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Improving performance of node clustering in wireless sensor networks using meta-heuristic algorithms and a novel validity index

  • Mohammad Karim SohrabiEmail author
  • Somayyeh Alimirzaee
Article
  • 27 Downloads

Abstract

The use of wireless sensor networks has significantly increased in the last decade. These networks consist of a large number of small sensors, which are efficient tools for data collection from different environments. The data collected by these sensors will be usually transmitted to a base station that will provide the end user’s data. In order to improve scalability of such networks, sensor nodes can be grouped into non-overlapped clusters. These clusters create a hierarchical design in wireless sensor networks that leads to better energy utilization and thus increase the network’s lifetime. Using validity indexes and meta-heuristic algorithms are common ways to improve performance of clustering. In this paper, we provide a new validity index called ASCS by enhancing the Chou and Su (CS) validity index, and improve the performance of the meta-heuristic algorithms for clustering using this new validity index as their objective functions. Differential evolution and harmony search are two algorithms that will be used for this purpose. The experimental results show the better performance of ASCS index comparing to Davies and Bouldin (DB) and CS validity indexes in determining appropriate number of clusters and determining proper clusters’ members.

Keywords

Clustering Wireless sensor network Validity index Meta-heuristic algorithm Performance evaluation 

Notes

References

  1. 1.
    Chen S, Wang K, Zhao C, Zhang H, Sun Y (2017) Accelerated distributed optimization design for reconstruction of big sensory data. IEEE Internet Things J 4(5):1716–1725Google Scholar
  2. 2.
    Dou C, Cui Y, Sun D, Wong R, Atif M, Li G, Ranjan R (2019) Unsupervised blocking and probabilistic parallelisation for record matching of distributed big data. J Supercomput 75(2):623–645Google Scholar
  3. 3.
    Xing EP, Ho Q, Dai W, Kim JK, Wei J, Lee S, Zheng X, Xie P, Kumar A (2015) Petuum: a new platform for distributed machine learning on big data. IEEE Trans Big Data 1(2):49–67Google Scholar
  4. 4.
    Reuther A, Byun C, Arcand W, Bestor D, Bergeron B, Hubbell M, Jones M, Michaleas P, Prout A, Rosa A, Kepner J (2018) Scalable system scheduling for HPC and big data. J Parallel Distrib Comput 111:76–92Google Scholar
  5. 5.
    Sohrabi MK (2018) A Gossip-based information fusion protocol for distributed frequent itemset mining. Enterp Inf Syst 12(6):679–694Google Scholar
  6. 6.
    Cheng F, Yang Z (2019) FastMFDs: a fast, efficient algorithm for mining minimal functional dependencies from large-scale distributed data with Spark. J Supercomput 75(2):2497–2517Google Scholar
  7. 7.
    Sohrabi MK, Taheri N (2018) A haoop-based parallel mining of frequent itemsets using N-Lists. J Chin Inst Eng 41(3):229–238Google Scholar
  8. 8.
    Tassa T, Cohen DJ (2013) Anonymization of centralized and distributed social networks by sequential clustering. IEEE Trans Knowl Data Eng 25(2):311–324Google Scholar
  9. 9.
    He C, Fei X, Li H, Tang Y, Liu H, Liu S (2018) Improving NMF-based community discovery using distributed robust nonnegative matrix factorization with SimRank similarity measure. J Supercomput 74(10):5601–5624Google Scholar
  10. 10.
    Li Y, Lui JCS (2014) Friends or foes: distributed and randomized algorithms to determine dishonest recommenders in online social networks. IEEE Trans Inf Forensics Secur 9(10):1695–1707Google Scholar
  11. 11.
    Islam AKMM, Wada K (2013) Communication protocols on dynamic cluster-based wireless sensor network. In: 2013 International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, pp 1–6Google Scholar
  12. 12.
    Intanagonwiwat C, Govindan R, Estrin D, Heidemann J, Silva F (2003) Directed diffusion for wireless sensor networking. IEEE/ACM Trans Netw 11(1):1–15Google Scholar
  13. 13.
    Das S, Abraham A, Konar A (2008) Automatic clustering using an improved differential evolution algorithm. IEEE Trans Syst Man Cybern Part A Syst Hum 38(1):218–237Google Scholar
  14. 14.
