Wireless Networks

, Volume 25, Issue 8, pp 4859–4871 | Cite as

Dynamic clustering approach with ACO-based mobile sink for data collection in WSNs

  • Muralitharan Krishnan
  • Sangwoon Yun
  • Yoon Mo JungEmail author


Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly.


Wireless sensor networks Dynamic clustering Network lifetime Optimization techniques 



This work was supported by the National Research Foundation of Korea NRF-2016R1A5A1008055. The corresponding author was supported by NRF-2016R1D1A1B03931337. The second author was supported by NRF-2016R1D1A1B03934371.


  1. 1.
    Zhao, M., Yang, Y., & Wang, C. (2015). Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks. IEEE Transactions on Mobile Computing, 14(4), 770–785.Google Scholar
  2. 2.
    Ji, L., Yang, Y., & Wang, W. (2015). Mobility assisted data gathering with solar irradiance awareness in heterogeneous energy replenishable wireless sensor networks. Computer Communications, 69, 88–97.Google Scholar
  3. 3.
    Dong, M., Liu, X., Qian, Z., Liu, A., & Wang, T. (2015). QoE-ensured price competition model for emerging mobile networks. IEEE Wireless Communications, 22(4), 50–57.Google Scholar
  4. 4.
    Cayirpunar, O., Kadioglu-Urtis, E., & Tavli, B. (2015). Optimal base station mobility patterns for wireless sensor network lifetime maximization. IEEE Sensors Journal, 15(11), 6592–6603.Google Scholar
  5. 5.
    Fadel, E., Gungor, V. C., Nassef, L., Akkari, N., Abbas Malik, M. G., Almasri, S., et al. (2015). A survey on wireless sensor networks for smart grid. Computer Communications, 71, 22–33.Google Scholar
  6. 6.
    Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network & Computer Applications, 60(6), 192–219.Google Scholar
  7. 7.
    Liu, Y., Xiong, N., Zhao, Y., Vasilakos, A. V., Gao, J., & Jia, Y. (2010). Multi-layer clustering routing algorithm for wireless vehicular sensor networks. IET Communications, 4(7), 810–816.Google Scholar
  8. 8.
    Sharmin, S., Nur, F. N., Razzaque, M. A., Rahman, M. M., Almogren, A., & Hassan, M. M. (2017). Tradeoff between sensing quality and network lifetime for heterogeneous target coverage using directional sensor nodes. IEEE Access, 5, 15490–15504.Google Scholar
  9. 9.
    Ullah, R., Faheem, Y., & Kim, B. S. (2017). Energy and congestion-aware routing metric for smart grid AMI networks in smart city. IEEE Access, 5, 13799–13810.Google Scholar
  10. 10.
    Deif, D. S., & Gadallah, Y. (2014). Classification of wireless sensor networks deployment techniques. IEEE Communications Surveys & Tutorials, 16(2), 834–855.Google Scholar
  11. 11.
    Krishnan, M., Rajagopal, V., & Rathinasamy, S. (2016). Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs. Wireless Networks. Scholar
  12. 12.
    Almobaideen, W., Hushaidan, K., Sleit, A., & Qatawneh, M. (2011). A cluster based approach for supporting qos in mobile adhoc networks. International Journal of Digital Content Technology and its Applications, 5(1), 1–9.Google Scholar
  13. 13.
    Patil, P., & Kulkarni, U. (2013). Analysis of data aggregation techniques in wireless sensor networks. International Journal of Computational Engineering & Management, 16(1), 22–27.Google Scholar
  14. 14.
    Kallapur, P. V., & Geetha, V. (2011). Research challenges in using mobile agents for data aggregation in wireless sensor networks with dynamic deadlines. International Journal of Computer Applications, 30(5), 34–38.Google Scholar
  15. 15.
    Xu, J., Liu, W., Lang, F., Zhang, Y., & Wang, C. (2010). Distance measurement model based on RSSI in WSN. Wireless Sensor Networks, 2(8), 606–611.Google Scholar
  16. 16.
    Maraiya, K., Kant, K., & Gupta, N. (2011). Architectural based data aggregation techniques in wireless sensor network: A comparative study. International Journal on Computer Science & Engineering, 3(3), 6599–6605.Google Scholar
  17. 17.
    Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673–687.Google Scholar
  18. 18.
    Chilamkurti, N., Zeadally, S., Vasilakos, A., & Sharma, V. (2009). Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, 2009, 1–9.Google Scholar
  19. 19.
    Azharuddin, M., & Jana, P. K. (2016). A PSO based fault tolerant routing algorithm for wireless sensor networks. Wireless Networks, 22(8), 2637–2647.Google Scholar
