Skip to main content

Advertisement

Log in

An energy-aware clustering method in the IoT using a swarm-based algorithm

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) is a set of interrelated devices on the Internet platform. It can receive and send data to make human life more efficient and convenient. The main challenge in the IoT network is energy consumption in nodes. Clustering is a proper data collection method in the IoT that selectively reduces energy consumption by forming IoT nodes into clusters. The Cluster Head (CH) can control all Cluster Member (CM) nodes, and all intra-cluster and inter-cluster connections are made through it. Today, metaheuristic algorithms solve many problems, including clustering, because they have good performance and are noticeable practical effects. This paper uses the artificial fish swarm algorithm, an effective algorithm to solve optimization problems based on imitation of fish behavior. The cost function contains the residual energy of the nodes, the sum of the distances, and the degree of each node. The simulation results on the dataset showed that the proposed method increases network lifetime value by at least 12.5% and reduces latency by at least 14%.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Xiao, N., et al. (2021). A diversity-based selfish node detection algorithm for socially aware networking. Journal of Signal Processing Systems, 93(7), 811–825.

    Article  Google Scholar 

  2. Lv, Z., Qiao, L., Li, J., & Song, H. (2020). Deep-learning-enabled security issues in the internet of things. IEEE Internet of Things Journal, 8(12), 9531–9538.

    Article  Google Scholar 

  3. Lv, Z., Lou, R., Li, J., Singh, A. K., & Song, H. (2021). Big data analytics for 6G-enabled massive internet of things. IEEE Internet of Things Journal, 8(7), 5350–5359.

    Article  Google Scholar 

  4. Sefati, S., & Navimipour, J. N. (2022). A QoS-aware service composition mechanism in the Internet of things using a hidden Markov model-based optimization algorithm. IEEE Internet of Things Journal, 8, 15620–15627.

    Article  Google Scholar 

  5. Zhang, J., Shen, C., Su, H., Arafin, M. T., and Qu, G. (2021). Voltage over-scaling-based lightweight authentication for IoT security. IEEE Transactions on Computers.

  6. Cai, K., Chen, H., Ai, W., Miao, X., Lin, Q., & Feng, Q. (2021). Feedback convolutional network for intelligent data fusion based on near-infrared collaborative IoT technology. IEEE Transactions on Industrial Informatics, 18, 1200–1209.

    Article  Google Scholar 

  7. Li, B., Liang, R., Zhou, W., Yin, H., Gao, H., and Cai, K. (2021). LBS meets blockchain: an efficient method with security preserving trust in SAGIN. IEEE Internet of Things Journal.

  8. Lv, Z., Qiao, L., & Song, H. (2020). Analysis of the security of internet of multimedia things. ACM Transactions on Multimedia Computing, Communications, and Applications (ToMM), 16(3), 1–16.

    Article  Google Scholar 

  9. Weng, L., He, Y., Peng, J., Zheng, J., & Li, X. (2021). Deep cascading network architecture for robust automatic modulation classification. Neurocomputing, 455, 308–324.

    Article  Google Scholar 

  10. Yi, H. (2021) Secure social internet of things based on post-quantum blockchain. IEEE Transactions on Network Science and Engineering.

  11. Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020). The future of healthcare internet of things: A survey of emerging technologies. IEEE Communications Surveys & Tutorials, 22(2), 1121–1167.

    Article  Google Scholar 

  12. Hajiheidari, S., Wakil, K., Badri, M., & Navimipour, N. J. (2019). Intrusion detection systems in the Internet of things: A comprehensive investigation. Computer Networks, 160, 165–191.

    Article  Google Scholar 

  13. Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., and Hosseinzadeh, M. Clustered routing method in the internet of things using a moth-flame optimization algorithm. International Journal of Communication Systems, e4964.

  14. Mansoor, K., Ghani, A., Chaudhry, S. A., Shamshirband, S., Ghayyur, S. A. K., and Mosavi, A. (2019). Securing IoT-based RFID systems: A robust authentication protocol using symmetric cryptography. Sensors (Switzerland) 19(21), Art. no. 4752.

  15. Sarkeshikian, A., Shafia, M., Zakery, A., and Aliahmadi, A. J. K. (2020). Simulation of stakeholders’ consensus on organizational technology acceptance (case study: Internet of Things). Kybernetes.

  16. Hamzei, M., & Navimipour, N. J. (2018). Toward efficient service composition techniques in the internet of things. IEEE Internet of Things Journal, 5(5), 3774–3787.

    Article  Google Scholar 

  17. Ghanbari, Z., Navimipour, N. J., Hosseinzadeh, M., & Darwesh, A. (2019). Resource allocation mechanisms and approaches on the Internet of Things. Cluster Computing, 22(4), 1253–1282.

    Article  Google Scholar 

  18. Rad, H. J., Abolhassani, B., & Abdizadeh, M. Mathematical analysis of optimal tracking interval management for power efficient target tracking wireless sensor networks.

