multimedia sensor networks. Sensors 10:3145–3169CrossRefGoogle Scholar
  8. Almalkawi IT, Zapta MG, Al-Karaki JN, Morillo-Pozo J (2010) Wireless multimedia sensor networks: current trends and future directions. Sensors 10(6662):6717Google Scholar
  9. Chan PK, Schlag MDF, Zien JY (1994) Spectral K-way ratio-cut partitioning and clustering. IEEE Trans Comput Aided Des Integr Circuits Syst 13(9):1088–1096CrossRefGoogle Scholar
  10. Dutta R, Gupta S, Das MK (2012) Efficient statistical clustering techniques for optimizing cluster size in wireless sensor network. Procedia Eng 38:1501–1507CrossRefGoogle Scholar
  11. Elbhiri B, El Fkihi S, Saadane R, Aboutajdine D (2010) Clustering in wireless sensor network based on near optimal bi-partitions. In: 6th EURO-NF conference on next generation internet (NGI)Google Scholar
  12. Ersahin K, Cumming IG, Ward RK (2010) Segmentation and classification of polarimetric SAR data using spectral graph partitioning. IEEE Trans Geosci Remote Sens 48(1):164–174CrossRefGoogle Scholar
  13. Garcia-Sanchez A-J, Garcia-Sanchez F, Rodenas-Herraiz D, Garcia-Haro J (2012) On the optimization of wireless multimedia sensor networks: a goal programming approach. Sensors 12:12634–12660CrossRefGoogle Scholar
  14. Heinzelman WR, Chandrakasan AP, Balakrishnan H (2000) Energy-efficient communication protocol for wireless micro sensor networks. In: Proceedings of 33rd annual hawaii international conference on system sciences, Jan 2000.Google Scholar
  15. Izadi D, Abawajy J, Ghanavati S (2013) A new energy efficient cluster-Head and Backup selection scheme in WSN. In: IEEE IRI, Aug 2013Google Scholar
  16. Kannan R, Vempala S, Vetta A (2004) On clusterings: good, bad and spectral. J. ACM 51(497):515MathSciNetzbMATHGoogle Scholar
  17. Kumar P, Chand N (2013a) clustering in wireless multi- media sensor networks using spectral graph partition ing. Int J Commun Netw Syst Sci 6(3):128–133Google Scholar
  18. Kumar P, Chand N (2013b) Clustering in wireless multimedia sensor networks. J Sens Technol 3:126CrossRefGoogle Scholar
  19. Kumarawat M, Dhawan M (2015) Survey on clustering algorithms of wireless sensor network. Int J Comput Sci Inf Technol 6(3):2046Google Scholar
  20. Li C, Ye M, Chen G, Wu J (2005) An energy efficient unequal clustering mechanism for wireless sensor networks. In: 2nd IEEE international conference on mobile adhoc and sensors ystems (MASS), pp 125–132Google Scholar
  21. Liao Y, Qi H, Li W (2013) Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sens J 13(5):1498–1506CrossRefGoogle Scholar
  22. Liu X (2012) A survey on clustering routing protocols in wireless sensor networks. Sensors 12:11113–11153CrossRefGoogle Scholar
  23. Park GY (2013) A novel cluster head selection method based on K-means algorithm for energy efficient wireless sensor network. In: 27th International conference on advanced information networking and applications workshopsGoogle Scholar
  24. Perumal V, Meenakshi Sundaram K (2017) The comparison of energy efficient in wireless sensor network using various clustering methods and different protocols. Int J Eng Dev Res 5(2):1974–1978Google Scholar
  25. Plageras AP, Psannis KE, Stergiou C, Wang H, Gupta BB (2018) Efficient IoT-based sensor BIG Data collection-processing and analysis in smart buildings. Future Gener Comput Syst 82:349–357CrossRefGoogle Scholar
  26. Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29:2230–2237CrossRefGoogle Scholar
  27. Reddy SS, Patwari GR, Patil SP, Sugoor VB (2016) Energy efficient QoS aware load balancing in wireless multimedia sensor network. Int J Adv Eng Technol Manag Appl Sci 3(2):53–62Google Scholar
  28. Singh S, Malik A, Kumar R (2017) Energy efficient heterogeneous DEEC protocol for enhancing lifetime in WSNs. Eng Sci Technol 20:345–353Google Scholar
  29. Wajgi D, Thakur NV (2012) Load balancing based approach to improve lifetime of wireless sensor network. Int J Wirel Mobile Netw (IJWMN) 4(4):155CrossRefGoogle Scholar
  30. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3:366–379CrossRefGoogle Scholar
  31. Younis O, Krunz M, Ramasubramanian S (2006) Node clustering in wireless sensor networks: recent developments and deployment challenge. IEEE Netw 20:20–25CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Christian Polytechnic CollegeOddanchatramIndia
  2. 2.St.Peter’s College of Engineering and TechnologyAvadiIndia

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