    Halkidi M, Vazirgiannis M (2001) Clustering validity assessment: finding the optimal partitioning of a data set. In: IEEE International Conference on Data Mining, ICDM, San Jose, CA, pp 187–194Google Scholar
  15. 15.
    Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3(1):1–27MathSciNetzbMATHGoogle Scholar
  16. 16.
    Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227Google Scholar
  17. 17.
    Pakhira MK, Bandyopadhyay S, Maulik U (2004) Validity index for crisp and fuzzy clusters. Pattern Recognit Lett 37(3):487–501zbMATHGoogle Scholar
  18. 18.
    Chou CH, Su MC, Lai E (2004) A new cluster validity measure and its application to image compression. Pattern Anal Appl 7(2):205–220MathSciNetGoogle Scholar
  19. 19.
    Sohrabi MK, Azgomi H (2018) A survey on the combined use of optimization methods and game theory. Arch Comput Methods Eng.  https://doi.org/10.1007/s11831-018-9300-5 Google Scholar
  20. 20.
    Selim SZ, Alsultan K (1991) A simulated annealing algorithm for the clustering problem. Pattern Recognit 24(10):1003–1008MathSciNetGoogle Scholar
  21. 21.
    Sohrabi MK, Karimi F (2018) Feature selection approach to detect spam in the Facebook social network. Arabian J Sci Eng.  https://doi.org/10.1007/s13369-017-2855-x Google Scholar
  22. 22.
    Arab M, Sohrabi MK (2017) Proposing a new clustering method to detect phishing websites. Turk J Electr Eng Comput Sci.  https://doi.org/10.3906/elk-1612-279 Google Scholar
  23. 23.
    Diwakaran S, Perumal B, Vimala Devi K (2019) A cluster prediction model-based data collection for energy efficient wireless sensor network. J Supercomput 75(6):3302–3316Google Scholar
  24. 24.
    Ahmed G, Zou J, Fareed MMS, Zeeshan M (2016) Sleep-awake energy efficient distributed clustering algorithm for wireless sensor networks. Comput Electr Eng 56:385–398Google Scholar
  25. 25.
    Dabirmoghaddam A, Ghaderi M, Williamson C (2014) On the optimal randomized clustering in distributed sensor networks. Comput Netw 59:17–32Google Scholar
  26. 26.
    Cenedese A, Luvisotto M, Michieletto G (2017) Distributed clustering strategies in industrial wireless sensor networks. IEEE Trans Ind Inf 13(1):228–237Google Scholar
  27. 27.
    Nayak P, Vathasavai B (2017) Energy efficient clustering algorithm for multi-hop wireless sensor network using type-2 Fuzzy logic. IEEE Sens J 17(14):4492–4499Google Scholar
  28. 28.
    Sasirekha S, Swamynathan S (2017) Cluster-chain mobile agent routing algorithm for efficient data aggregation in wireless sensor network. J Commun Netw 19(4):392–401Google Scholar
  29. 29.
    Kirton J, Bradbury M, Jhumka A (2017) Source location privacy-aware data aggregation scheduling for wireless sensor networks. In: IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA, 5–8 June 2017Google Scholar
  30. 30.
    Boukerche A, Zhou X (2017) MAC transmission protocols for delay-tolerant sensor networks. Comput Netw 124:108–125Google Scholar
  31. 31.
    Cuevas-Martinez JC, Yuste-Delgado AJ, Triviño-Cabrera A (2017) Cluster head enhanced election type-2 fuzzy algorithm for wireless sensor networks. IEEE Commun Lett 21(9):2069–2072Google Scholar
  32. 32.
    Jia D, Zhu H, Zu S, Hu P (2016) Dynamic cluster head selection method for wireless sensor network. IEEE Sens J 16(8):2746–2754Google Scholar
  33. 33.
    Naeem MK, Patwary M, Abdel-Maguid M (2017) Universal and dynamic clustering scheme for energy constrained cooperative wireless sensor networks. IEEE Access 5:12318–12337Google Scholar
  34. 34.
    Neamatollahi P, Naghibzadeh M, Abrishami S (2017) Fuzzy-based clustering-task scheduling for lifetime enhancement in wireless sensor networks. IEEE Sens J 17(20):6837–6844Google Scholar
  35. 35.
    Naranjo PGV, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E (2017) P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J Supercomput 73(2):733–755Google Scholar
  36. 36.
    Zhou P, Wang C, Yang Y (2017) Leveraging target k-coverage in wireless rechargeable sensor networks. In: IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA, 5–8 June 2017Google Scholar
  37. 37.
    Rajeswari K, Neduncheliyan S (2017) Genetic algorithm based fault tolerant clustering in wireless sensor network. IET Commun 11(12):1927–1932Google Scholar
