Skip to main content

Advertisement

SpringerLink
Log in

An Energy-Aware IoT Routing Approach Based on a Swarm Optimization Algorithm and a Clustering Technique

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The Internet of Things (IoT) comprises many nodes dispersed around a particular target region, and it has lately been applied in a variety of sectors such as smart cities, farming, climatology, smart metering, waste treatment, and others. Even though the IoT has tremendous potential, some difficulties must be addressed. When building the clustering and routing protocol for huge-scale IoT networks, uniform energy usage and optimization are two significant concerns. Clustering and routing are well-known NP-hard optimization challenges applied to the IoT. The ease with which chicken can be implemented has garnered much interest compared to other population-based metaheuristic algorithms in solving optimization problems in the IoT. Aiming to reduce and improve node energy consumption in the IoT network by choosing the most suitable cluster head, the current effort seeks to extend the life of a network by selecting the most appropriate cluster head. A new cost function for homogenous dispersion of cluster heads was proposed in this research, and a good balance among exploration and exploitation search skills to create a node clustering protocol based on chicken search. This procedure is a big step forward from previous state-of-the-art protocols. The number of packets received, the total power consumption, the number of active nodes, and the latency of the suggested integrated clustered routing protocol are all used to evaluate the protocol's overall performance. The proposed strategy has been demonstrated to improve power consumption by at least 16 percent.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data Availability

Not applicable.

Code Availability

The authors confirm that, if necessary, all software codes will be provided to the magazine and the chief editor.

References

  1. Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., Hosseinzadeh, M. (2021). Clustered routing method in the internet of things using a moth‐flame optimization algorithm. International Journal of Communication Systems, 34(16). https://doi.org/10.1002/dac.4964

  2. Nauman, A., Qadri, Y. A., Amjad, M., Zikria, Y. B., Afzal, M. K., & Kim, S. W. (2020). Multimedia Internet of Things: A comprehensive survey. IEEE Access, 8, 8202–8250.

    Article  Google Scholar 

  3. Sennan, S., Balasubramaniyam, S., Luhach, A. K., Ramasubbareddy, S., Chilamkurti, N., & Nam, Y. (2019). Energy and delay aware data aggregation in routing protocol for Internet of Things. Sensors, 19(24), 5486.

    Article  Google Scholar 

  4. Rahmani, A. M., Ali Naqvi, R., Hussain Malik, M., Malik, T. S., Sadrishojaei, M., Hosseinzadeh, M., & Al-Musawi, A. (2021). E-learning development based on Internet of Things and blockchain technology during COVID-19 pandemic. Mathematics, 9(24), 3151.

    Article  Google Scholar 

  5. Sefati, S., & Navimipour, N. J. (2021). 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(20), 15620–15627.

    Article  Google Scholar 

  6. Laghari, A. A., Wu, K., Laghari, R. A., Ali, M., & Khan, A. A. (2021). A review and state of art of Internet of Things (IoT). Archives of Computational Methods in Engineering, 1–19

  7. Lombardi, M., Pascale, F., & Santaniello, D. (2021). Internet of things: A general overview between architectures, protocols and applications. Information, 12(2), 87.

    Article  Google Scholar 

  8. Yousefi, S., Derakhshan, F., Aghdasi, H. S., & Karimipour, H. (2020). An energy-efficient artificial bee colony-based clustering in the internet of things. Computers & Electrical Engineering, 86, 106733.

    Article  Google Scholar 

  9. 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(13), 10652–10664.

    Article  Google Scholar 

  10. Liang, H., Liu, G., Gao, J., & Khan, M. J. (2020). Overflow remote warning using improved fuzzy c-means clustering in IoT monitoring system based on multi-access edge computing. Neural Computing and Applications, 32(19), 15399–15410.

    Article  Google Scholar 

  11. Hamidouche, R., Aliouat, Z., Ari, A. A. A., & Gueroui, M. (2019). An efficient clustering strategy avoiding buffer overflow in IoT sensors: A bio-inspired based approach. IEEE Access, 7, 156733–156751.

    Article  Google Scholar 

  12. Sindhuja, M., & Selvamani, K. (2019). Cluster head selection framework for risk awareness enabled IoT networks using ant lion optimisation approach. Wireless Personal Communications, 107(1), 1–21.

    Article  Google Scholar 

  13. Chen, Z., Long, X., Chen, L., Wu, Y., Wu, J., & Liu, S. (2021). Intra‐cluster aggregation aware routing for distributed training in wireless sensor networks. Concurrency and Computation: Practice and Experience, e6795.

  14. Choudhury, N., Matam, R., Mukherjee, M., Lloret, J., & 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.

  15. Sadrishojaei, M., Navimipour, N. J., Reshadi, M., & Hosseinzadeh, M. (2021). A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Computing, 1–11.

  16. Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., Hosseinzadeh, M., & Unal, M. (2022). An energy-aware clustering method in the IoT using a swarm-based algorithm. Wireless Networks, 28(1), 125–136.

    Article  Google Scholar 

  17. Deb, S., & Gao, X.-Z. (2021). A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem. Complex & Intelligent Systems, 1–18.

