Secure and Reliable Routing Framework for IoT-Based Wireless Sensor Networks through Deep Learning and Homomorphic Encryption

Abstract

Wireless Sensor Networks (WSNs) form the backbone of Internet of Things (IoT) ecosystems, enabling pervasive data collection and communication. However, the inherent constraints of sensor nodes limited energy, processing power, and memorycoupled with deployment in open environments, create significant security vulnerabilities and routing challenges. This paper proposes a novel framework integrating deep learning-based attack prediction, optimized clustering, collision-aware routing, and homomorphic encryption for secure and energy-efficient data transmission in WSN-IoT environments. The proposed methodology first identifies malicious nodes using a deep learning classifier, followed by optimized clustering and cluster head selection using an enhanced metaheuristic algorithm. A collision-aware routing protocol selects optimal paths for data transmission, while homomorphic encryption ensures end-to-end data security without decryption overhead. Simulation results demonstrate significant improvements in network lifetime, packet delivery ratio, energy consumption, and end-to-end delay compared to existing approaches.

Introduction

This research focuses on improving security and energy efficiency in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). IoT enables seamless communication between interconnected devices, while WSNs play a key role in sensing and data collection. However, these networks face major challenges due to limited sensor energy, vulnerability to attacks, packet collisions, and inefficient routing, especially in large-scale deployments.

The study highlights that existing solutions often address either security or energy efficiency but rarely both together. Many centralized and traditional routing approaches fail to scale and introduce issues such as high energy consumption, delays, and reduced network lifetime. Additionally, low-power sensor nodes are highly vulnerable to attacks due to limited computational and storage capabilities.

To overcome these limitations, the proposed framework introduces a machine learning and deep learning-based secure routing system. It includes:

• Deep Neural Network (DNN) for early detection of malicious nodes
• PSO-based clustering for optimized cluster head selection and energy balancing
• Modified A algorithm (SEERP)* for secure and energy-efficient path selection
• CSMA/CA enhancement for reducing packet collisions
• Paillier homomorphic encryption for secure end-to-end data transmission

The system is implemented using Python and TensorFlow, with simulation parameters such as network size, node energy, and transmission range defined for performance evaluation.

Conclusion

This paper proposed a comprehensive framework for secure and energy-efficient data transmission in WSN-IoT environments integrating deep learning-based attack prediction, optimized clustering, collision-aware routing, and homomorphic encryption. The proposed methodology effectively addresses key challenges including malicious node detection, energy conservation, packet collision reduction, and data security.Simulation results demonstrate significant performance improvements: 26% increase in network lifetime, 89%+ packet delivery ratio even under 30% malicious nodes, 119% improvement in residual energy retention at round 1000, 28% reduction in end-to-end delay, and robust security properties including confidentiality, integrity, authentication, and privacy-preserving computation.Future work will extend this framework to underwater WSN environments, integrate blockchain for enhanced trust management, and explore lightweight homomorphic encryption variants for resource-constrained nodes.

References

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Copyright

Copyright © 2026 Mrs. C. Maheshsathya, Dr. R. Ramkumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.