Mobile cluster head selection using soft computing technique in wireless sensor network

  • G. PrabaharanEmail author
  • S. Jayashri


Wireless sensor networks generally consist of static sensor node, which can be deployed to monitor the environment. The network is built by the sensor nodes, and the data from the source reach base station by passing through a number of sensor nodes. This causes loss of energy at the sensor nodes. To reduce energy loss of the sensor nodes in WSN, cluster-based topology can be used. Sensor nodes are grouped into clusters. Each cluster contains one cluster head for effective communication. Cluster head collects the data from the sensor nodes and sends to the base station. This causes fast depletion of cluster heads energy. To overcome energy problem, we have proposed a novel mobile data gathering in WSN by soft computing-based CH selection and clustering. It is based on fuzzy inference system. The smart CH selection and vehicular data gathering reduce the time and energy loss due to uploading. Due to reduced energy loss, the lifetime of network is also increased. In this paper, we compared the smart CH selection technique with high-energy CH selection on the quality measures packet loss, collection delay, residual energy, distance travelled and network lifetime. The proposed smart CH selection-based vehicular data gathering is produced better results compared to the other methods.


Wireless sensor network Mobile data gathering Clustering Fuzzy Cluster head selection Shortest route 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. Abdo AM, Zhao X, Zhang R, Zhou Z, Zhang J, Zhang Y, Memon I (2018) Mu-MIMO downlink capacity analysis and optimum code weight vector design for 5G big data massive antenna millimeter wave communication. Wirel Commun Mob Comput 2018:1–12Google Scholar
  2. Akhtar R, Leng S, Memon I et al (2015) Architecture of hybrid mobile social networks for efficient content delivery. Wirel Pers Commun 80(1):85–96Google Scholar
  3. Arain QA, Memon H, Memon I, Memon MH, Shaikh RA, Mangi FA (2017a) Intelligent travel information platform based on location base services to predict user travel behavior from user-generated GPS traces. Int J Comput Appl 39(3):155–168Google Scholar
  4. Arain QA, Deng Z, Memon I et al (2017b) Location privacy with dynamic pseudonym-based multiple mix-zones generation over road networks. Wirel Pers Commun 97(3):3645–3671Google Scholar
  5. Bensaid C, Hacene SB, Faraoun KM (2016) Detection and ignoring of blackhole attack in vanets networks. Int J Cloud Appl Comput 6(2):1–10Google Scholar
  6. Dargie W, Poellabauer C (2010) Fundamentals of wireless sensor networks: theory and practice. Wiley, HobokenGoogle Scholar
  7. Dasgupta K, Kalpakis K, Namjoshi P (2003) An efficient clustering-based heuristic for data gathering and aggregation in sensor networks. IEEE Wirel Commun Netw 3:1948–1953zbMATHGoogle Scholar
  8. El Alami H, Najid A (2016) Energy-efficient fuzzy logic cluster head selection in wireless sensor networks. In: 2016 international conference on information technology for organizations development (IT4OD). IEEEGoogle Scholar
  9. Enami N, Moghadam RA (2010) Energy based clustering self organizing map protocol for extending wireless sensor networks lifetime and coverage. Can J Multimed Wirel Netw 1(4):42–54Google Scholar
  10. Frey H, Rührup S, Stojmenović I (2009) Routing in wireless sensor networks. In: Misra S, Woungang I, Misra S (eds) Guide to wireless sensor networks. Springer, London, pp 81–111Google Scholar
  11. Gadai SR, Kumar S (2016) Centroid cluster head selection in wireless sensor network. Int J Adv Res Comput Sci Softw Eng 6(7):101–106Google Scholar
  12. Gao T, Jin RC, Song JY, Xu TB, Wang LD (2012) Energy-efficient cluster head selection scheme based on multiple criteria decision making for wireless sensor networks. Wirel Pers Commun 63(4):871–894Google Scholar
  13. Gou H, Yoo Y (2010) An energy balancing LEACH algorithm for wireless sensor networks. In: 7th international conference on information technology: new generations (ITNG). IEEE, pp 822–827Google Scholar
  14. Gustav YH, Wang Y, Domenic MK, Zhang F, Memon I (2013) Velocity similarity anonymization for continuous query location based services. In: 2013 international conference on computational problem-solving (ICCP), pp 433–436Google Scholar
  15. Hasan MK, Ismail AF, Islam S et al (2018) A novel HGBBDSA-CTI approach for subcarrier allocation in heterogeneous network. Telecommun Syst 2018:1–18Google Scholar
  16. Ian HW, Eibe F, Mark AH (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Elsevier, Amsterdam. ISBN 978-0-12-374856-0zbMATHGoogle Scholar
