Social Spider Enhanced Multi-Layered ANN Routing Scheme for Wireless Sensor Networks Utilizing Internet of Things and Blockchain Technology

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 7

Year of Publication : 2025

Authors : Jinsha Lawrence, Shaji K. A. Theodore, Dinesh Kumar Budagam, B. Rajalakshmi, K. Manojkumar, Srikanth Mylapalli

10.14445/23488549/IJECE-V12I7P117

How to Cite?

Jinsha Lawrence, Shaji K. A. Theodore, Dinesh Kumar Budagam, B. Rajalakshmi, K. Manojkumar, Srikanth Mylapalli, "Social Spider Enhanced Multi-Layered ANN Routing Scheme for Wireless Sensor Networks Utilizing Internet of Things and Blockchain Technology," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 7, pp. 220-230, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I7P117

Abstract:

Wireless Sensor Network (WSN) is prone to various attacks during data transmission and also faces difficulties including reduced energy efficiency, minimized security and less network lifetime. For this reason, appropriate security measures and routing approaches need to be implemented. Henceforth, this paper presents a secure and energy-efficient routing scheme for WSN to address these difficulties. The proposed work consists of a Social Spider enhanced Multi-layered Artificial Neural Network (ANN) based routing scheme for attaining improved WSN management with reduced complexity. The deployment of an SSO-multi-layered ANN routing scheme acquires adaptive and dynamic routing with energy efficiency and increased robustness. Moreover, conventional IoT platforms struggle with various limitations, including cyberattacks; thus, to enhance data access with improved data privacy and security, IoT is integrated with Blockchain technology to ensure data integrity and protection. In spite of this, achieving a dependable routing scheme is crucial for assuring the security and efficiency of WSN. As a result, the proposed SSO-multi-layered ANN routing scheme is combined with IoT and Blockchain technology, enhancing WSN efficacy and Reliability. The proposed system is validated using NS-2. The results show reduced packet loss and energy consumption with increased PDR, network lifetime and throughput, indicating highly secure and protected WSN performance.

Keywords:

Wireless Sensor Network (WSN), SSO-multi-layered ANN routing, IoT and Blockchain technology.

References:

[1] Ibrahim A. Abd El-Moghith, and Saad M. Darwish, “Towards Designing a Trusted Routing Scheme in Wireless Sensor Networks: A New Deep Blockchain Approach,” IEEE Access, vol. 9, pp. 103822-103834, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Saba Awan et al., “Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks,” Sensors, vol. 22, no. 2, pp. 1-24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] K.H. Vijayendra Prasad, and Sasikumar Periyasamy, “Secure-Energy Efficient Bio-Inspired Clustering and Deep Learning-Based Routing Using Blockchain for Edge Assisted WSN Environment,” IEEE Access, vol. 11, pp. 145421-145440, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Azath Mubarakali, “An Efficient Authentication Scheme Using Blockchain Technology for Wireless Sensor Networks,” Wireless Personal Communications, vol. 127, pp. 255-269, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Rahul Nawkhare, and Daljeet Singh, “Machine Learning Approach on Efficient Routing Efficient Techniques in Wireless Sensor Network,” 2022 IEEE International Conference on Current Development in Engineering and Technology, Bhopal, India, pp. 1-6, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Abdul Rehman et al., “Ensuring Security and Energy Efficiency of Wireless Sensor Network by Using Blockchain,” Applied Sciences, vol. 12, no. 21, pp. 1-22, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Zahoor Ali Khan et al., “A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks,” IEEE Access, vol. 11, pp. 31036-31051, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Gebrekiros Gebreyesus Gebremariam, J. Panda, and S. Indu, “Secure Localization Techniques in Wireless Sensor Networks against Routing Attacks Based on Hybrid Machine Learning Models,” Alexandria Engineering Journal, vol. 82, pp. 82-100, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] S. Harihara Gopalan et al., “An Energy Efficient Routing Protocol with Fuzzy Neural Networks in Wireless Sensor Network,” Ain Shams Engineering Journal, vol. 15, no. 10, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Lei Hang, and Do-Hyeun Kim, “Design and Implementation of an Integrated IoT Blockchain Platform for Sensing Data Integrity,” Sensors, vol. 19, no. 10, pp. 1-26, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Awatef Salem Balobaid et al., “Neural Network Clustering and Swarm Intelligence-Based Routing Protocol for Wireless Sensor Networks: A Machine Learning Perspective,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, pp. 1-10, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Greeshma Arya, Ashish Bagwari, and Durg Singh Chauhan, “Performance Analysis of Deep Learning-Based Routing Protocol for an Efficient Data Transmission in 5G WSN Communication,” IEEE Access, vol. 10, pp. 9340-9356, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Rajesh Kumar Varun et al., “Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, pp. 1-13, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Carlos Lester Duenas Santos et al., “ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks,” IEEE Access, vol. 11, pp. 57401-57414, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] H.L. Gururaj et al., “Collaborative Energy-Efficient Routing Protocol for Sustainable Communication in 5G/6G Wireless Sensor Networks,” IEEE Open Journal of the Communications Society, vol. 4, pp. 2050-2061, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Kegomoditswe Boikanyo et al., “Performance Optimization for Mobile Wireless Sensor Networks Routing Protocol Using Adaptive Boosting With Sensitivity Analysis,” IEEE Access, vol. 12, pp. 146494-146512, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Geetanjali Rathee et al., “A Secure IoT Sensors Communication in Industry 4.0 Using Blockchain Technology,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 533-545, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Sunil Kumar et al., “Division Algorithm Based Energy-Efficient Routing in Wireless Sensor Networks,” Wireless Personal Communications, vol. 122, pp. 2335-2354, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Saleh A. Alghamdi, “Cuckoo Energy-Efficient Load-Balancing On-Demand Multipath Routing Protocol,” Arabian Journal for Science and Engineering, vol. 47, pp. 1321-1335, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammed Al Mazaideh, and Janos Levendovszky, “A Multi-Hop Routing Algorithm for WSNs Based on Compressive Sensing and Multiple Objective Genetic Algorithm,” Journal of Communications and Networks, vol. 23, no. 2, pp. 138-147, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Munuswamy Selvi et al., “An Energy Efficient Clustered Gravitational and Fuzzy Based Routing Algorithm in WSNs,” Wireless Personal Communications, vol. 116, pp. 61-90, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Chunfu Zhang et al., “An Improved Public Key Cryptographic Algorithm Based on Chebyshev Polynomials and RSA,” Symmetry, vol. 16, no. 3, pp. 1-15, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Kevin Hendy, and Arya Wicaksana, “Post-Quantum Hybrid Encryption Scheme for Blockchain Application,” International Journal of Innovative Computing, Information and Control, vol. 18, no. 6, pp. 1701-1717, 2022.
[CrossRef] [Google Scholar] [Publisher Link]