Skip to main content

Advertisement

SpringerLink
Log in

Multilayer cluster designing algorithm for lifetime improvement of wireless sensor networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cluster-based network is a proven architecture for energy-aware routing, but more attention is required to ameliorate the energy consumption aspect of its cluster designing process. In this research work, we introduce a novel design of clustered network architecture. The proposed design technique is innovative in its idea. The general trend in this scene is either centralized decision at base station for cluster head selection and its members or distributed decision by exchanging information between neighboring nodes until the cluster head and its members are selected. Both the techniques drastically create mess in energy consumption due to too much broadcasting, especially in large networks as well as message exchange until some final decision is made. Our novel layer-based hybrid algorithm for cluster head and cluster member selection comes up to novel communication architecture. Since its substantial constituent is cluster designing, we named it Multilayer Cluster Designing Algorithm (MCDA). The proposed design not only has effect on lessening blind broadcasting, but also on decreasing the message exchange in a passionate way. It also encapsulates the beauty of efficient centralized decision making for cluster designing and energy-aware distributed cluster head selection and cluster member allocation process. Comprehensive experimentations have been performed on the comparative analysis of MCDA with state-of-the-art centralized and distributed cluster designing approaches present in published literature. Calculation of energy consumption in various operational parametric values, number of clusters designed and the number of packets broadcasted during cluster designing are the main performance evaluation parameters. It has been found that MCDA outperforms compared to its three competing algorithms with respect to the aforementioned parameters due to its multilayered synergistic mating approach.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Katiyar V, Chand N, Soni S (2011) A survey on clustering algorithms for heterogeneous wireless sensor networks. Int J Adv Netw Appl 02(04): 745–754

    Google Scholar 

  2. Kumar D, Aseri TC, Patel RB (2011) EECDA: energy efficient clustering and data aggregation protocol for heterogeneous wireless sensor networks. Int J Comput Commun Control VI(1):113–124. (ISSN 1841–9836, E-ISSN 1841–9844)

  3. Malazi HT, Zamanifar K, Khalili A, Dulman S (2013) DEC: diversity-based energy aware clustering for. Adhoc Sensor Wireless Netw J 17(1–2):53–72

    Google Scholar 

  4. Song M, He B (2007) Capacity analysis for flat and clustered wireless sensor networks. International conference on wireless algorithms, systems and applications

  5. Iwata A, Chiang C-C, Pei G, Chen T-W (1999) Scalable routing strategies for ad hoc wireless networks. IEEE J Selected Areas Commun 17(8):1369–1379

    Article  Google Scholar 

  6. Singh SK, Singh MP, Singh DK (2010) A survey of energy—efficient hierarchical cluster-based routing in wireless sensor networks. Int J Adv Netw Appl 02(02): 570–580

    Google Scholar 

  7. Naeimi S, Ghafghazi H, Chow C-O, Ishii H (2012) A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12:7350–7409. doi:10.3390/s120607350

    Article  Google Scholar 

  8. Aslam N, Robertson W, Phillip W (2009) Algorithms for relay node selection in randomly deployed homogenous cluster-based wireless sensor networks. Adhoc Sensor Wirel Netw J Spec Issue Sensor Technol Appl 8(1–2)

  9. Muruganathan SD, Ma DC, Bhasin RI, Fapojuwo AO (2005) A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Radio Commun

  10. Samet H (2008) K-nearest neighbor finding using max nearest dist. IEEE Trans Pattern Anal Mach Intell 30(2):243–252

    Google Scholar 

  11. Cheung Y, Li MJ, Ng MK, Huang JZ (2008) Agglomerative fuzzy K-means clustering algorithm with selection of number of clusters. IEEE Trans Knowl Data Eng 20(11):1519–1534

    Google Scholar 

  12. Mahmud MS, Rahman MM, Akhtar MN (2012) Improvement of K-means clustering algorithm with better initial centroids based on weighted average. In: 7th International Conference on Electrical & Computer Engineering (ICECE). pp 647–650

  13. Guan R, Shi X, Marchese M, Yang C, Liang Y (2011) Text clustering with seeds affinity propagation. IEEE Trans Knowl Data Eng 23(4):627–637

    Google Scholar 

  14. Jabbar S, Butt AE, Najm-us-Sehr, Minhas AA (2011) TLPER: threshold based load balancing protocol for energy efficient routing in WSN. In: The 13th International Conference on Advanced Communication Technology (ICACT’11), Seoul, South Korea

  15. Maimour M, Zeghilet H, Lepage F (2010) Cluster-based routing protocols for energy efficiency in wireless sensor networks. http://www.intechopen.com

  16. Forster A, Murphy AL (2009) CLIQUE: role-free clustering with Q-learning for wireless sensor networks. In: 29th IEEE international conference on distributed computing systems, IEEE Computer Society, Washington, DC, USA, pp 441–449

  17. Zeghilet H, Badache N, Maimour M (2009) Energy efficient cluster-based routing in wireless sensor networks. IEEE symposium on computers and communications (ISCC’09), Sousse, Tunisia, pp 701–704

  18. Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379

    Article  Google Scholar 

  19. Kang T, Yun J, Lee H, Lee I, Kim H, Lee B, Lee B, Han K (2007) A clustering method for energy efficient routing in wireless sensor networks. In: Proceedings of the 6th WSEAS international conference on electronics, hardware, wireless and optical communications. Wisconsin, USA, pp 133–139

