Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, particle swarm optimisation has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on ant-colony optimisation has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab simulations.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Abo-Zahhad, M., Ahmed, S. M., Sabor, N., & Sasaki, S. (2015). Mobile sink-based adaptive immune energy-efficient clustering protocol for improving the lifetime and stability period of wireless sensor networks. IEEE Sensors Journal, 15(8), 4576–4586.

    Article  Google Scholar 

  2. Abuarqoub, A., Hammoudeh, M., Adebisi, B., Jabbar, S., Bounceur, A., & Al-Bashar, H. (2017). Dynamic clustering and management of mobile wireless sensor networks. Computer Networks, 117, 62–75.

    Article  Google Scholar 

  3. Ahmad, A., Javaid, N., Khan, Z. A., Qasim, U., & Alghamdi, T. A. (2014). (ACH)2: Routing scheme to maximize lifetime and throughput of wireless sensor networks. IEEE Sensors Journal, 14(10), 3516–3532.

    Article  Google Scholar 

  4. Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.

    Article  Google Scholar 

  5. Almi’ani, K., Viglas, A., & Libman, L. (2010). Energy-efficient data gathering with tour length-constrained mobile elements in wireless sensor networks. In IEEE 35th conference on local computer networks (LCN), 2010 (pp. 582–589).

  6. Bagci, H., & Yazici, A. (2013). An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Applied Soft Computing, 13(4), 1741–1749.

    Article  Google Scholar 

  7. Bojan-Dragos, C. A., Precup, R. E., Preitl, S., Szedlak-Stinean, A. I., & Petriu, E. M. (2016). Particle swarm optimization of fuzzy models for electromagnetic actuated clutch systems. In 18th Mediterranean Electrotechnical Conference (MELECON), 2016 (pp. 1–6).

  8. Butun, I., Morgera, S. D., & Sankar, R. (2014). A survey of intrusion detection systems in wireless sensor networks. IEEE Communications Surveys Tutorials, 16(1), 266–282.

    Article  Google Scholar 

  9. Chanak, P., Banerjee, I., Wang, J., & Sherratt, R. S. (2014). Obstacle avoidance routing scheme through optimal sink movement for home monitoring and mobile robotic consumer devices. IEEE Transactions on Consumer Electronics, 60(4), 596–604.

    Article  Google Scholar 

  10. Chang, J. Y., & Shen, T. H. (2016). An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sensors Journal, 16(20), 7545–7557.

    Article  Google Scholar 

  11. Collotta, M., Pau, G., & Maniscalco, V. (2017). A fuzzy logic approach by using particle swarm optimization for effective energy management in IWSNs. Transactions on Industrial Electronics, 99, 1.

    Google Scholar 

  12. Gajjar, S., Sarkar, M., & Dasgupta, K. (2016). Famacrow: Fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Applied Soft Computing, 43, 235–247.

    Article  Google Scholar 

  13. Gao, S., Zhang, H., & Das, S. K. (2011). Efficient data collection in wireless sensor networks with path-constrained mobile sinks. IEEE Transactions on Mobile Computing, 10(4), 592–608.

    Article  Google Scholar 

  14. Ghosh, N., & Banerjee, I. (2015). An energy-efficient path determination strategy for mobile data collectors in wireless sensor network. Computers & Electrical Engineering, 48, 417–435.

    Article  Google Scholar 

  15. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000, (Vol. 2, p. 10).

  16. Hu, Y., & Niu, Y. (2016). An energy-efficient overlapping clustering protocol in WSNs. Wireless Networks, 1–17.

  17. Huang, G., Chen, D., & Liu, X. (2015). A node deployment strategy for blindness avoiding in wireless sensor networks. IEEE Communications Letters, 19(6), 1005–1008.

    Article  Google Scholar 

  18. Izadi, D., Abawajy, J., & Ghanavati, S. (2015). An alternative clustering scheme in WSN. IEEE Sensors Journal, 15(7), 4148–4155.

    Article  Google Scholar 

  19. Izakian, H., & Abraham, A. (2011). Fuzzy c-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications, 38(3), 1835–1838.