  19. Sadrishojaei, M., et al. (2021). A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Computing, 1–11.

  20. Choudhury, N., Matam, R., Mukherjee, M., Lloret, J., and Kalaimannan, E. (2020). NCHR: A Non-threshold-based cluster-head rotation scheme for IEEE 802.15. 4 Cluster-tree Networks. IEEE Internet of Things Journal. 1–6.

  21. Amirinasab, M., Shamshirband, S., Chronopoulos, A. T., Mosavi, A., and Nabipour, N. (2020). Energy-efficient method for wireless sensor networks low-power radio operation in internet of things. (in English). Electronics (Switzerland), 9(2), Art. no. 320.

  22. Hady, A. A. (2020). Duty cycling centralized hierarchical routing protocol with content analysis duty cycling mechanism for wireless sensor networks. Computer Systems Science And Engineering, 35(5), 347–355.

    Article  Google Scholar 

  23. Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2021). A New Preventive Routing Method Based on Clustering and Location Prediction in the Mobile Internet of Things. IEEE Internet of Things Journal, 8, 10652–10664.

    Article  Google Scholar 

  24. Latiff, N. A., Tsimenidis, C. C, and Sharif, B. S. (2007). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In 2007 IEEE 18th international symposium on personal, indoor and mobile radio communications, pp. 1–5: IEEE.

  25. Aslani, R., & Rasti, M. (2020). A distributed power control algorithm for energy efficiency maximization in wireless cellular networks. IEEE Wireless Communications Letters, 9(11), 1975–1979.

    Article  Google Scholar 

  26. Ni, T., Liu, D., Xu, Q., Huang, Z., Liang, H., & Yan, A. (2020). Architecture of cobweb-based redundant TSV for clustered faults. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 28(7), 1736–1739.

    Article  Google Scholar 

  27. Rajaram, V., and Kumaratharan, N. (2020). An optimized clustering using hybrid meta-heuristic approach for wireless sensor networks. International Journal of Communication Systems, 33(18).

  28. Rani, S., and Ahmed, S. H. (2015). Multi-hop routing in wireless sensor networks: An overview, taxonomy, and research challenges.

  29. Hu, L., Hong, G., Ma, J., Wang, X., & Chen, H. (2015). An efficient machine learning approach for diagnosis of paraquat-poisoned patients. Computers in Biology and Medicine, 59, 116–124.

    Article  Google Scholar 

  30. Wang, M., et al. (2017). Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing, 267, 69–84.

    Article  Google Scholar 

  31. Zhang, Y. and Wang, Y. (2020) A novel energy-aware bio-inspired clustering scheme for IoT communication. Journal of Ambient Intelligence and Humanized Computing, pp. 1–10.

  32. Neshat, M., Adeli, A., Sepidnam, G., Sargolzaei, V., and Toosi, A. N. (2017). A review of artificial fish swarm optimization methods and applications. International Journal on Smart Sensing and Intelligent Systems, 5(1).

  33. Reddy, M. P. K., & Babu, M. R. (2019). A hybrid cluster head selection model for Internet of Things. Cluster Computing, 22(6), 13095–13107.

    Google Scholar 

  34. Hriez, S., Almajali, S., Elgala, H., Ayyash, M., and Salameh, H. B. (2021). A novel trust-aware and energy-aware clustering method that uses stochastic fractal search in IoT-enabled wireless sensor networks. IEEE Systems Journal, 1–12.

  35. Shukla, A., and Tripathi, S. (2020). A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network. Wireless Networks, pp. 1–23.

  36. Aziz, A., Osamy, W., Khedr, A. M., El-Sawy, A. A., and Singh, K. (2020). Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs. Wireless Networks, pp. 1–24.

  37. Amutha, S., Kannan, B., and Kanagaraj, M.(2020). Energy‐efficient cluster manager‐based cluster head selection technique for communication networks. International Journal of Communication Systems, pp. e4741.

  38. Thangaramya, K., Kulothungan, K., Logambigai, R., Selvi, M., Ganapathy, S., & Kannan, A. (2019). Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT. Computer Networks, 151, 211–223.

    Article  Google Scholar 

  39. Behera, T. M., Mohapatra, S. K., Samal, U. C., Khan, M. S., Daneshmand, M., & Gandomi, A. H. (2019). Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet of Things Journal, 6(3), 5132–5139.

    Article  Google Scholar 

  40. Saini, T. K., and Sharma, S. (2019). Self-managed access scheme for demand request in TDM/TDMA Star Topology Network. Defence Science Journal, 69(1).

  41. Roshani, M., et al. (2020). Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Journal of Flow Measurement and Instrumentation, 75, 101804.

    Article  Google Scholar 

  42. Sattari, M. A., Roshani, G. H., Hanus, R., & Nazemi, E. (2021). Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique. Measurement, 168, 108474.

    Article  Google Scholar 

  43. Chen, H., Heidari, A. A., Chen, H., Wang, M., Pan, Z., & Gandomi, A. H. (2020). Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies. Future Generation Computer Systems, 111, 175–198.

    Article  Google Scholar 

  44. Zhang, Y., Liu, R., Wang, X., Chen, H., and Li, C. (2020). Boosted binary Harris hawks optimizer and feature selection. Engineering with Computers, pp. 1–30.