  20. 20.
    Han, G., Qian, A., Jiang, J., Sun, N., & Liu, L. (2016). A grid-based joint routing and charging algorithm for industrial wireless rechargeable sensor networks. Computer Networks, 101, 19–28.Google Scholar
  21. 21.
    Song, Y., Liu, L., Ma, H., & Vasilakos, A. V. (2014). A Biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Transactions on Network & Service Management, 11(3), 417–430.Google Scholar
  22. 22.
    Wang, Y. C., Wu, F. J., & Tseng, Y. C. (2012). Mobility management algorithms and applications for mobile sensor networks. Wireless Communications & Mobile Computing, 12(1), 7–21.Google Scholar
  23. 23.
    Cobo, L., Quintero, A., & Pierre, S. (2010). Ant-based routing for wireless multimedia sensor networks using multiple QoS. Computer Networks, 54(17), 2991–3010.Google Scholar
  24. 24.
    Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.Google Scholar
  25. 25.
    Hamida, E. B., & Chelius, G. (2008). Strategies for data dissemination to mobile sinks in wireless sensor networks. IEEE Wireless Communications, 15(6), 31–37.Google Scholar
  26. 26.
    Yun, Y., & Xia, Y. (2010). Maximizing the lifetime of wireless sensor networks with mobile sink in delay-tolerant applications. IEEE Transactions on Mobile Computing, 9(9), 1308–1318.Google Scholar
  27. 27.
    Di Francesco, M., Das, S. K., & Anastasi, G. (2011). Data collection in wireless sensor networks with mobile elements: A survey. ACM Transactions on Sensor Networks (TOSN), 8(11), 7. Scholar
  28. 28.
    Sara, G., Kalaiarasi, R., Pari, N., & Sridharan, D. (2010). Energy efficient clustering and routing in mobile wireless sensor network. International Journal of Wireless and Mobile Networks, 2(4), 106–114.Google Scholar
  29. 29.
    Karim, L., & Nasser, N. (2012). Reliable location-aware routing protocol for mobile wireless sensor network. IET Communications, 6(14), 2149–2158.Google Scholar
  30. 30.
    Ma, M., Yang, Y., & Zaho, M. (2013). Tour planning for mobile data-gathering mechanisms in wireless sensor networks. IEEE transactions on Vehicular Technology, 62(4), 1472–1482.Google Scholar
  31. 31.
    Kinalis, A., Nikoletseas, S., Patroumpa, D., & Rolim, J. (2014). Biased sink mobility with adaptive stop times for low latency data collection in sensor networks. Information Fusion, 15, 56–63.Google Scholar
  32. 32.
    Arshadlis, M., Kamel, N., Armi, N., & Saad, N. M. (2011). Mobile data collector based routing protocol for wireless sensor networks. Scientific Research and Essays, 6(29), 6162–6175.Google Scholar
  33. 33.
    Kim, J. W., In, J. S., Hur, K., Kim, J. W., & Eom, D. S. (2010). An intelligent agent-based routing structure for mobile sinks in WSNs. IEEE Transactions on Consumer Electronics, 56(4), 2310–2316.Google Scholar
  34. 34.
    Gupta, S. K., & Prasantam, K. J. (2015). Energy efficient clustering and routing algorithms for wireless sensor networks: GA based approach. Wireless Personal Communications, 83(3), 2403–2423.Google Scholar
  35. 35.
    Srinivasa Rao, P. C., Prasanta, K. Jana, & Banka, Haider. (2016). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005–2020.Google Scholar
  36. 36.
    Chen, T. S., Tsai, H. W., Chang, Y. H., & Chen, C. T. (2013). Geographic convergecast using mobile sink in wireless sensor networks. Computer communications, 36(4), 445–458.Google Scholar
  37. 37.
    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.Google Scholar
  38. 38.
    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 Transaction on Consumer Electronics, 61(4), 438–444.Google Scholar
  39. 39.
    Heinzelman, W. R., Chandrakasan. A., & Balakishnan, H. (2002). Energy efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, pp. 8020–8024.Google Scholar
  40. 40.
    Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36(2), 623–645.Google Scholar
  41. 41.
    Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks, 5(1), 1–38.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.AORCSungkyunkwan UniversitySuwonSouth Korea
  2. 2.Department of Mathematics EducationSungkyunkwan UniversitySeoulSouth Korea
  3. 3.Department of MathematicsSungkyunkwan UniversitySuwonSouth Korea

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