Skip to main content
SpringerLink
Log in

A Novel Cross-Layer Cross-Domain Routing Model and It’s Optimization for Cluster-Based Dense WSN

Wireless Personal Communications volume 118, pages 2765–2784 (2021)Cite this article

Abstract

Wireless Sensor Networks (WSNs) plays its adorable performance in the current day communication as it could sense different environmental and physical parameters by utilizing low-cost sensor devices. The network's growth due to scientific enrichment has altogether made it feasible to design a cross-layer protocol based on the energy-efficient network. This obviously concerns the prolonging of network lifetime. This research work attempts to introduce a novel Cross-Layer Design Routing model under the clustering approach. The implemented work depends on a cross-layer mechanism via diverse layers (comprising physical layer and network layer). A cluster-based routing is introduced, where the optimal cluster head is selected using a new hybrid algorithm named Alpha Wolf-assisted Whale Optimization Algorithm (AW-WOA). Thereby, the shortest path is defined and ensures the prolonging of network lifetime. The proposed hybrid algorithm is the hybridized form of the Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO). Moreover, the optimal cluster head selection is purely based on certain constraints like energy consumption, delay, and distance, respectively. In the end, the performance of the implemented technique is proved over other conventional approaches with regards to the alive node and network lifetime. In the alive node analysis of supernode, on considering the 1st test case, the presented AW-WOA model at 2000 rounds accomplishes 100% alive node than other existing models, wherein GWO and ATEER attain 91.67% and 75%, the other WOA, PSO, and AWOA ATEER attain 83.33% of alive nodes respectively.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Abbreviations

WSN:

Wireless sensor network

QoS:

Quality of services

CH:

Cluster head

COOR:

Cross-layer optimized opportunistic routing

GCRAD:

Geographic cross-layer routing adapted for disaster

MAC:

Medium access control

NCRP:

Network coding based cross-layer routing protocol

WMSN:

Wireless multimedia sensor network

EECP:

Energy efficient cross-layer protocol

CCOF:

Cross-layer opportunistic forwarding

EDES:

Efficient dynamic selective encryption framework

References

  1. Yarinezhad, R. (2019). Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure. Ad Hoc Networks, 84, 42–55.

    Article  Google Scholar 

  2. Yang, L., Zhu, H., Wang, H., Kang, K., & Qian, H. (2019). Data censoring with network lifetime constraint in wireless sensor networks. Digital Signal Processing, 92, 73–81.

    Article  Google Scholar 

  3. Khalily-Dermany, M., Nadjafi-Arani, M. J., & Doostali, S. (2019). Combining topology control and network coding to optimize lifetime in wireless-sensor networks. Computer Networks, 162, 106859.

    Article  Google Scholar 

  4. Radhika, S., & Rangarajan, P. (2019). On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction. Applied Soft Computing, 83, 105610.

    Article  Google Scholar 

  5. Yarinezhad, R., & Hashemi, S. N. (2019). Solving the load balanced clustering and routing problems in WSNs with an fpt-approximation algorithm and a grid structure. Pervasive and Mobile Computing, 58, 101033.

    Article  Google Scholar 

  6. He, Y., Han, G., Wang, H., Ansere, J. A., & Zhang, W. (2019). A sector-based random routing scheme for protecting the source location privacy in WSNs for the Internet of Things. Future Generation Computer Systems, 96, 438–448.

    Article  Google Scholar 

  7. Maimour, M. (2020). Interference-aware multipath routing for WSNs: Overview and performance evaluation. Applied Computing and Informatics.

  8. Hong, C., Zhang, Y., Xiong, Z., Xu, A., & Ding, W. (2018). FADS: Circular/spherical sector based forwarding area division and adaptive forwarding area selection routing protocol in WSNs. Ad Hoc Networks, 70, 121–134.

    Article  Google Scholar 

  9. Brajula, W., & Praveena, S. (2018). Energy efficient genetic algorithm based clustering technique for prolonging the life time of wireless sensor network. Journal of Networking and Communication Systems, 1(1), 1–9.

    Google Scholar 

  10. Shelgaonkar, S.L. (2020). I-CSA based cluster head selection model in wireless sensor network. Journal of Networking and Communication Systems, 3(2).

  11. Gajjar, S., Sarkar, M., & Dasgupta, K. (2016). FAMACROW: fuzzy and ant colony optimization based combined mac, routing, and unequal clustering cross-layer protocol for wireless sensor networks. Applied Soft Computing, 43, 235–247.

    Article  Google Scholar 

  12. Elhadj, H. B., Elias, J., Chaari, L., & Kamoun, L. (2016). A priority based cross layer routing protocol for healthcare applications. Ad Hoc Networks, 42, 1–18.

    Article  Google Scholar 

  13. Benzerbadj, A., Kechar, B., Bounceur, A., & Pottier, B. (2018). Cross-layer greedy position-based routing for multihop wireless sensor networks in a real environment. Ad Hoc Networks, 71, 135–146.

    Article  Google Scholar 

  14. Fanian, F., & Rafsanjani, M. K. (2019). Cluster-based routing protocols in wireless sensor networks: a survey based on methodology. Journal of Network and Computer Applications, 142, 111–142.