  17. Jamro DA, Hong J, Bah MH, Mangi FA, Memon I (2016) Triangular antenna with novel techniques for RCS reduction applications. In: Zeng QA (ed) Wireless communications, Networking and applications. Lecture notes in electrical engineering, vol 348, pp 775–782Google Scholar
  18. Karaboga D, Okdem S, Ozturk C (2012) Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel Netw 18:847–860Google Scholar
  19. Lakshmeesha P, Shiva Murthy G (2016) Dynamic cluster head selection mechanism for wireless sensor networks. Int J Eng Comput Sci 5(9):17884–17888Google Scholar
  20. Lee C, Jeong T (2011) FRCA: a fuzzy relevance-based cluster head selection algorithm for wireless mobile ad-hoc sensor networks. Sensors 11(5):5383–5401Google Scholar
  21. Mangi FA, Xiao S, Mallah GA, Jamro DA, Memon I, Kakepoto GF (2016) Multiband circular polarizer based on fission transmission of linearly polarized wave for X-band applications. J Electr Comput Eng 2016Google Scholar
  22. Mangi FA, Xiao S, Yao Z, Memon I, Kakepoto GF (2018) Dual-band asymmetric circular polariser based on fission transmission of linearly polarised wave. IET Microw Antennas Propag 12(8):1414–1419Google Scholar
  23. Memon I (2018) Distance and clustering-based energy-efficient pseudonyms changing strategy over road network. Int J Commun Syst 2018:e3704Google Scholar
  24. Memon I, Arain QA (2017) Dynamic path privacy protection framework for continuous query service over road networks. World Wide Web 20(4):639–672Google Scholar
  25. Memon I, Chen L, Majid A et al (2015) Travel recommendation using geo-tagged photos in social media for tourist. Wireless Pers Commun 80(4):1347–1362Google Scholar
  26. Memon I, Arain QA, Memon MH, Mangi FA, Akhtar R (2017a) Search me if you can: multiple mix zones with location privacy protection for mapping services. Int J Commun Syst. Google Scholar
  27. Memon I, Ali Q, Zubedi A et al (2017b) DPMM: dynamic pseudonym-based multiple mix-zones generation for mobile traveler. Multimed Tools Appl 76(22):24359–24388Google Scholar
  28. Memon I, Chen L, Arain QA, Memon H, Chen G (2018) Pseudonym changing strategy with multiple mix zones for trajectory privacy protection in road networks. Int J Commun Syst 31(1):e3437Google Scholar
  29. Peng S, Liu A, Song L, Memon I, Wang H (2018) Spectral efficiency maximization for deliberate clipping-based multicarrier faster-than-Nyquist signaling. IEEE Access 6:13617–13623Google Scholar
  30. Pitchai R, Jayashri S, Raja J (2016) Searchable encrypted data file sharing method using public cloud service for secure storage in cloud computing. J Wirel Pers Commun 90(2):947–960Google Scholar
  31. Praveena A, Smys S (2016) Efficient cryptographic approach for data security in wireless sensor networks using MES VU. In: 2016 10th international conference on intelligent systems and control (ISCO), 7 Jan 2016. IEEE, pp 1–6Google Scholar
  32. Rajasekaran A, Nagarajan V (2016) Improved cluster head selection for energy efficient data aggregation in sensor networks. Int J Appl Eng Res 11(2):1379–1385Google Scholar
  33. Singh MP (2010) A survey of energy-efficient hierarchical cluster-based routing in wireless sensor networks. Int J Adv Netw Appl 02(02):570–580Google Scholar
  34. Singh SP, Sharma SC (2015) A survey on cluster based routing protocols in wireless sensor networks. Procedia Comput Sci 2015:687–695Google Scholar
  35. Smys S, Bala GJ, Raj JS (2009) Construction of virtual backbone to support mobility in MANET—a less overhead approach. In: 2009 international conference on application of information and communication technologies, 14 Oct 2009. IEEE, pp 1–4Google Scholar
  36. Smys S, Bala GJ, Raj JS (2010) Self-organizing hierarchical structure for wireless networks. In: 2010 international conference on advances in computer engineering, 20 June 2010. IEEE, pp 268–270Google Scholar
  37. Vimalarani C, Subramanian R, Sivanandam SN (2016) An enhanced PSO-based clustering energy optimization algorithm for wireless sensor network. Sci World J 2016:1–11Google Scholar
  38. Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on IEEE tools with artificial intelligence (ICTAI). IEEE, pp 853–858Google Scholar
  39. Wang J, Wang H, Zhou Y, McDonald N (2015) Multiple kernel multivariate performance learning using cutting plane algorithm. In: 2015 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1870–1875Google Scholar
  40. Zhao X, Abdo AMA et al (2018) Dimension reduction of channel correlation matrix using CUR-decomposition technique for 3-D massive antenna system. IEEE Access 6:3031–3039Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAdhiparasakthi Engineering CollegeMelmaruvathurIndia
  2. 2.Department of Electronics and Communication EngineeringAdhiparasakthi Engineering CollegeMelmaruvathurIndia

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