Efficient and Secure Multi-Tier Routing Framework for Energy-Constrained Wireless Sensor Networks
Authors
Research Scholar, Faculty of Engineering, Ramchandra Chandravansi University (India)
Professor, Faculty of Engineering, Ramchandra Chandravansi University (India)
Article Information
DOI: 10.51244/IJRSI.2026.1307000315
Subject Category: Education
Volume/Issue: 13/7 | Page No: 4302-4306
Publication Timeline
Submitted: 2026-07-28
Accepted: 2026-08-03
Published: 2026-08-17
Abstract
Wireless Sensor Networks (WSNs) are increasingly deployed in resource-constrained and hostile environments for critical applications such as environmental monitoring, industrial automation, and structural auditing. However, their real-world efficacy is severely limited by battery energy constraints and high vulnerability to routing-layer security threats. Conventional cryptographic solutions incur excessive computational overhead, while standard energy-efficient routing protocols often neglect security. To resolve this trade-off, this paper proposes an integrated, multi-tier optimization framework combining three novel algorithms: Proficient Secure Cluster Head Selection (PSCH), Total Path Trust Assessment Routing (TPTAR), and Energy-Proficient, Secure, and Trust-Based Shortest Path Optimized Routing (EST-SPOR). The proposed architecture utilizes fuzzy logic for cluster-head selection, a dynamic trust evaluation model combining direct and reference trust metrics, and an optimized shortest-path routing mechanism based on Ant Colony Optimization (ACO) principles. Simulation analyses conducted in NS-3 demonstrate that the framework achieves superior performance in terms of Packet Delivery Ratio (PDR), energy conservation, end-to-end latency, and resilience against malicious routing attacks compared to existing baseline protocols.
Keywords
Key Words: Wireless Sensor Networks (WSNs), Cluster Head Selection, Trust Management, Energy Efficiency and Ant Colony Optimization (ACO)
Downloads
References
1. Fang, W., Zhang, W., Chen, W., & Yang, Y. (2021). Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks (LEACH-TM). EURASIP Journal on Wireless Communications and Networking, 2021(1), 1–18. https://doi.org/10.1186/s13638-021-01934-8 [Google Scholar] [Crossref]
2. Fang, W., Zhang, W., Yang, W., & Yang, Y. (2016). BTRES: Beta-based trust and reputation evaluation system for wireless sensor networks. Journal of Network and Computer Applications, 59, 88–94. https://doi.org/10.1016/j.jnca.2015.06.012 [Google Scholar] [Crossref]
3. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences (pp. 1–10). IEEE. https://doi.org/10.1109/HICSS.2000.926982 [Google Scholar] [Crossref]
4. Hu, H., Zhang, X., & Liu, Y. (2021). Trust-aware secure routing protocol for wireless sensor networks. ETRI Journal, 43(4), 629–642. https://doi.org/10.4218/etrij.2020-0251 [Google Scholar] [Crossref]
5. Kalidoss, T., Rajaram, A., & Sankar, A. (2020). Quality of service aware energy efficient routing algorithm for wireless sensor networks. Wireless Personal Communications, 114(4), 2889–2905. https://doi.org/10.1007/s11277-020-07508-y [Google Scholar] [Crossref]
6. Lindsey, S., & Raghavendra, C. S. (2002). PEGASIS: Power-efficient gathering in sensor information systems. In Proceedings IEEE Aerospace Conference (Vol. 3, pp. 1125–1135). IEEE. https://doi.org/10.1109/AERO.2002.1035247 [Google Scholar] [Crossref]
7. Liu, Y., Dong, M., Ota, K., & Liu, A. (2016). ActiveTrust: Secure and trustable routing in wireless sensor networks. IEEE Transactions on Information Forensics and Security, 11(9), 2013–2027. https://doi.org/10.1109/TIFS.2016.2570740 [Google Scholar] [Crossref]
8. Maheshwari, P., Sharma, A. K., & Verma, K. (2021). Energy efficient cluster head selection using butterfly optimization algorithm in wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 12(1), 1045–1058. https://doi.org/10.1007/s12652-020-02128-2 [Google Scholar] [Crossref]
9. Manjeshwar, A., & Agrawal, D. P. (2001). TEEN: A routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th International Parallel and Distributed Processing Symposium (pp. 2009–2015). IEEE. https://doi.org/10.1109/IPDPS.2001.925197 [Google Scholar] [Crossref]
10. Saini, M., & Sharma, B. (2019). Trust-based secure hybrid framework (TSER) for wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 10(8), 3125–3137. https://doi.org/10.1007/s12652-018-1032-4 [Google Scholar] [Crossref]
11. Xu, X., Gu, Z., & Pan, L. (2020). Elite immune ant colony optimization (EIACO) for job allocation in wireless sensor networks. IEEE Access, 8, 11234–11245. https://doi.org/10.1109/ACCESS.2020.2965412 [Google Scholar] [Crossref]
12. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379. https://doi.org/10.1109/TMC.2004.41 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
- Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
- “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
- Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
- Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study