    Article  Google Scholar 

  20. Kang, S. H., & Nguyen, T. (2012). Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Communications Letters, 16(9), 1396–1399.

    Article  Google Scholar 

  21. Kennedy, J. & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, 1995. (Vol. 4).

  22. Kim, K. T., & Youn, H. Y. (2017). A dynamic level-based routing protocol for energy efficiency in wireless sensor networks. Journal of Internet Technology, 18(1), 11–21.

    Google Scholar 

  23. Kim, J. M., Park, S. H., Han, Y. J., & Chung, T. M. (2008). Chef: Cluster head election mechanism using fuzzy logic in wireless sensor networks. In 10th International conference on advanced communication technology, 2008. ICACT 2008 (Vol. 1, pp. 654–659).

  24. Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.

    Article  Google Scholar 

  25. Lee, J. S., & Cheng, W. L. (2012). Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sensors Journal, 12(9), 2891–2897.

    Article  Google Scholar 

  26. Lee, J. W., Choi, B. S., & Lee, J. J. (2011). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7(3), 419–427.

    Article  Google Scholar 

  27. Lee, J. W., & Lee, J. J. (2012). Ant-colony-based scheduling algorithm for energy-efficient coverage of WSN. IEEE Sensors Journal, 12(10), 3036–3046.

    Article  Google Scholar 

  28. Ma, Y., Guo, Y., Tian, X., & Ghanem, M. (2011). Distributed clustering-based aggregation algorithm for spatial correlated sensor networks. IEEE Sensors Journal, 11(3), 641–648.

    Article  Google Scholar 

  29. Ma, M., & Yang, Y. (2007). Sencar: An energy-efficient data gathering mechanism for large-scale multihop sensor networks. IEEE Transactions on Parallel and Distributed Systems, 18(10), 1476–1488.

    Article  Google Scholar 

  30. Ma, M., Yang, Y., & Zhao, M. (2013). Tour planning for mobile data-gathering mechanisms in wireless sensor networks. IEEE Transactions on Vehicular Technology, 62(4), 1472–1483.

    Article  Google Scholar 

  31. Manjeshwar, A., & Agrawal, D. P. (2001). Teen: a routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th international parallel and distributed processing symposium, IPDPS 2001 (pp. 2009–2015).

  32. Manjeshwar, A., & Agrawal, D. P. (2002). Apteen: A hybrid protocol for efficient routing and comprehensive information retrieval in wireless. In Proceedings 16th international parallel and distributed processing symposium (p. 8).

  33. Mirzaie, M. & Mazinani, S. M. (2017). MCFL: An energy efficient multi-clustering algorithm using fuzzy logic in wireless sensor network. Wireless Networks, 1–16.

  34. Nabi, A., & Singh, N. A. (2016). Particle swarm optimization of fuzzy logic controller for voltage sag improvement. In 3rd International conference on advanced computing and communication systems (ICACCS), 2016 (Vol. 01, pp. 1–5).

  35. Nayak, P., & Devulapalli, A. (2016). A fuzzy logic-based clustering algorithm for wsn to extend the network lifetime. IEEE Sensors Journal, 16(1), 137–144.

    Article  Google Scholar 

  36. 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 Transactions on Computational Biology and Bioinformatics, 14(1), 76–84.

    Article  Google Scholar 

  37. Prasad, D. R., Naganjaneyulu, P. V., & Prasad, K. S. (2017). A hybrid swarm optimization for energy efficient clustering in multi-hop wireless sensor network. Wireless Personal Communications, 94(4), 2459–2471.

    Article  Google Scholar 

  38. Rao, P. C. S., & Banka, H. (2017). Energy efficient clustering algorithms for wireless sensor networks: Novel chemical reaction optimization approach. Wireless Networks, 23(2), 433–452.

    Article  Google Scholar 

  39. Safaee, B., & Mashhadi, S. K. M. (2016). Fuzzy membership functions optimization of fuzzy controllers for a quad rotor using particle swarm optimization and genetic algorithm. In 4th International conference on control, instrumentation, and automation (ICCIA), 2016 (pp. 256–261).

