Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Soft Computing
Published in: Soft Computing 8/2019

10-01-2019 | Focus

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

Authors: P. X. Britto, S. Selvan

Published in: Soft Computing | Issue 8/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Medical big data analysis: preserving security and privacy with hybrid cloud technology
next article Digital acquisition and character extraction from stone inscription images using modified fuzzy entropy-based adaptive thresholding

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30:2826–2841CrossRef
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.
Akyildiz IF, Melodia T, Chowdhury KR (2006) A survey on wireless multimedia sensor networks. Comput Netw 51(921):960
Akyildiz IF, Melodia T, Chowdury KR (2007a) Wireless multimedia sensor networks: a survey. IEEE Wirel Commun 14:32–39CrossRef
Akyildiz IF, Melodia T, Chowdhury KR (2007b) A survey on wireless multimedia sensor networks. Comput Netw 51(4):921–960CrossRef
Akyildiz IF, Melodia T, Chowdhury KR (2008) Wireless multimedia sensor networks: applications and testbeds. Proc IEEE 96(1588):1605
Alaei M, Barcelo-Ordinas JM (2010) A method for clustering and cooperation in wireless multimedia sensor networks. Sensors 10:3145–3169CrossRef
Almalkawi IT, Zapta MG, Al-Karaki JN, Morillo-Pozo J (2010) Wireless multimedia sensor networks: current trends and future directions. Sensors 10(6662):6717
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):1088–1096CrossRef
Dutta R, Gupta S, Das MK (2012) Efficient statistical clustering techniques for optimizing cluster size in wireless sensor network. Procedia Eng 38:1501–1507CrossRef
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)
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):164–174CrossRef
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 12:12634–12660CrossRef
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.
Izadi D, Abawajy J, Ghanavati S (2013) A new energy efficient cluster-Head and Backup selection scheme in WSN. In: IEEE IRI, Aug 2013
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):128–133
Kumar P, Chand N (2013b) Clustering in wireless multimedia sensor networks. J Sens Technol 3:126CrossRef
Kumarawat M, Dhawan M (2015) Survey on clustering algorithms of wireless sensor network. Int J Comput Sci Inf Technol 6(3):2046
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 125–132
Liao Y, Qi H, Li W (2013) Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. IEEE Sens J 13(5):1498–1506CrossRef
Liu X (2012) A survey on clustering routing protocols in wireless sensor networks. Sensors 12:11113–11153CrossRef
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 workshops
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):1974–1978
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 82:349–357CrossRef
Qing L, Zhu Q, Wang M (2006) Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Comput Commun 29:2230–2237CrossRef
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):53–62
Singh S, Malik A, Kumar R (2017) Energy efficient heterogeneous DEEC protocol for enhancing lifetime in WSNs. Eng Sci Technol 20:345–353
Wajgi D, Thakur NV (2012) Load balancing based approach to improve lifetime of wireless sensor network. Int J Wirel Mobile Netw (IJWMN) 4(4):155CrossRef
Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3:366–379CrossRef
Younis O, Krunz M, Ramasubramanian S (2006) Node clustering in wireless sensor networks: recent developments and deployment challenge. IEEE Netw 20:20–25CrossRef
Metadata
Title
A hybrid soft computing: SGP clustering methodology for enhancing network lifetime in wireless multimedia sensor networks
Authors
P. X. Britto
S. Selvan
Publication date
10-01-2019
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 8/2019
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-018-03716-3

Other articles of this Issue 8/2019

Soft Computing 8/2019 Go to the issue

Focus

Performance analysis of soft computing techniques for the automatic classification of fruits dataset

Focus

A novel vessel detection and classification algorithm using a deep learning neural network model with morphological processing (M-DLNN)

Focus

Optimal resource allocation algorithm for OFDMA-based WiMAX network using stochastic fish swarm optimization

Focus

Destination-aware context-based routing protocol with hybrid soft computing cluster algorithm for VANET

Focus

Hybrid data fusion model for restricted information using Dempster–Shafer and adaptive neuro-fuzzy inference (DSANFI) system

Focus

Enhanced secure communication over inter-domain routing in heterogeneous wireless networks based on analysis of BGP anomalies using soft computing techniques

Premium Partner

    Image Credits