  45. Xu, X., & Chen, H.-L. (2014). Adaptive computational chemotaxis based on field in bacterial foraging optimization. Soft Computing, 18(4), 797–807.

    Article  Google Scholar 

  46. Yu, C et al. (2021). SGOA: Annealing-behaved grasshopper optimizer for global tasks. Engineering with Computers, pp. 1–28.

  47. Hu, J., et al. (2021). Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowledge-Based Systems, 213, 106684.

    Article  Google Scholar 

  48. Zhao, X., et al. (2019). Chaos enhanced grey wolf optimization wrapped ELM for diagnosis of paraquat-poisoned patients. Computational biology and chemistry, 78, 481–490.

    Article  Google Scholar 

  49. Shan, W., Qiao, Z., Heidari, A. A., Chen, H., Turabieh, H., & Teng, Y. (2021). Double adaptive weights for stabilization of moth flame optimizer: Balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Systems, 214, 106728.

    Article  Google Scholar 

  50. Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S., & Zhang, X. (2019). Enhanced Moth-flame optimizer with mutation strategy for global optimization. Information Sciences, 492, 181–203.

    Article  MathSciNet  Google Scholar 

  51. Shen, L., et al. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61–75.

    Article  Google Scholar 

  52. Yu, H et al. (2020). Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis. Engineering with Computers, pp. 1–29.

  53. Sun, G., Li, C., and Deng, L. (2021). An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Computing and Applications, pp. 1–17.

  54. Tu, J., et al. (2021). Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance. Knowledge-Based Systems, 212, 106642.

    Article  Google Scholar 

  55. Wang, M., & Chen, H. (2020). Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis. Applied Soft Computing, 88, 105946.

    Article  Google Scholar 

  56. Zhong, S.-Q., Zhao, S.-C., & Zhu, S.-N. (2021). Photovoltaic properties enhanced by the tunneling effect in a coupled quantum dot photocell. Results in Physics, 24, 104094.

    Article  Google Scholar 

  57. Zhao, X., Li, D., Yang, B., Ma, C., Zhu, Y., & Chen, H. (2014). Feature selection based on improved ant colony optimization for online detection of foreign fiber in cotton. Applied Soft Computing, 24, 585–596.

    Article  Google Scholar 

  58. Li, X.-L. (2002). An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering-Theory & Practice, 22(11), 32–38.

    Google Scholar 

  59. Neshat, M., Sepidnam, G., Sargolzaei, M., & Toosi, A. N. (2014). Artificial fish swarm algorithm: A survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial intelligence review, 42(4), 965–997.

    Article  Google Scholar 

  60. Gorgich, S., and Tabatabaei, S. (2021). Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (Wireless Sensor Networks). Wireless Personal Communications, pp. 1–21.

  61. Li, X., Keegan, B., & Mtenzi, F. (2018). Energy efficient hybrid routing protocol based on the artificial fish swarm algorithm and ant colony optimisation for WSNs. Sensors, 18(10), 3351.

    Article  Google Scholar 

  62. Mechta, D., Harous, S. (2019). Clustering in WSNs based on Artificial Fish Swarming Algorithm. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 161–167: IEEE.

  63. Zainal, N., Zain, A. M., & Sharif, S. (2015). Overview of artificial fish swarm algorithm and its applications in industrial problems. Applied Mechanics and Materials, 815, 253–257.

    Article  Google Scholar 

  64. Rao, P. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless networks, 23(7), 2005–2020.

    Article  Google Scholar 

  65. Reddy, M. P. K., & Babu, M. R. (2019). Implementing self adaptiveness in whale optimization for cluster head section in Internet of Things. Cluster Computing, 22(1), 1361–1372.

    Article  Google Scholar 

  66. Bounceur, A. et al. (2018). Cupcarbon-lab: An iot emulator. In 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–2: IEEE.

  67. Bounceur, A. (2016). CupCarbon: a new platform for designing and simulating smart-city and IoT wireless sensor networks (SCI-WSN). In Proceedings of the International Conference on Internet of things and Cloud Computing, pp. 1–1.

  68. Sun, G., Cong, Y., Dong, J., Liu, Y., Ding, Z., and Yu, H. (2021). What and How: Generalized lifelong spectral clustering via dual memory. IEEE Transactions on Pattern Analysis and Machine Intelligence.

  69. Sun, G., Cong, Y., Wang, Q., Zhong, B., and Fu, Y. (2020). Representative task self-selection for flexible clustered lifelong learning. IEEE Transactions on Neural Networks and Learning Systems, 1–15.

  70. Osamy, W., El-Sawy, A. A., & Salim, A. (2020). CSOCA: Chicken swarm optimization based clustering algorithm for wireless sensor networks. IEEE Access, 8, 60676–60688.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nima Jafari Navimipour.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M. et al. An energy-aware clustering method in the IoT using a swarm-based algorithm. Wireless Netw 28, 125–136 (2022). https://doi.org/10.1007/s11276-021-02804-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-021-02804-x

Keywords

Navigation