  38. 38.
    Gherbi C, Aliouat Z, Benmohammed M (2016) An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy 114:647–662Google Scholar
  39. 39.
    Wu J, Chen J, Xiong H, Xie M (2009) External validation measures for K-means clustering: a data distribution perspective. Expert Syst Appl 36:6050–6061Google Scholar
  40. 40.
    Luna-Romera JM, Martínez-Ballesteros M, García-Gutiérrez J, Riquelme JC (2019) External clustering validity index based on Chi-squared statistical test. Inf Sci 487:1–17MathSciNetGoogle Scholar
  41. 41.
    Lee S-H, Jeong Y-S, Kim J-Y, Jeong MK (2018) A new clustering validity index for arbitrary shape of clusters. Pattern Recognit Lett 112:263–269Google Scholar
  42. 42.
    Hancer E, Karaboga D (2017) A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number. Swarm Evolut Comput 32:49–67Google Scholar
  43. 43.
    Zhou S, Xu Z (2018) A novel internal validity index based on the cluster centre and the nearest neighbour cluster. Appl Soft Comput 71:78–88Google Scholar
  44. 44.
    Žalik KR, Žalik B (2011) Validity index for clusters of different sizes and densities. Pattern Recognit Lett 32(2):221–234zbMATHGoogle Scholar
  45. 45.
    Rojas-Thomas JC, Santos M, Mora M (2017) New internal index for clustering validation based on graphs. Expert Syst Appl 86:334–349Google Scholar
  46. 46.
    Zhao Q, Fränti P (2014) WB-index: a sum-of-squares based index for cluster validity. Data Knowl Eng 92:77–89Google Scholar
  47. 47.
    Bhargavi MS, Gowda SD (2015) A novel validity index with dynamic cut-off for determining true clusters. Pattern Recognit 48(11):3673–3687Google Scholar
  48. 48.
    Zhu E, Ma R (2018) An effective partitional clustering algorithm based on new clustering validity index. Appl Soft Comput 71:608–621Google Scholar
  49. 49.
    Flexa C, Santos R, Gomes W, Sales C, Costa JCWA (2019) Mutual equidistant-scattering criterion: a new index for crisp clustering. Expert Syst Appl 128:225–245Google Scholar
  50. 50.
    Ünlü R, Xanthopoulos P (2019) Estimating the number of clusters in a dataset via consensus clustering. Expert Syst Appl 125:33–39Google Scholar
  51. 51.
    Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65zbMATHGoogle Scholar
  52. 52.
    Zhang Y, Wang W, Zhang X, Li Y (2008) A cluster validity index for fuzzy clustering. Inf Sci 178(4):1205–1218zbMATHGoogle Scholar
  53. 53.
    Rezaee B (2010) A cluster validity index for fuzzy clustering. Fuzzy Sets Syst 161(23):3014–3025MathSciNetzbMATHGoogle Scholar
  54. 54.
    Žalik KR (2010) Cluster validity index for estimation of fuzzy clusters of different sizes and densities. Pattern Recognit 43(10):3374–3390zbMATHGoogle Scholar
  55. 55.
    Zhang D, Ji M, Yang J, Zhang Y, Xie F (2014) A novel cluster validity index for fuzzy clustering based on bipartite modularity. Fuzzy Sets Syst 253:122–137MathSciNetGoogle Scholar
  56. 56.
    Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evolut Comput 30:1–10Google Scholar
  57. 57.
    Ni Q, Pan Q, Du H, Cao C, Zhai Y (2017) A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization. IEEE/ACM Trans Comput Biol Bioinf 14(1):76–84Google Scholar
  58. 58.
    Bhatia T, Kansal S, Goel S, Verma AK (2016) A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Comput Electr Eng 56:441–455Google Scholar
  59. 59.
    Liao W, Kao Y, Fan C (2008) Data aggregation in wireless sensor networks using ant colony algorithm. J Netw Comput Appl 31(4):387–401Google Scholar
  60. 60.
    Hashim HA, Ayinde BO, Abido MA (2016) Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. J Netw Comput Appl 64:239–248Google Scholar
  61. 61.
    Zhou Y, Wang N, Xiang W (2017) Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access 5:2241–2253Google Scholar
  62. 62.
    Güngör Z, Ünler A (2008) K-harmonic means data clustering with tabu-search method. Appl Math Model 32(6):1115–1125zbMATHGoogle Scholar
  63. 63.
    Xenakis A, Foukalas F, Stamoulis G (2016) Cross-layer energy-aware topology control through Simulated Annealing for WSNs. Comput Electr Eng 56:576–590Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Semnan BranchIslamic Azad UniversitySemnanIran

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