Linear Regressive Momentum Optimized Dense Neural Network for Energy and Secure Data Transmission in WSN

Abstract

Wireless Sensor Networks (WSNs) is a distributed network initially designed as simple monitoring systems comprising numerous sensor nodes focusing on data collection, processing, and transmission. In WSNs, a huge amount of vulnerabilities can arise, specifically those initiating from malicious nodes (MNs), which direct to cooperate data integrity, network stability, and reliability. Although security remains critical, current MN detection methods are time-consuming and increased latency for constrained WSNs. In order to overcome these issues, a novel Linear Regressive Momentum Optimized Dense neural Network (LiRMO-DenseNet) model is developed to enhance the data transmission security in WSN. The proposed LiRMO-DenseNet model utilizes the dense neural network concept to categorize the sensor nodes as legitimate sensor nodes or intruders with help of several layers such as input, numerous hidden layers, and output layer. First, the number of sensor nodes is given to the input layer. After that, the input layer transmits the collected sensor nodes to the first hidden layer. In that layer, the different characteristics of the sensor nodes like energy, cooperativeness and trust level are computed. Then, the computed values of the sensor nodes are given to third hidden layer. In that layer, Changepoint linear regression analysis is carried out for analyzing the sensor node with their characteristics by setting the threshold. Depending on the analysis, the nodes are classified as legitimate sensor nodes and intruders. A new part of this process is fine-tuning of dense neural network, where the Metaheuristic Walrus Optimization algorithm is employed to update the hyperparameter of dense neural network for minimizing the training and validation errors, thereby boosting the accuracy of node classification. Finally, the accurate node classification is carried out at output layer. With the selected legitimate sensor nodes, secure data transmission is achieved in WSN. The effectiveness of the proposed LiRMO-DenseNet model is assessed using a comprehensive set of performance measures, including accuracy, confidentiality rate, data integrity rate, packet delivery rate, throughput and delay. The simulation findings demonstrate that the proposed LiRMO-DenseNet model consistently achieves superior security performance, exhibiting higher confidentiality and reduced delay compared to existing deep learning based methods.

Introduction

Wireless Sensor Networks (WSNs) are widely used in applications such as smart cities, healthcare, and monitoring systems, but their performance is constrained by limited energy resources and vulnerability to security attacks. Ensuring energy-efficient and secure data transmission is therefore a critical challenge, as malicious nodes, inefficient routing, and high computational overhead can significantly reduce network lifetime and reliability.

Existing research has proposed a range of machine learning, deep learning, trust-based, fuzzy, and optimization-driven routing and intrusion detection schemes. While these approaches improve specific aspects such as energy efficiency, security, or detection accuracy, most suffer from notable limitations, including high computational complexity, inadequate intruder detection accuracy, lack of lightweight design for resource-constrained nodes, poor scalability, or insufficient integration of energy-aware routing with advanced AI techniques.

To address these gaps, the paper proposes a novel LiRMO-DenseNet model for secure and energy-efficient data transmission in WSNs. The model employs a dense neural network (MLP) to classify sensor nodes as legitimate or intruders based on three key characteristics: residual energy, node cooperativeness, and trust level. Changepoint linear regression is used to analyze node behavior and improve classification reliability, while the Walrus Optimization algorithm fine-tunes network weights to minimize classification errors and enhance accuracy, precision, and recall.

Only legitimate nodes are allowed to participate in data forwarding, which improves throughput, reduces energy waste, and strengthens network security. Extensive simulations demonstrate that the proposed LiRMO-DenseNet model outperforms existing methods across multiple performance metrics. Overall, the work presents an integrated AI- and optimization-driven framework that effectively balances security, energy efficiency, and reliable data transmission in WSN environments.

Conclusion

The paper introduces a novel approach called the LiRMO-DenseNet model, designed to achieve energy-efficient and secure data transmission in WSN. The proposed approach starts with the deployment of multiple sensor nodes across the large scale sensor network, followed by the application of a dense neural network that categorizes the nodes into legitimate or intruder according to their residual energy, trust and node cooperativeness. To further improve the classification accuracy performance, walrus optimization algorithm is employed. This optimization mechanism effectively minimizes error rate during sensor node classification. The effectiveness of the proposed LiRMO-DenseNet model is validated through extensive simulations using key performance metrics such as accuracy, precision, recall, F1 score, data transfer security, throughput, and end to end delay. The simulation results clearly designate that the LiRMO-DenseNet model outperforms existing approaches by achieving superior data transfer security, throughput and transmission success while considerably lowering delay when compared to conventional methods.