  18. Wang, J., Cheng, Z., Ersoy, O. K., Zhang, M., Sun, K., & Bi, Y. (2019). Improvement and application of chicken swarm optimization for constrained optimization. IEEE Access, 7, 58053–58072.

    Article  Google Scholar 

  19. Zouache, D., Arby, Y. O., Nouioua, F., & Abdelaziz, F. B. (2019). Multi-objective chicken swarm optimization: a novel algorithm for solving multi-objective optimization problems. Computers & Industrial Engineering, 129, 377–391.

    Article  Google Scholar 

  20. Deb, S., Gao, X. Z., Tammi, K., Kalita, K., & Mahanta, P. (2020). A new teaching–learning-based chicken swarm optimization algorithm. Soft Computing, 24(7), 5313–5331.

    Article  Google Scholar 

  21. Saxena, S., & Mehta, D. (2021). An adaptive fuzzy-based clustering and bio-inspired energy efficient hierarchical routing protocol for wireless sensor networks. Wireless Personal Communications, 1–20.

  22. Kumar, J. S., & Zaveri, M. A. (2016). Hierarchical clustering for dynamic and heterogeneous internet of things. Procedia Computer Science, 93, 276–282.

    Article  Google Scholar 

  23. Xiuwu, Y., Ying, L., Yong, L., & Hao, Y. (2022). WSN Clustering routing algorithm based on hybrid genetic tabu search. Wireless Personal Communications, 1–22.

  24. 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 

  25. Janakiraman, S. (2018). A hybrid ant colony and artificial bee colony optimization algorithm-based cluster head selection for IoT. Procedia computer science, 143, 360–366.

    Article  Google Scholar 

  26. Sankar, S., Ramasubbareddy, S., Chen, F., & Gandomi, A. H. (2020). Energy-efficient cluster-based routing protocol in internet of things using swarm intelligence. In 2020 IEEE symposium series on computational intelligence (SSCI). IEEE.

  27. Ahmad, M., Ikram, A. A., Wahid, I., Ullah, F., Ahmad, A., & Alam Khan, F. (2020). Optimized clustering in vehicular ad hoc networks based on honey bee and genetic algorithm for internet of things. Peer-to-Peer Networking and Applications, 13(2), 532–547.

    Article  Google Scholar 

  28. Saini, T. K., & Sharma, S. (2019). Self-managed access scheme for demand request in TDM/TDMA star topology network. Defence Science Journal, 69(1).

  29. Meng, X., Liu, Y., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: chicken swarm optimization. In International conference in swarm intelligence. Springer.

  30. 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 

  31. Deb, S., Gao, X. Z., Tammi, K., Kalita, K., & Mahanta, P. (2020). Recent studies on chicken swarm optimization algorithm: A review (2014–2018). Artificial Intelligence Review, 53(3), 1737–1765.

    Article  Google Scholar 

  32. Yu, X., Zhou, L., & Li, X. (2019). A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization. Computer Networks, 154, 73–78.

    Article  Google Scholar 

  33. Fouad, M. M., Hafez, A. I., & Hassanien, A. E. (2019). Optimizing topologies in wireless sensor networks: A comparative analysis between the Grey Wolves and the Chicken Swarm Optimization algorithms. Computer Networks, 163, 106882.

    Article  Google Scholar 

  34. Al Shayokh, M., & Shin, S. Y. (2017). Bio inspired distributed WSN localization based on chicken swarm optimization. Wireless Personal Communications, 97(4), 5691–5706.

    Article  Google Scholar 

  35. Rao, P. 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 

  36. 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 

  37. Riley, G. F., & Henderson, T. R. (2010). The ns-3 network simulator. Modeling and tools for network simulation (pp. 15–34). Springer.

    Chapter  Google Scholar 

  38. Carneiro, G. (2010). NS-3: Network simulator 3. In UTM Lab Meeting April. 2010.

Download references

Funding

This work is not supported.

Author information

Authors and Affiliations

  1. Faculty of Industry, University of Applied Science and Technology (UAST), Tehran, Iran

    Mahyar Sadrishojaei

  2. Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey

    Nima Jafari Navimipour

  3. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Midia Reshadi

  4. Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh

  5. Computer Science, University of Human Development, Sulaymaniyah, Iraq

    Mehdi Hosseinzadeh

Authors
  1. Mahyar Sadrishojaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nima Jafari Navimipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Midia Reshadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mehdi Hosseinzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This manuscript was written with equal contributions from all of the authors. The final version was viewed and confirmed by all of the authors.

Corresponding author

Correspondence to Nima Jafari Navimipour.

Ethics declarations

Conflict of Interest

The authors state that they are not involved in any conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadrishojaei, M., Navimipour, N.J., Reshadi, M. et al. An Energy-Aware IoT Routing Approach Based on a Swarm Optimization Algorithm and a Clustering Technique. Wireless Pers Commun 127, 3449–3465 (2022). https://doi.org/10.1007/s11277-022-09927-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-022-09927-0

Keywords

Search

Navigation