![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Empowered Chicken Swarm Optimization with Intuitionistic Fuzzy Trust Model for Optimized Secure and Energy Aware Data Transmission in Clustered Wireless Sensor Networks
Each sensor node functions autonomously to conduct data transmission in wireless sensor networks. It is very essential to focus on energy dissipation and sensor nodes lifespan. There are many existing energy consumption models, and the problem of selecting optimized cluster head along with efficient path selection is still challenging. To address this energy consumption issue in an effective way the proposed work is designed with a two-phase model for performing cluster head selection, clustering, and optimized route selection for the secure transmission of data packets with reduced overhead. The scope of the proposed methodology is to choose the most prominent cluster head and assistant cluster head which aids in prolonging the network lifespan and also securing the inter-cluster components from selective forwarding attack (SFA) and black hole attack (BHA). The proposed methodology is Empowered Chicken Swarm Optimization (ECSO) with Intuitionistic Fuzzy Trust Model (IFTM) in Inter-Cluster communication. ECSO provides an efficient clustering technique and cluster head selection and IFTM provides a secure and fast routing path from SFA and BHA for Inter-Cluster Single-Hop and Multi-Hop Communication. ESCO uses chaos theory for local optima in cluster head selection. The IFTM incorporates reliance of neighbourhood nodes, derived confidence of nodes, estimation of data propagation of nodes and an element of trustworthiness of nodes are used to implement security in inter-cluster communication. Experimental results prove that the proposed methodology outperforms the existing approaches by increasing packet delivery ratio and throughput, and minimizing packet drop ratio and energy consumption.
Keywords
Wireless Sensor Networks, Chicken Swarm Optimization, Intuitionistic Fuzzy Trust Model, Energy Aware, Security, Cluster Head, Clustering and Inter-Cluster Communication.
User
Font Size
Information
- M. Jain and H. Kandwal, “A Survey on Complex Wormhole Attack in Wireless Ad Hoc Networks,” in International Conference Advances in Computing, Control, and Telecomm. Technologies (ACT ’09), pp. 555-558 (2009).
- He Z, Chen L, Li F, Jin G (2023) A fuzzy model for content-centric routing in Zigbee-based wireless sensor networks (WSNs). PLoS ONE 18(6): e0286913. https://doi.org/10.1371/journal.pone.0286913
- Sathish kumar, R., Ramesh C, “A modified method for preventing black-hole attack in mobile ad hoc networks,” in J. Eng. Appl. Sci. 11(2), 182–191 (2016)
- M. Shinde and D. C. Mehetre, “Black Hole and Selective Forwarding Attack Detection and Prevention in WSN,” in 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, pp. 1-6,(2017).
- Saleh, A.; Joshi, P.; Rathore, R.S.; Sengar, S.S. Trust-Aware Routing Mechanism through an Edge Node for IoT-Enabled Sensor Networks. Sensors 2022, 22, 7820. https://doi.org/10.3390/s22207820
- Yao, Y., Chen, W., Guo, J. et al. Simplified clustering and improved intercluster cooperation approach for wireless sensor network energy balanced routing. J Wireless Com Network 2020, 131 (2020). https://doi.org/10.1186/s13638-020-01748-8
- Srinivas, T. A., S-S, M, “Black Hole and Selective Forwarding Attack Detection and Prevention in IoT in Health Care Sector: Hybrid meta-heuristic-based shortest path routing,” in Journal of Ambient Intelligence and Smart Environments, 13(2):133–156, (2021).
- C. Lai, H. Li, R. Lu and X. S. Shen, “SE-AKA: A secure and efficient group authentication and key agreement protocol for LTE networks,” in Comput. Netw., vol. 57, no. 17, pp. 3492-3510, December (2013).
- El Khediri, Salim Khan, Rehanullah Nejah, Nasri Kachouri, Abdennaceur, “MW-LEACH: Low energy adaptive clustering hierarchy approach for WSN,” in IET Wireless Sensor Systems. Vol 10(4), (2020).
- Hicham Qabouche, Aicha Sahel, Abdelmajid Badri, and Ilham El Mourabit, “Novel Reliable and Dynamic Energy-Aware Routing Protocol for Large Scale Wireless Sensor Networks,” in International Journal of Electrical and Computer Engineering, ISSN: 2088-8708, Vol. 12, No. 6, Decemeber 2022, pp. 6440-6448.
- S. Srinivasa Rao, K. Chenna Keshava Reddy and S. Ravi Chand (2022), A Novel Optimization based Energy Efficient and Secured Routing Scheme using SRFIS-CWOSRR for Wireless Sensor Networks. IJEER 10(3), 644-650. DOI: 10.37397/IJEER.100338.
- Mehetre, D.C., Roslin, S.E. & Wagh, S.J, “ Detection and prevention of black hole and selective forwarding attack in clustered WSN with Active Trust,” in Cluster Comput 22 (Suppl 1), 1313–1328 (2019).
- Yadav R. K., & Mahapatra R. P. (2021). Energy aware optimized clustering for hierarchical routing in wireless sensor network. Computer Science Review, 41, 100417
- A. Mehbodniya, S. Bhatia, A. Mashat, M. Elangovan and S. Sengan, “Proportional fairness based energy efficient routing in wireless sensor network,” Computer Systems Science and Engineering, vol. 41, no.3, pp. 1071–1082, 2022.
- Vinodhini R., & Gomathy C. (2021). Fuzzy Based Unequal Clustering and Context-Aware Routing Based on Glow-Worm Swarm Optimization in Wireless Sensor Networks: Forest Fire Detection. Wireless Personal Communications, 118(4), 3501–3522.
- Udhayavani, M., & Chandrasekaran, M. (2019). Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: A trust-aware routing framework for wireless sensor networks. Cluster Computing, 22(5), 11919-11927.
- Meng, X., Liu, Y., Gao, X., Zhang, H. (2014). A New Bio-inspired Algorithm: Chicken Swarm Optimization. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, Vol. 8794. Springer
- Smith, C.L., Zielinski, S.L.: The Startling Intelligence of the Common Chicken. Scientific American 310(2) (2014).
- Gehad Ismail Sayed, Alaa Tharwat and Aboul Ella Hassanien, “Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection” in Journal: Applied Intelligence, Volume 49, Number 1, Page 188 (2019).
- Wang, Zhenwu, Chao Qin, Benting Wan, William Wei Song, and Guoqiang Yang. “An adaptive fuzzy chicken swarm optimization algorithm.” Mathematical Problems in Engineering 2021 (2021): 1-17.
- Atanassov K.T, “Intuitionistic Fuzzy Sets” in Fuzzy Sets Syst. 1986; 20:87–96.
- Xu Z., Yager R.R, “Some Geometric Aggregation Operators Based on Intuitionistic Fuzzy Sets,” in Int. J. Gen. Syst. 2006; 35:417–433.
- Shen F., Ma X., Li Z., Xu Z., Cai D, “ An Extended Intuitionistic Fuzzy TOPSIS Method Based on a New Distance Measure with an Application to Credit Risk Evaluation,” in Inf. Sci. 2018; 428:105–119.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 136
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 1