Hybrid Based Cross Layer Optimization of Wireless Sensor Networks

Authors

  • V. Kiran Kumar Research Scholar, Dept. of CSE, VTU, BELAGAVI
  • Shivamurthy G. Associate Professor & Head, Dept. of CSE, VTUCPGS, Chikkaballapura

Keywords:

Performance of Services (QoS), Cross-Layer Optimisation, Link Health Estimator, QoS Upgraded MAC, Multifunction access controls (MAC), Network (NWK) layer, Wireless Sensor Network (WSN)

Abstract

Due to their many advantages over traditional communication techniques, wireless sensor networks, or WSNs, are seen as a potential substitute for evaluation, diagnosing, and operating remote electrical equipment. The task of addressing Value of Service (QoS) issues in WSNs continues to be challenging. This article presents the findings of a thorough experimental investigation to maximise multi-objective QoS in the WSN protocol layers. At the Physical Layer (PHY) layer, a Link Quality Estimate is constructed to evaluate the condition of the links in a WSN. A Quality of Service (QoS) Enhanced MAC technique is used at the MAC layer to reduce contentions between nodes and provide priority to mission-critical data. A routing technique for multi-objective QoS optimisation is presented at the Network (NWK) layer. These algorithms work together across layers to improve the system's dependability and decrease latency. The results show that the proposed algorithms are superior when measured against the state-of-the-art. The proposed algorithms outperformed the ZigBee protocol and the basic Distribution Grid wireless communications system in real-world tests, which demonstrated that they offered a better level of service (QoS) with mission-critical traffic.

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References

D. I. J. Jacob and D. P. E. Darney, “Artificial bee colony optimization algorithm for enhancing routing in wireless networks,” J. Artif. Intell. Capsul. Networks, vol. 3, no. 1, pp. 62–71, 2021.

J. Amutha, S. Sharma, and S. K. Sharma, “Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions,” Comput. Sci. Rev., vol. 40, p. 100376, 2021.

K. Gulati, R. S. K. Boddu, D. Kapila, S. L. Bangare, N. Chandnani, and G. Saravanan, “A review paper on wireless sensor network techniques in Internet of Things (IoT),” Mater. Today Proc., vol. 51, pp. 161–165, 2022.

A. Kumar et al., “Optimal cluster head selection for energy efficient wireless sensor network using hybrid competitive swarm optimization and harmony search algorithm,” Sustain. Energy Technol. Assessments, vol. 52, p. 102243, 2022.

K. Guleria and A. K. Verma, “Meta-heuristic ant colony optimization based unequal clustering for wireless sensor network,” Wirel. Pers. Commun., vol. 105, pp. 891–911, 2019.

R. Saraswathi, J. Srinivasan, and S. Aruna, “An Energy Efficient Routing Protocol and Cross Layer Based Congestion Detection Using Hybrid Genetic Fuzzy Neural Network (HGFNN) Model for MANET.,” J. Algebr. Stat., vol. 13, no. 2, 2022.

J. Luo, Y. Chen, M. Wu, and Y. Yang, “A survey of routing protocols for underwater wireless sensor networks,” IEEE Commun. Surv. Tutorials, vol. 23, no. 1, pp. 137–160, 2021.

S. Parween and S. Z. Hussain, “A review on cross-layer design approach in WSN by different techniques,” Adv. Sci. Technol. Eng. Syst., vol. 5, no. 4, pp. 741–754, 2020, doi: 10.25046 /AJ050488.

S. R. Lahane and K. N. Jariwala, “Secured cross-layer cross-domain routing in dense wireless sensor network: A new hybrid based clustering approach,” Int. J. Intell. Syst., vol. 36, no. 8, pp. 3789–3812, 2021, doi: 10.1002/int.22438.

K. Guleria, D. Prasad, U. K. Lilhore, and S. Simaiya, “Asynchronous Media Access Control Protocols and Cross Layer Optimizations for Wireless Sensor Networks: An Energy Efficient Perspective,” J. Comput. Theor. Nanosci., vol. 17, no. 6, pp. 2531–2538, 2020, doi: 10.1166/jctn.2020.8926.

K. Babber and R. Randhawa, Cross-Layer designs in wireless sensor networks, vol. 776. Springer Berlin Heidelberg, 2019. doi: 10.1007/978-3-662-57277-1_7.

C. Chandravathi and K. Mahadevan, “Web Based Cross Layer Optimization Technique for Energy Efficient WSN,” Wirel. Pers. Commun., vol. 117, no. 4, pp. 2781–2792, 2021, doi: 10.1007/s11277-020-07047-1.

O. Deepa and J. Suguna, “An optimized QoS-based clustering with multipath routing protocol for wireless sensor networks,” J. King Saud Univ. Inf. Sci., vol. 32, no. 7, pp. 763–774, 2020.

S. Qu, L. Zhao, and Z. Xiong, “Cross-layer congestion control of wireless sensor networks based on fuzzy sliding mode control,” Neural Comput. Appl., vol. 32, pp. 13505–13520, 2020.

H. M. Jawad et al., “Accurate empirical path-loss model based on particle swarm optimization for wireless sensor networks in smart agriculture,” IEEE Sens. J., vol. 20, no. 1, pp. 552–561, 2019.

K. N. Dattatraya and K. R. Rao, “Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN,” J. King Saud Univ. Inf. Sci., vol. 34, no. 3, pp. 716–726, 2022.

Z. Al Aghbari, A. M. Khedr, W. Osamy, I. Arif, and D. P. Agrawal, “Routing in wireless sensor networks using optimization techniques: A survey,” Wirel. Pers. Commun., vol. 111, pp. 2407–2434, 2020.

V. Srivastava, S. Tripathi, K. Singh, and L. H. Son, “Energy efficient optimized rate based congestion control routing in wireless sensor network,” J. Ambient Intell. Humaniz. Comput., vol. 11, pp. 1325–1338, 2020.

K. Guleria and A. K. Verma, “Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks,” Wirel. Networks, vol. 25, pp. 1159–1183, 2019.

R. Sinde, F. Begum, K. Njau, and S. Kaijage, “Refining network lifetime of wireless sensor network using energy-efficient clustering and DRL-based sleep scheduling,” Sensors, vol. 20, no. 5, p. 1540, 2020.

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Published

27.12.2023

How to Cite

Kumar, V. K. ., & G., S. . (2023). Hybrid Based Cross Layer Optimization of Wireless Sensor Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 225–237. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4268

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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