  40. Salarian, H., Chin, K. W., & Naghdy, F. (2014). An energy-efficient mobile-sink path selection strategy for wireless sensor networks. IEEE Transactions on Vehicular Technology, 63(5), 2407–2419.

    Article  Google Scholar 

  41. Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003). Data mules: Modeling a three-tier architecture for sparse sensor networks. In Proceedings of the 1st IEEE international workshop on sensor network protocols and applications, 2003 (pp. 30–41).

  42. Shigei, N., Morishita, H., & Miyajima, H. (2010). Energy efficient clustering communication based on number of neighbors for wireless sensor networks. In Proceedings of international multiconference of engineers and computer scientists. Berlin: Springer.

  43. Silva Filho, T. M., Pimentel, B. A., Souza, R. M., & Oliveira, A. L. (2015). Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Systems with Applications, 42(17), 6315–6328.

    Article  Google Scholar 

  44. Somasundara, A. A., Ramamoorthy, A., & Srivastava, M. B. (2007). Mobile element scheduling with dynamic deadlines. IEEE Transactions on Mobile Computing, 6(4), 395–410.

    Article  Google Scholar 

  45. Soufi, Y., Bechouat, M., & Kahla, S. (2016). Fuzzy controller design using particle swarm optimization for photovoltaic maximum power point tracking. In International smart grid workshop and certificate program (ISGWCP), 2016 (pp. 1–6).

  46. Taheri, H., Neamatollahi, P., Younis, O. M., Naghibzadeh, S., & Yaghmaee, M. H. (2012). An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Networks, 10(7), 1469–1481.

    Article  Google Scholar 

  47. Thakkar, A., & Kotecha, K. (2015). A new bollinger band based energy efficient routing for clustered wireless sensor network. Applied Soft Computing, 32, 144–153.

    Article  Google Scholar 

  48. Wang, J., Cao, J., Li, B., Lee, S., & Sherratt, R. S. (2015). Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Transactions on Consumer Electronics, 61(4), 438–444.

    Article  Google Scholar 

  49. Wang, C., Guo, S., & Yang, Y. (2016). An optimization framework for mobile data collection in energy-harvesting wireless sensor networks. IEEE Transactions on Mobile Computing, 15(12), 2969–2986.

    Article  Google Scholar 

  50. Wang, J., Yin, Y., Zhang, J., Lee, S., & Sherratt, R. S. (2013). Mobility based energy efficient and multi-sink algorithms for consumer home networks. IEEE Transactions on Consumer Electronics, 59(1), 77–84.

    Article  Google Scholar 

  51. Wang, J., Cao, Y., Li, B., Kim, H. J., & Lee, S. (2016). Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Generation Computer Systems.

  52. Younis, O., & Fahmy, S. (2004). Heed: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  53. Yu, G., Ren, F., Ji, Y., & Li, J. (2016). The evolution of sink mobility management in wireless sensor networks: A survey. IEEE Communications Surveys Tutorials, 18(1), 507–524.

    Article  Google Scholar 

  54. Zhao, M., Gong, D., & Yang, Y. (2015). Network cost minimization for mobile data gathering in wireless sensor networks. IEEE Transactions on Communications, 63(11), 4418–4432.

    Article  Google Scholar 

  55. Zualkernan, I. A., Al-ali, A. R., Jabbar, M. A., Zabalawi, I., & Wasfy, A. (2009). Infopods: Zigbee-based remote information monitoring devices for smart-homes. IEEE Transactions on Consumer Electronics, 55(3), 1221–1226.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Nimisha Ghosh & Indrajit Banerjee

  2. School of Systems Engineering, University of Reading, Berkshire, RG6 6AY, UK

    R. Simon Sherratt

Authors
  1. Nimisha Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Indrajit Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Simon Sherratt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nimisha Ghosh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosh, N., Banerjee, I. & Sherratt, R.S. On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network. Wireless Netw 25, 1829–1845 (2019). https://doi.org/10.1007/s11276-017-1635-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1635-6

Keywords

Search

Navigation