Weight Red Deer Algorithm Based Clustering Selection And Fuzzy Trust Evaluation For Wireless Sensor Networks
Main Article Content
Keywords
Wireless Sensor Networks (WSNs), fuzzy trust evaluation, clustering, Cluster Head (CH) selection, security, K-Means Clustering (KMC),Weight Red Deer Algorithm (WRDA)
Abstract
Wireless Sensor Networks (WSNs) have received the most interest owing to their vast variety of applications.WSNs detect air pollution, humidity, and temperature, and seismic event detections. They are made up of thousands of sensor nodes that interact with one another.The primary issues with the WSN in the current system are security and energy usage. Additionally, they are unable to defend against attacks from compromised or self-centered internal nodes with proper identities.Weight Red Deer Algorithm (WRDA) is suggested in this study as a solution to the aforementioned issue.The system model, fuzzy trust assessment, outlier identification, and CH node selection are the primary stages of this study.Fuzzy trust evaluation is initially used to translate transmission evidences into trust values and minimise trust uncertainty.A K-Means-based outlier identification approach is then presented to analyse a large number of trust values from fuzzy trust assessment or trust recommendation.A meta-heuristic-based secure clustering technique is provided to balance sensor node security and energy savings while selecting CHs.Energy, neighbours, and node base station distance, and security assurance are among the parameters utilized in the WRDA strategy to choose CHs.CHs at the intermediate layer establish the routing backbone to collect, integrate, and transmit data from member nodes. The simulation reveals that the proposed WRDA architecture outperforms existing techniques in throughput, network lifetime, data transfer rate, and energy use.
References
2. Yang, Guisong, et al. "Global and local reliability-based routing protocol for wireless sensor networks." IEEE Internet of Things Journal 6.2 (2018): 3620-3632.
3. Jadidoleslamy, Hossein, Mohammad Reza Aref, and Hossein Bahramgiri. "A fuzzy fully distributed trust management system in wireless sensor networks." AEU-International Journal of Electronics and Communications 70.1 (2016): 40-49.
4. Shaikh, Riaz Ahmed, and Ahmed Saeed Alzahrani. "Trust management method for vehicular ad hoc networks." Quality, Reliability, Security and Robustness in Heterogeneous Networks: 9th International Conference, QShine 2013, Greader Noida, India, January 11-12, 2013, Revised Selected Papers 9. Springer Berlin Heidelberg, 2013.
5. Ye, Zhengwang, et al. "An efficient dynamic trust evaluation model for wireless sensor networks." Journal of Sensors 2017 (2017).
6. Elsmany, EymanFathelrhman Ahmed, et al. "EESRA: Energy efficient scalable routing algorithm for wireless sensor networks." IEEE Access 7 (2019): 96974-96983
7. Thakkar, Ankit, and KetanKotecha. "Cluster head election for energy and delay constraint applications of wireless sensor network." IEEE sensors Journal 14.8 (2014): 2658-2664
8. Pavani, Movva, and PolipalliTrinatha Rao. "Adaptive PSO with optimised firefly algorithms for secure cluster‐based routing in wireless sensor
networks." IET Wireless Sensor Systems 9.5 (2019): 274-283.
9. Wang, Rui, et al. "ETMRM: An energy-efficient trust management and routing mechanism for SDWSNs." Computer Networks 139 (2018): 119-135.
10. Nguyen, GiaNhu, et al. "Blockchain enabled energy efficient red deer algorithm based clustering protocol for pervasive wireless sensor networks." Sustainable Computing: Informatics and Systems 28 (2020): 100464.
11. Saidi, Ahmed, KhelifaBenahmed, and NouredineSeddiki. "Secure cluster head election algorithm and misbehavior detection approach based on trust management technique for clustered wireless sensor networks." Ad Hoc Networks 106 (2020): 102215.
12. Yang, Liu, et al. "A dynamic behavior monitoring game-based trust evaluation scheme for clustering in wireless sensor networks." IEEE Access 6 (2018): 71404-71412.
13. Yang, Liu, et al. "An evolutionary game-based secure clustering protocol with fuzzy trust evaluation and outlier detection for wireless sensor networks." IEEE Sensors Journal 21.12 (2021): 13935-13947.
14. Chan, Wai Hong Ronald, et al. "Adaptive duty cycling in sensor networks with energy harvesting using continuous-time Markov chain and fluid models." IEEE Journal on Selected Areas in Communications 33.12 (2015): 2687-2700.
15. Yang, Liu, et al. "A hybrid, game theory based, and distributed clustering protocol for wireless sensor networks." Wireless Networks 22 (2016): 1007-1021.
16. Tan, Shuaishuai, Xiaoping Li, and Qingkuan Dong. "A trust management system for securing data plane of ad-hoc networks." IEEE Transactions on Vehicular Technology 65.9 (2015): 7579-7592.
17. Peng, Wei, et al. "Interval type-2 fuzzy logic based transmission power allocation strategy for lifetime maximization of WSNs." Engineering Applications of Artificial Intelligence 87 (2020): 103269.
18. Sreejith, S., H. Khanna Nehemiah, and A. Kannan. "A clinical decision support system for polycystic ovarian syndrome using red deer algorithm and random forest classifier." Healthcare Analytics 2 (2022): 100102.
19. Haider, Syed Kamran, et al. "Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks." Sensors 21.24 (2021): 8445 20. Sreejith, S., H. Khanna Nehemiah, and A. Kannan. "A clinical decision support system for polycystic ovarian syndrome using red deer algorithm and random forest classifier." Healthcare Analytics 2 (2022): 100102.
21. Zitar, Raed Abu, and LaithAbualigah. "Application of red deer algorithm in optimizing complex functions." 2021 14th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). IEEE, 2021.
22. Yang, Liu, et al. "An evolutionary game-based secure clustering protocol with fuzzy trust evaluation and outlier detection for wireless sensor networks." IEEE Sensors Journal 21.12 (2021): 13935-13947.
23. Augustine, Susan, and John Patrick Ananth. "Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks." Wireless Networks 26 (2020): 5113-5132.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.