    Article  Google Scholar 

  15. Du, T., Qu, S., Liu, F., & Wang, Q. (2015). An energy efficiency semi-static routing algorithm for WSNs based on HAC clustering method. Information Fusion, 21, 18–29.

    Article  Google Scholar 

  16. Ke, W., Yangrui, O., Hong, J., Heli, Z., & Xi, L. (2016). Energy aware hierarchical cluster-based routing protocol for WSNs. The Journal of China Universities of Posts and Telecommunications, 23(4), 46–52.

    Article  Google Scholar 

  17. Elhabyan, R., Shi, W., & St-Hilaire, M. (2018). A Pareto optimization-based approach to clustering and routing in wireless sensor networks. Journal of Network and Computer Applications, 114, 57–69.

    Article  Google Scholar 

  18. Al-Sodairi, S., & Ouni, R. (2018). Reliable and energy-efficient multi-hop LEACH-based clustering protocol for wireless sensor networks. Sustainable Computing: Informatics and Systems, 20, 1–13.

    Google Scholar 

  19. Arora, V. K., Sharma, V., & Sachdeva, M. (2016). A survey on LEACH and other’s routing protocols in wireless sensor network. Optik, 127(16), 6590–6600.

    Article  Google Scholar 

  20. Guttula, R., & Nandanavanam, V. R. (2020). Patch antenna design optimization using opposition based grey wolf optimizer and map-reduce framework. Data Technologies and Applications, 54, 1.

    Article  Google Scholar 

  21. Guttula, R., & Nandanavanam, V. R. (2019). Mutation probability-based lion algorithm for design and optimization of microstrip patch antenna. Evolutionary Intelligence, 13(3), 331–344.

    Article  Google Scholar 

  22. Devi, K. S. G. (2019). Hybrid genetic algorithm and particle swarm optimization algorithm for optimal power flow in power system. Journal of Computational Mechanics, Power System and Control, 2(2), 31–37.

    Article  Google Scholar 

  23. Basha, T. S. G., Aloysius, G., Rajakumar, B. R., Prasad, M. N. G., & Sridevi, P. V. (2012). A constructive smart antenna beam-forming technique with spatial diversity. IET Microwaves. Antennas and Propagation, 6(7), 773–780.

    Article  Google Scholar 

  24. Singh, R., & Verma, A. K. (2017). Energy efficient cross layer based adaptive threshold routing protocol for WSN. AEU International Journal of Electronics and Communications, 72, 166–173.

    Article  Google Scholar 

  25. Xu, X., Yuan, M., Liu, X., Liu, A., Xiong, N. N., Cai, Z., & Wang, T. (2018). A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs. Sensors (Basel), 18(5), 1422.

    Article  Google Scholar 

  26. Niroumand, Z., & Aghdasi, H. S. (2017). A geographic cross-layer routing adapted for disaster relief operations in wireless sensor networks. Computers and Electrical Engineering, 64, 395–406.

    Article  Google Scholar 

  27. Semchedine, F., Oukachbi, W., Zaichi, N., & Bouallouche-Medjkoune, L. (2015). EECP: a new cross-layer protocol for routing in wireless sensor networks. Procedia Computer Science, 73, 336–341.

    Article  Google Scholar 

  28. Wang, H., Wang, S., Bu, R., & Zhang, E. (2017). A novel cross-layer routing protocol based on network coding for underwater sensor networks. Sensors (Basel), 17(8), 1821.

    Article  Google Scholar 

  29. Espes, D., Lagrange, X., & Suárez, L. (2015). A cross-layer MAC and routing protocol based on slotted aloha for wireless sensor networks. Annals of Telecommunications, 70(3–4), 159–169.

    Article  Google Scholar 

  30. Karyakarte, M. S., Tavildar, A. S., & Khanna, R. (2015). Connectivity-based cross-layer opportunistic forwarding for MWSNs. IETE Journal of Research, 61(5), 547–465.

    Article  Google Scholar 

  31. Khattak, H. A., Ameer, Z., Din, I. U., & Khan, M. K. (2019). Cross-layer design and optimization techniques in wireless multimedia sensor networks for smart cities. Computer Science and Information Systems, 16(1), 1–17.

    Article  Google Scholar 

  32. Yong, Z., & Pei, Q. (2012). A energy-efficient clustering routing algorithm based on distance and residual energy for wireless sensor networks. Procedia Engineering, 29, 1882–1888.

    Article  Google Scholar 

  33. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

  34. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf optimizer. Advances in Engineering Software, 69, 46–61.

    Article  Google Scholar 

  35. Pedersen, M. E. H., & Chipperfield, A. J. (2010). Simplifying particle swarm optimization. Applied Soft Computing, 10(2), 618–628.

    Article  Google Scholar 

  36. 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 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, S.V.N.I.T, Surat, India

    Shivaji R. Lahane & Krupa N. Jariwala

  2. Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Shivaji R. Lahane & Krupa N. Jariwala

Authors
  1. Shivaji R. Lahane
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krupa N. Jariwala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shivaji R. Lahane.

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

Lahane, S.R., Jariwala, K.N. A Novel Cross-Layer Cross-Domain Routing Model and It’s Optimization for Cluster-Based Dense WSN. Wireless Pers Commun 118, 2765–2784 (2021). https://doi.org/10.1007/s11277-021-08154-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-08154-3

Keywords

search

Navigation