Skip to main content

Advertisement

Log in

Unequal sized cells based on cross shapes for data collection in green Internet of Things (IoT) networks

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

Abstract

In the Internet of Things (IoT), smart devices such as sensors gather data by sensing the IoT environment and communicating with each other. This data is transmitted to a base station to satisfy certain requests of remote users. Energy conservation is a critical issue for battery-powered IoT nodes. The mobility of the sink can effectively conserve the energy of the sensor nodes. However, improper use of the mobile sink may either erode the energy conservation goal or increase the data delivery delay. Thus, to achieve a green IoT network with minimum delay, this paper addresses the energy conservation issue in these networks by proposing a Cross-zone based Routing mechanism for IoT–based WSN (CRIoT). CRIoT has more control over the routing tree by using a grid-based virtual structure with cells of different sizes that are formed by connecting several smaller cross-shaped pieces. These paths are created so that they not only prevent the occurrence of hot spots and prolong the network lifetime but also lead to minimal delay. The simulation results indicate that the proposed routing protocol provides better network performance in terms of delay, energy utilization, network lifetime, and throughput.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Salih, K. O. M., Rashid, T. A., Radovanovic, D., & Bacanin, N. (2022). A comprehensive survey on the Internet of Things with the industrial marketplace. Sensors, 22(3), 730.

    Google Scholar 

  2. Sinha, B. B., & Dhanalakshmi, R. (2022). Recent advancements and challenges of Internet of Things in smart agriculture: A survey. Future Generation Computer Systems, 126, 169–184.

    Google Scholar 

  3. Wójcicki, K., Biegańska, M., Paliwoda, B., & Górna, J. (2022). Internet of Things in industry: Research profiling, application, challenges and opportunities—a review. Energies, 15(5), 1806.

    Google Scholar 

  4. Sinche, S., et al. (2020). A survey of IoT management protocols and frameworks. IEEE Communications Surveys & Tutorials, 22(2), 1168–1190.

    Google Scholar 

  5. Hasan, R., & Hasan, R. (2022). Pedestrian safety using the Internet of Things and sensors: Issues, challenges, and open problems. Future Generation Computer Systems, 134, 187–203.

    Google Scholar 

  6. Ravidas, S., Lekidis, A., Paci, F., & Zannone, N. (2019). Access control in Internet-of-Things: A survey. Journal of Network and Computer Applications, 144, 79–101.

    Google Scholar 

  7. Elazhary, H. (2019). Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions. Journal of Network and Computer Applications, 128, 105–140.

    Google Scholar 

  8. Bhuiyan, M. N., Rahman, M. M., Billah, M. M., & Saha, D. (2021). Internet of Things (IoT): A review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet of Things Journal, 8(13), 10474–10498.

    Google Scholar 

  9. Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787–2805.

    MATH  Google Scholar 

  10. Guleria, K., & Verma, A. K. (2019). Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks. Wireless Networks, 25(3), 1159–1183.

    Google Scholar 

  11. Khan, M. I., Gansterer, W. N., & Haring, G. (2013). Static vs. mobile sink: The influence of basic parameters on energy efficiency in wireless sensor networks. Computer Communications, 36(9), 965–978.

    Google Scholar 

  12. Hawbani, A., et al. (2018). Sink-oriented tree based data dissemination protocol for mobile sinks wireless sensor networks. Wireless Networks, 24(7), 2723–2734.

    Google Scholar 

  13. Amini, S. M., Karimi, A., & Esnaashari, M. (2020). Energy-efficient data dissemination algorithm based on virtual hexagonal cell-based infrastructure and multi-mobile sink for wireless sensor networks. The Journal of Supercomputing, 76(1), 150–173.

    Google Scholar 

  14. Shafiq, M., Ashraf, H., Ullah, A., & Tahira, S. (2020). Systematic literature review on energy efficient routing schemes in WSN–a survey. Mobile Networks and Applications, 25(3), 882–895.

    Google Scholar 

  15. Aishwarya Lakshmi, T., Hariharan, B., & Rekha, P. (2019). A survey on energy efficient routing protocol for IoT based precision agriculture. In Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019, pp. 1284–1288.

  16. Chan, L., Gomez Chavez, K., Rudolph, H., & Hourani, A. (2020). Hierarchical routing protocols for wireless sensor network: a compressive survey. Wireless Networks, 26(5), 3291–3314.

    Google Scholar 

  17. Abbas, Z., & Yoon, W. (2015). A survey on energy conserving mechanisms for the internet of things: Wireless networking aspects. Sensors (Switzerland), 15(10), 24818–24847.

    Google Scholar 

  18. Li, S., Da Xu, L., & Zhao, S. (2015). The internet of things: A survey. Information Systems Frontiers, 17(2), 243–259.

    Google Scholar 

  19. Kamalinejad, P., Mahapatra, C., Sheng, Z., Mirabbasi, S., Victor, V. C., & Guan, Y. L. (2015). Wireless energy harvesting for the Internet of Things. IEEE Communications Magazine, 53(6), 102–108.