References

[1] K. Vinotha , P. Eswaran, “Quantum inspired hyperparameter optimization for enhanced deep learning based intrusion detection in wireless sensor networks”, Systems and Soft Computing, Elsevier, Volume 8, June 2026, Pages 1-12. https://doi.org/10.1016/j.sasc.2025.200431 [2] [16] Edeh Michael Onyemaa, S. Kanimozhi Sugunac, B. Sundaravadivazhagand , Rutvij H. Jhaverie , Ugwuja Nnenna Estherf , Edeh Chinecherem Deborahg , K. Shantha Kumari, “A secure routing protocol for improving the energy efficiency in wireless sensor network applications for industrial manufacturing”, Next Energy, Elsevier, Volume 7, 2025, Pages 1-8. https://doi.org/10.1016/j.nxener.2024.100219 [3] S. Ramachandra, M. Baskar, “Real-time multi-level trust-based secure routing for improved QoS in WSN using blockchain”, Results in Engineering, Elsevier, Volume 26, 2025, Pages 1-10. https://doi.org/10.1016/j.rineng.2025.104732 [4] S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda & S. Rohith, “A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN”, Scientific Reports, volume 15, 2025, Pages 1-24. https://doi.org/10.1038/s41598-024-82635-w [5] Rahul Priyadarshi, Ravi Ranjan Kumar, Rakesh Ranjan & Padarti Vijaya Kumar, “AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks”, Scientific Reports, volume 15, 2025, Pages 1-19. https://doi.org/10.1038/s41598-025-08677-w [6] Prafull Goswami, Tayyab Khan, Vinay Pathak & Abdulatif Alabdultif, “Machine learning based dynamic trust estimation framework for Securing wireless sensor networks”, Scientific Reports, volume 15, 2025, Pages 1-19. https://doi.org/10.1038/s41598-025-19768-z [7] R Barath Ramesh, S. John Justin Thangaraj, G. Mary Amirtha Sagayee, K. Saravanan, “Detection and prevention in WSN security framework using deep learning against black hole and wormhole attacks”, Ain Shams Engineering Journal, Elsevier, Volume 16, Issue 10, 2025, Pages 1-13. https://doi.org/10.1016/j.asej.2025.103624 [8] Poornima Poojar, Muhibur Rahman, Nagaraj Basavaraj Patil, “Energy Efficient and Secure Clustering and Routing Using Multi-Objective Trust Aware Adaptive Oscillation Weight Cheetah Optimization Algorithm”, International Journal of Intelligent Engineering and Systems, Volume 18, Issue 7, 2025, Pages 210-223. DOI: 10.22266/ijies2025.0831.15 [9] Shaha Al-Otaibi, Sarra Ayouni, Nadeem Sarwar, Asma Irshad & Faizan Ullah, “AI-driven intrusion detection and lightweight authentication framework for secure and efficient medical sensor networks”, Scientific Reports , 2025, Pages 1-27. https://doi.org/10.1038/s41598-025-31981-4 [10] Santosh Anand, Anantha Narayanan V, “Addressing Rogue Nodes and Trust Management: Leveraging Deep Learning-Enhanced Hybrid Trust to Optimize Wireless Sensor Networks Management”, Journal of Robotics and Control (JRC), Volume 6, Issue 2, 2025, Pages 846-861. DOI: 10.18196/jrc.v6i2.25600 [11] Hanadi Ahmad Simmak, Ahmed A El-Douh, Tareef S Alkellezli, Rabih Sbera, Darin shafek, Ahmed Abdelhafeez, “Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models”, Neutrosophic Sets and Systems, Volume 87, Pages 900-909. https://fs.unm.edu/nss8/index.php/111/article/view/6597 [12] Sarvenaz Sadat Khatami, Mehrdad Shoeibi, Reza Salehi and Masoud Kaveh, “Energy-Efficient and Secure Double RIS-Aided Wireless Sensor Networks: A QoS-Aware Fuzzy Deep Reinforcement Learning Approach”, Journal of sensor and actuator network, Volume 14, Issue 1, 2025, Pages 1-29.https://doi.org/10.3390/jsan14010018 [13] Shaik Shafiuddin, Konda Hari Krishna, “TrustRIDR-Net: A Hybrid Trust-Aware Routing Framework Using RFO and DRL for Scalable IoT Networks”, Engineering, Technology & Applied Science Research, Volume 15, Issue 5, 2025, Pages 27421-27429. https://doi.org/10.48084/etasr.12294 [14] Vikas, Charu Wahi, Bharat Bhushan Sagar and Manisha Manjul, “Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks”, Sensors, Volume 25, Issue 8, 2025, Pages 1-36. https://doi.org/10.3390/s25082519 [15] G. Nagarajan, R.I. Minu, Meena Devi R, T. Samraj Lawrence, Sajith P J , M. Viju Prakash, “Eco-conscious multi-factor authentication protocols for energy-aware wireless networks”, Results in Engineering, Elsevier, Volume 28, 2025, Pages 1-14. https://doi.org/10.1016/j.rineng.2025.107807 [16] Mohammad Bilal J, D. Suresh & R. Karthikeyan, “Sleep-wakeup based secure multipath routing in wsn using fennec fox optimized deep learning framework”, Scientific Reports, volume 16, 2026, Pages 1-27. https://doi.org/10.1038/s41598-025-30622-0 [17] Radha Raman Chandan , Hemalatha Thanganadar, Upendra Dwivedi, Surendra Kumar Shukla, Shreyas Pagare, Navruzbek Shavkatov, “Energy-efficient multi factor authentication protocols in sustainable security for wireless networks using machine learning algorithm”, Results in Engineering, Elsevier, Volume 28, 2025, Pages 1-8. https://doi.org/10.1016/j.rineng.2025.107196 [18] Priyanshu Sinha, Dinesh Sahu, Shiv Prakash, Rajkumar Singh Rathore, Pratibha Dixit, Vivek Kumar Pandey & Iryna Hunko, “An efficient data driven framework for intrusion detection in wireless sensor networks using deep learning”, Scientific Reports, volume 15, 2025, Pages 1-25. https://doi.org/10.1038/s41598-025-12867-x [19] Mohamed Loughmari, Anass El Affar, “A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks”, International Journal of Electrical and Computer Engineering (IJECE), volume 15, Issue 2, 2025, Pages. 2089-2097 DOI: 10.11591/ijece.v15i2.pp2089-2097 [20] Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova , Nurzhan Mukazhanov and Assem Ayapbergenova, “Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks”, Future Internet, Volume 17, Issue 7 , 2025, Pages 1-20. https://doi.org/10.3390/fi17070301 [21] Ra’ed M. Al-Khatib, Laila Heilat, Wala Qudah, Salem Alhatamleh and Asef Al-Khateeb, “A novel improved deep learning model based on Bi-LSTM algorithm for intrusion detection in WSN”, Network and heterogeneous Media, Volume 20, Issue 2, Pages 532–565. DOI: 10.3934/nhm.2025024 [22] Kaumudi Keerthana and A. Mahesh Babu, “A Novel Trust Management and Secure Communication Framework for Wireless Sensor Networks”, Engineering, Technology & Applied Science Research, Volume 15, Issue 2, 2025, Pages 21728-21737. https://doi.org/10.48084/etasr.10009 [23] Seyed Salar Sefati, Seyedeh Tina Sefati, Saqib Nazir, Roya Zareh Farkhady and Serban Georgica Obreja, “Federated Reinforcement Learning with Hybrid Optimization for Secure and Reliable Data Transmission in Wireless Sensor Networks (WSNs)”, Mathematics, Volume 13, Issue 19, 2025, Pages 1-37. https://doi.org/10.3390/math13193196 [24] Gajjala Savithri and N. Raghavendra Sai, “A Reliable Hybrid Framework for Anomaly Detection in Secure and Robust Wireless Sensor Networks”, Engineering, Technology & Applied Science Research, Volume 15, Issue 4, 2005, Pages 25789-25797. https://doi.org/10.48084/etasr.11494 [25] Hongwei Zhang, Darshana Upadhyay, Marzia Zaman, Achin Jain, Srinivas Sampalli, “SC-MLIDS: Fusion-based Machine Learning Framework for Intrusion Detection in Wireless Sensor Networks”, Ad Hoc Networks, Elsevier, Volume 175, 2025, Pages 1-18. https://doi.org/10.1016/j.adhoc.2025.103871 [26] Anshika Sharma and Shalli Rani, “An Optimized Ensemble Approach for Securing Wireless Sensor Networks Against Attacks”, ICCK Transactions on Wireless Networks, Volume 1, Issue 1, 2025, Pages 5-15. DOI: 10.62762/TWN.2025.109626 [27] Waqar Khan, Muhammad Usama, Muhammad Shahbaz Khan, Oumaima Saidani, Hussam Al Hamadi, Noha Alnazzawi, Mohammed S. Alshehri and Jawad Ahmad, “Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach”, Frontiers in Sustainable Cities, Volume 7, 2025, Pages 1-19. https://doi.org/10.3389/frsc.2025.1580006 [28] Rahul Mahajan, Srikant V. Sonekar, “Design of an Intelligent Model for Wireless Data Security Using Graph Neural Cryptonets and Evolutionary Protocol Synthesis”, International Journal of Environmental Sciences, Volume 11, Issue 3s, 2025, Pages 458-467. http://theaspd.com/index.php/ijes/article/view/308 [29] Venkatesh Prasad B.S and Roopashree H.R., “Energy aware and secure routing for hierarchical cluster through trust evaluation”, Measurement: Sensors, Elsevier, Volume 33, June 2024, Pages 1-18. https://doi.org/10.1016/j.measen.2024.101132 [30] Ali Adnan Al-Khazraji, Fatimah Abdulridha Rashid, “SafeTrack: Secure Tracking Protocol for Mobile Sensor Nodes in Unstable Wireless Sensor Networks”, IEEE Access, Volume 13, 2025, Pages 113736 – 113751. DOI: 10.1109/ACCESS.2025.3584869

Copyright

Copyright © 2026 D. Mohanapriya, Dr. V. Saravanan. 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.