  20. Akhtar A, Minhas AA, Jabbar S (2010) Energy aware intra-cluster routing for wireless sensor networks. Int J Hybrid Inf Technol 3(1)

  21. Lombriser C, Hunkeler U, Truong HL (2011) Centrally controlled clustered wireless sensor networks. IBM Research Report

  22. Meenakshi D, Sushil K (2012) An energy efficient level based clustering routing protocol for wireless sensor. IJASSN 2(2)

  23. Bani Yassein M, Al-zou’bi A, Khamayseh Y, Mardini W (2009) Improvement on LEACH Protocol of Wireless Sensor Network (VLEACH). Int J Digit Content Technol Appl 3(2)

  24. Heinzelman W, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless Microsensor networks. In: Proceedings of the 33rd International Conference on System Sciences (HICSS ’00)

  25. Wang D (2010) Power-mitigating scheme for clusterheads in. Adhoc Sensor Wirel Netw J 9:239–253

    Google Scholar 

  26. Handy MJ, Haase M, Timmermann D (2002) Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In: 4th International Workshop on Mobile and Wireless Communications, Network. pp 368–372

  27. Loscri V, Morabito G, Marano S (2005) A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In: IEEE 62nd Vehicular Technology Conference, vol 3. pp 1809–1813

  28. Asaduzzaman; Kong, HY (2010) Energy efficient cooperative LEACH protocol for wireless sensor networks. J Commun Netw 12(4):358–365

  29. Jin N, Lou X, Peng T, Zhou Q, Chen Y (2012) Improvement of LEACH protocol for WSN. In: 9th international conference on fuzzy systems and knowledge discovery (FSKD). pp 2174–2177

  30. Ye M, Li C, Chen G (2007) An energy efficient clustering scheme in wireless sensor networks. Adhoc Sensor Wirel Netw 3:99–119

    Google Scholar 

  31. Meenakshi S, Kalpana S (2012) An energy efficient extended LEACH (EEE LEACH). In: International conference on communication systems and network technologies

  32. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4)

  33. Murata T, Ishibuchi H (1994) Performance evaluation of genetic algorithms for flowshop scheduling problems. In: Proceedings of 1st IEEE conference evolutionary computation, vol 2. pp 812–817

  34. Ammari HM, Das SK (2012) Centralized and clustered k-coverage protocols for wireless sensor networks. IEEE Trans Comput 61(1):118–133

    Google Scholar 

  35. Yu M, Leung KK, Malvankar A (2007) A dynamic clustering and energy efficient routing technique for sensor networks. IEEE Trans Wirel Commun 6(8)

  36. Wu Y, Chen Z, Jing Q, Wang Y (2007) LENO: Least rotation near-optimal cluster head rotation strategy in wireless sensor networks. In: 21st international conference on advanced networking and applications

  37. http://bullseye.xbow.com:81/Products/Product_pdf_files/Wireless_pdf/MICAz_Datasheet.pdf. Accessed on Feb 4, 2013

  38. Yu J, Qi Y, Wang G, Guo Q, Gu X (2011) An energy-aware distributed unequal clustering protocol for wireless sensor networks. Int J Distrib Sensor Netw 2011. Article ID 202145

  39. Yang P-T, Lee S (2012) A distributed reclustering hierarchy routing protocol using socialwelfare in wireless sensor networks. Int J Distrib Sensor Netw 2012. Article ID 681026

  40. Jabbar S, Zubair Aziz M, Minhas AA, Hussain D (2010) PTAL: power tuning anchors localization algorithm for wireless Ad-Hoc micro sensors network (IEEE), ICESS’2010. Bradford, UK

  41. Paul A, Jiang Y-C, Wang J-F (2012) Parallel reconfigurable computing based mapping algorithm for motion estimation in advance video coding. ACM Trans Embed Comput Syst 11(S2). Article No: 40

    Google Scholar 

  42. Paul A (2013) Dynamic power management for ubiquitous network devices. Adv Sci Lett 19(7):2046–2049

    Google Scholar 

  43. Paul A (2013) Graph based M2M optimization in an IoT environment. In: Proceedings of ACM research in adaptive and convergent systems ACM RACS 2013, Montreal, Canada October 1–4, 2013. pp 45–46

Download references

Acknowledgments

We are very obliged to the Higher Education Commission (HEC) Pakistan for exerting efforts in the real sense to better higher education in Pakistan through scholarships and travel and research grants. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2013R1A1A2061978).

Author information

Authors and Affiliations

  1. Department of Computer Science, Bahria University, Islamabad, Pakistan

    Sohail Jabbar & Abid Ali Minhas

  2. Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan

    Sohail Jabbar

  3. CCIS, Al Yamamah University, Riyadh, Kingdom of Saudi Arabia

    Abid Ali Minhas

  4. The School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea

    Anand Paul

  5. Department of Multimedia, Sungkyul University, Anyang-si, Korea

    Seungmin Rho

Authors
  1. Sohail Jabbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abid Ali Minhas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anand Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Seungmin Rho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sohail Jabbar or Seungmin Rho.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jabbar, S., Minhas, A.A., Paul, A. et al. Multilayer cluster designing algorithm for lifetime improvement of wireless sensor networks. J Supercomput 70, 104–132 (2014). https://doi.org/10.1007/s11227-014-1108-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1108-y

Keywords

Search

Navigation