    Google Scholar 

  20. Tunca, C., Isik, S., Donmez, M. Y., & Ersoy, C. (2015). Ring routing: An energy-efficient routing protocol for wireless sensor networks with a mobile sink. IEEE Transactions on Mobile Computing, 14(9), 1947–1960.

    Google Scholar 

  21. Jain, S., Pattanaik, K. K., & Shukla, A. (2019). QWRP: Query-driven virtual wheel based routing protocol for wireless sensor networks with mobile sink. Journal of Network and Computer Applications, 147, 102430.

    Google Scholar 

  22. Habib, M. A., Saha, S., Razzaque, M. A., Mamun-or-Rashid, M., Fortino, G., & Hassan, M. M. (2018). Starfish routing for sensor networks with mobile sink. Journal of Network and Computer Applications, 123, 11–22.

    Google Scholar 

  23. Maurya, S., Jain, V. K., & Chowdhury, D. R. (2019). Delay aware energy efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network. Journal of Network and Computer Applications, 144, 118–137.

    Google Scholar 

  24. Jadoon, R. N., Zhou, W. Y., Jadoon, W., & Khan, I. A. (2018). RARZ: Ring-zone based routing protocol for wireless sensor networks. Applied Sciences, 8(7), 1023.

    Google Scholar 

  25. Siva Ranjani, S., Radha Krishnan, S., Thangaraj, C., & Vimala Devi, K. (2013). Achieving energy conservation by cluster based data aggregation in wireless sensor networks. Wireless personal communications, 73(3), 731–751.

    Google Scholar 

  26. Sabet, M., & Naji, H. (2016). An energy efficient multi-level route-aware clustering algorithm for wireless sensor networks: A self-organized approach. Computers & Electrical Engineering, 56, 399–417.

    Google Scholar 

  27. Khan, A. W., Abdullah, A. H., Razzaque, M. A., & Bangash, J. I. (2015). VGDRA: A virtual grid-based dynamic routes adjustment scheme for mobile sink-based wireless sensor networks. IEEE Sensors Journal, 15(1), 526–534.

    Google Scholar 

  28. Luo, H., Ye, F., Cheng, J., Lu, S., & Zhang, L. (2005). TTDD: Two-tier data dissemination in large-scale wireless sensor networks. Wireless Networks, 11(1–2), 161–175.

    Google Scholar 

  29. Meng, X., Shi, X., Wang, Z., Wu, S., & Li, C. (2016). A grid-based reliable routing protocol for wireless sensor networks with randomly distributed clusters. Ad Hoc Networks, 51, 47–61.

    Google Scholar 

  30. Darabkh, K. A., El-Yabroudi, M. Z., & El-Mousa, A. H. (2019). BPA-CRP: A balanced power-aware clustering and routing protocol for wireless sensor networks. Ad Hoc Networks, 82, 155–171.

    Google Scholar 

  31. Darabkh, K. A., Odetallah, S. M., Al-qudah, Z., Khalifeh, A. F., & Shurman, M. M. (2019). Energy-aware and density-based clustering and relaying protocol (EA-DB-CRP) for gathering data in wireless sensor networks. Applied Soft Computing, 80, 154–166.

    Google Scholar 

  32. 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.

    Google Scholar 

  33. Naghibi, M., & Barati, H. (2020). EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks. Sustainable Computing: Informatics and Systems, 25, 100377.

    Google Scholar 

  34. Agrawal, A., Singh, V., Jain, S., & Gupta, R. K. (2018). GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink. AEU-International Journal of Electronics and Communications, 94, 1–11.

    Google Scholar 

  35. Dahiya, S., & Singh, P. K. (2018). Optimized mobile sink based grid coverage-aware sensor deployment and link quality based routing in wireless sensor networks. AEU-International Journal of Electronics and Communications, 89, 191–196.

    Google Scholar 

  36. Zhu, C., Long, X., Han, G., Jiang, J., & Zhang, S. (2018). A virtual grid-based real-time data collection algorithm for industrial wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2018(1), 134.

    Google Scholar 

  37. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.

    Google Scholar 

  38. Elhabyan, R. S. Y., & Yagoub, M. C. E. (2015). Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. Journal of Network and Computer Applications, 52, 116–128.

    Google Scholar 

  39. Ari, A. A. A., Yenke, B. O., Labraoui, N., Damakoa, I., & Gueroui, A. (2016). A power efficient cluster-based routing algorithm for wireless sensor networks: Honeybees swarm intelligence based approach. Journal of Network and Computer Applications, 69, 77–97.

    Google Scholar 

  40. Preeth, S. K. S. L., Dhanalakshmi, R., Kumar, R., & Shakeel, P. M. (2018). An adaptive fuzzy rule based energy efficient clustering and immune-inspired routing protocol for WSN-assisted IoT system. Journal of Ambient Intelligence and Humanized Computing, pp. 1–13.

  41. Rani, S., Ahmed, S. H., & Rastogi, R. (2020). Dynamic clustering approach based on wireless sensor networks genetic algorithm for IoT applications. Wireless Networks, 26(4), 2307–2316.

    Google Scholar 

  42. Verma, S., Sood, N., & Sharma, A. K. (2019). Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Applied Soft Computing, 85, 105788.

