Wireless Networks

, Volume 25, Issue 8, pp 4459–4477 | Cite as

LiMCA: an optimal clustering algorithm for lifetime maximization of internet of things

  • Subir HalderEmail author
  • Amrita Ghosal
  • Mauro Conti


The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches.


Internet of things Energy balance Network lifetime Static clustering Wireless sensor network 


  1. 1.
    Atzori, L., Iera, A., & Morabito, G. (2017). Understanding the internet of things: Definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122–140.CrossRefGoogle Scholar
  2. 2.
    Fuqaha, A. A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys and Tutorials, 17(4), 2347–2376.CrossRefGoogle Scholar
  3. 3.
    Xu, L., Collier, R., & O’Hare, G. M. P. (2017). A survey of clustering techniques in WSN and consideration of the challenges of applying such to 5G IoT scenarios. IEEE Internet of Things Journal, 4(5), 1229–1249.CrossRefGoogle Scholar
  4. 4.
    Anastasi, G., Conti, M., Francesco, M., & Passarella, A. (2009). Energy conservation in wireless sensor networks: A survey. Ad Hoc Networks, 7(3), 537–568.CrossRefGoogle Scholar
  5. 5.
    Li, Q. Q., Gochhayat, S. P., Conti, M., & Liu, F. A. (2017). EnergIoT: A solution to improve network lifetime of IoT devices. Pervasive and Mobile Computing, 42, 124–133.CrossRefGoogle Scholar
  6. 6.
    Younis, O., Krunz, M., & Ramasubramanian, S. (2006). Node clustering in wireless sensor networks: Recent developments and deployment challenges. IEEE Network, 20(3), 20–25.CrossRefGoogle Scholar
  7. 7.
    Afsar, M. M., & Tayarani-N, M. H. (2014). Clustering in sensor networks: A literature survey. Journal of Network and Computer Applications, 46, 198–226.CrossRefGoogle Scholar
  8. 8.
    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.CrossRefGoogle Scholar
  9. 9.
    Lin, H., & Uster, H. (2014). Exact and heuristic algorithms for data-gathering cluster-based wireless sensor network design problem. IEEE/ACM Transactions on Networking, 22(3), 903–916.CrossRefGoogle Scholar
  10. 10.
    Moon, S. H., Park, S., & Han, S. J. (2017). Energy efficient data collection in sink-centric wireless sensor networks: A cluster-ring approach. Computer Communications, 101, 12–25.CrossRefGoogle Scholar
  11. 11.
    Wei, D., Jin, Y., Vural, S., Moessner, K., & Tafazolli, R. (2011). An energy-efficient clustering solution for wireless sensor networks. IEEE Transactions on Wireless Communications, 10(11), 3973–3983.CrossRefGoogle Scholar
  12. 12.
    Li, H., Liu, Y., Chen, W., Jia, W., Li, B., & Xiong, J. (2013). COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256–268.CrossRefGoogle Scholar
  13. 13.
    Shu, T., & Krunz, M. (2010). Coverage-time optimization for clustered wireless sensor networks: A power-balancing approach. IEEE/ACM Transactions on Networking, 18(1), 202–215.CrossRefGoogle Scholar
  14. 14.
    Sanchez, A. J. G., Sanchez, F. G., & Haro, J. G. (2011). Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture, 75(2), 288–303.CrossRefGoogle Scholar
  15. 15.
    Conti, M. (2015). Secure wireless sensor networks: Threats and solutions. Berlin: Springer.CrossRefGoogle Scholar
  16. 16.
    Halder, S., & Ghosal, A. (2015). Lifetime optimizing clustering structure using archimedes spiral based deployment in WSNs. In Proceedings of 14th IFIP/IEEE symposium on integrated network and service management (IM) (pp. 592–598).Google Scholar
  17. 17.
    Baranidharan, B., & Santhi, B. (2016). DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Applied Soft Computing, 40, 495–506.CrossRefGoogle Scholar
  18. 18.
    Halder, S., & Ghosal A. (2015). A predetermined deployment technique for lifetime optimization in clustered wsns. In Proceedings of international conference on algorithms and architectures for parallel processing (ICA3PP), LNCS 9531 (pp. 682–696).Google Scholar
  19. 19.
    Darabkh, K. A., Ismail, S. S., Shurman, M. A., Jafar, I. F., Alkhader, E., & Mistarihi, M. F. A. (2012). Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks. Journal of Network and Computer Applications, 35(6), 2068–2080.CrossRefGoogle Scholar
  20. 20.
    Xu, L., O’Hare, G. M. P., & Collier, R. (2017). A smart and balanced energy-efficient multihop clustering algorithm (smart-beem) for MIMO IoT systems in future networks. Sensors, 17(7), article no. 1574.Google Scholar