    Google Scholar 

  43. Chaudhry, R., Tapaswi, S., & Kumar, N. (2019). FZ enabled Multi-objective PSO for multicasting in IoT based Wireless Sensor Networks. Information Science, 498, 1–20.

    MathSciNet  Google Scholar 

  44. Yarinezhad, R., & Hashemi, S. N. (2020). Exact and approximate algorithms for clustering problem in wireless sensor networks. IET Communications, 14(4), 580–587.

    Google Scholar 

  45. Agarwal, V., Tapaswi, S., & Chanak, P. (2022). Intelligent fault-tolerance data routing scheme for IoT-enabled WSNs. IEEE Internet Things Journal. https://doi.org/10.1109/JIOT.2022.3151501

    Article  Google Scholar 

  46. Erman, A. T., Dilo, A., & Havinga, P. (2012). A virtual infrastructure based on honeycomb tessellation for data dissemination in multi-sink mobile wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2012(1), 17.

    Google Scholar 

  47. Chen, X., & Xu, M. (2005) A geographical cellular-like architecture for wireless sensor networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS (Vol. 3794, pp. 249–258).

  48. Yarinezhad, R., & Hashemi, S. N. (2020). Increasing the lifetime of sensor networks by a data dissemination model based on a new approximation algorithm. Ad Hoc Networks, 100, 102084.

    Google Scholar 

  49. Shin, J. H., Kim, J., Park, K., & Park D. (2005). Railroad: Virtual infrastructure for data dissemination in wireless sensor networks. In PE-WASUN’05 - Proceedings of the Second ACM International Workshop on Performance Evaluation of Wireless Ad Hoc, Sensor, and Ubiquitous Networks, pp. 168–174.

  50. Ben Hamida, E., & Chelius G. (2008), A line-based data dissemination protocol for wireless sensor networks with mobile sink. In IEEE International Conference on Communications, pp. 2201–2205.

  51. Anzola, J., Pascual, J., Tarazona, G., & González, R. (2018). A clustering WSN routing protocol based on k-d tree algorithm. Sensors (Switzerland), 18(9), 2899.

    Google Scholar 

  52. Chowdhury, S., & Giri, C. (2019). EETC: Energy efficient tree-clustering in delay constrained wireless sensor network. Wireless Personal Communications, 109(1), 189–210.

    Google Scholar 

  53. Zhao, H., Guo, S., Wang, X., & Wang, F. (2015). Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink. Applied Soft Computing Journal, 34, 539–550.

    Google Scholar 

  54. Karunanithy, K., & Velusamy, B. (2018). Reliable location aware and Cluster-Tap Root based data collection protocol for large scale wireless sensor networks. Journal of Network and Computer Applications, 118, 83–101.

    Google Scholar 

  55. Yarinezhad, R., & Azizi, S. (2021). An energy-efficient routing protocol for the Internet of Things networks based on geographical location and link quality. Computer Networks, 193, 108116.

    Google Scholar 

  56. Kaswan, A., Nitesh, K., & Jana, P. K. (2017). Energy efficient path selection for mobile sink and data gathering in wireless sensor networks. AEU-International Journal of Electronics and Communications, 73, 110–118.

    Google Scholar 

  57. Yim, Y., Mo, H. S., Kim, C., Kim, S. H., Leung, V. C. M., & Lee, E. (2020). Virtual tube storage scheme for supporting mobile sink groups in wireless sensor networks. Computer Communications, 159, 245–257.

    Google Scholar 

  58. Azharuddin, M., & Jana, P. K. (2017). PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Computing, 21(22), 6825–6839.

    Google Scholar 

  59. Yarinezhad, R., & Hashemi, S. N. (2019). An efficient data dissemination model for wireless sensor networks. Wireless Networks, 25(6), 3419–3439.

    Google Scholar 

  60. Shahryari, M. S., et al. (2022). High-throughput and energy-efficient data gathering in heterogeneous multi-channel wireless sensor networks using genetic algorithm. Ad Hoc Networks. https://doi.org/10.1016/j.adhoc.2022.103041

    Article  Google Scholar 

  61. Jannu, S., et al. (2022). Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2022.3220186

    Article  Google Scholar 

  62. Najjar-Ghabel, S., Farzinvash, L., & Razavi, S. N. (2022). Data harvesting in wireless sensor networks using mobile sinks under real-world circumstances. The Journal of Supercomputing, pp. 1–30.

Download references

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

Authors

Contributions

RY, SA, and TT defined the problem, designed the protocol, and wrote the manuscript. TT performed the simulations.

Corresponding author

Correspondence to Sadoon Azizi.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Ethical Approval

This manuscript has not been published and is not under consideration for publication elsewhere.

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 (e.g. a society or other partner) 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

Taami, T., Azizi, S. & Yarinezhad, R. Unequal sized cells based on cross shapes for data collection in green Internet of Things (IoT) networks. Wireless Netw 29, 2143–2160 (2023). https://doi.org/10.1007/s11276-023-03281-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-023-03281-0

Keywords

Navigation