  21. 21.
    Lin, H., Wang, L., & Kong, R. (2015). Energy efficient clustering protocol for large-scale sensor networks. IEEE Sensors Journal, 15(12), 7150–7160.CrossRefGoogle Scholar
  22. 22.
    Chen, D. R. (2015). A link-and hop-constrained clustering for multi-hop wireless sensor networks. Computer Communications, 72, 78–92.CrossRefGoogle Scholar
  23. 23.
    Ghosal, A., & Halder, S. (2017). Lifetime optimizing clustering structure using archimedes’ spiral based deployment in WSNs. IEEE Systems Journal, 11(2), 1039–1048.CrossRefGoogle Scholar
  24. 24.
    Kong, L., Xiang, Q., Liu, X., Liu, X. Y., Gao, X., Chen, G., et al. (2016). ICP: Instantaneous clustering protocol for wireless sensor networks. Computer Networks, 101, 144–157.CrossRefGoogle Scholar
  25. 25.
    Ye, M., Li, C., Chen, G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In Proceedings of 24th IEEE international performance, computing, and communications conference (IPCCC) (pp. 535–540).Google Scholar
  26. 26.
    Ye, M., Li, C., Chen, G., & Wu, J. (2007). An energy efficient clustering scheme in wireless sensor networks. Ad Hoc and Sensor Wireless Networks, 3(2–3), 99–119.Google Scholar
  27. 27.
    Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117–131.CrossRefGoogle Scholar
  28. 28.
    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.CrossRefGoogle Scholar
  29. 29.
    Chang, X., Tan, R., Xing, G., Yuan, Z., Lu, C., Chen, Y., et al. (2011). Sensor placement algorithms for fusion-based surveillance networks. IEEE Transaction on Parallel and Distributed Systems, 22(8), 1407–1414.CrossRefGoogle Scholar
  30. 30.
    Bagci, H., Korpeoglu, I., & Yazıcı, A. (2015). A distributed fault-tolerant topology control algorithm for heterogeneous wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 26(4), 914–923.CrossRefGoogle Scholar
  31. 31.
    Zhang, H., & Shen, H. (2009). Balancing energy consumption to maximize network lifetime in data-gathering sensor networks. IEEE Transactions on Parallel and Distributed Systems, 20(10), 1526–1539.CrossRefGoogle Scholar
  32. 32.
    Cotuk, H., Bicakci, K., Tavli, B., & Uzun, E. (2014). The impact of transmission power control strategies on lifetime of wireless sensor networks. IEEE Transactions on Computers, 63(11), 2866–2879.MathSciNetCrossRefGoogle Scholar
  33. 33.
    Zuniga, M., & Krishnamachari, B. (2004). Analyzing the transitional region in low power wireless links. In Proceedings 1st annual IEEE communications society conference on sensor and ad hoc communications and networks (SECON) (pp. 517–526).Google Scholar
  34. 34.
    Li, B., Li, H., Wang, W., Yin, Q., & Liu, H. (2013). Performance analysis and optimization for energy-efficient cooperative transmission in random wireless sensor network. IEEE Transactions on Wireless Communications, 12(9), 4647–4657.CrossRefGoogle Scholar
  35. 35.
    Luenberger, D. G., & Ye, Y. (2008). Linear and nonlinear programming (3rd ed., Vol. 116). New York: Springer.CrossRefGoogle Scholar
  36. 36.
    Barsi, F., Bertossi, A. A., Lavault, C., Navarra, A., Olariu, S., Pinotti, M. C., et al. (2011). Efficient location training protocols for heterogeneous sensor and actor networks. IEEE Transactions on Mobile Computing, 10(3), 377–391.CrossRefGoogle Scholar
  37. 37.
  38. 38.
    Li, X., Yan, S., Xu, C., Nayak, A., & Stojmenovic, I. (2011). Localized delay-bounded and energy-efficient data aggregation in wireless sensor and actor networks. Wireless Communications and Mobile Computing, 11(12), 1603–1617.CrossRefGoogle Scholar
  39. 39.
    Grant, M., & Boyd, S. (2006). CVX: Matlab software for disciplined convex programming, version 2.0 Beta [Online].
  40. 40.
    Halder, S., & Ghosal, A. (2016). A location-wise predetermined deployment for optimizing lifetime in visual sensor networks. IEEE Transactions on Circuits and Systems for Video Technology, 26(6), 1131–1145.CrossRefGoogle Scholar
  41. 41.
    Ishak, R., Xu, Q., Olariu, S., & Salleh, S. (2007). Hybrid training with binary search protocol for wireless sensor networks. Mobile Information Systems, 3(3–4), 233–249.CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of PaduaPaduaItaly

Personalised recommendations