Fog-Based Secure Chaotic Wireless Sensor Network for ECG Data Transmission in Healthcare Systems
DOI:
https://doi.org/10.22399/ijcesen.1742Keywords:
Fog Computing, Wireless Sensor Networks, Electrocardiogram (ECG), Healthcare Monitoring, Chaotic EncryptionAbstract
The continuous monitoring and transmission of electrocardiogram (ECG) data are essential for the proactive and responsive management of cardiovascular health, particularly in remote and connected healthcare systems. However, ensuring the secure and efficient transmission of this highly sensitive data over Wireless Sensor Networks (WSNs) remains a significant challenge due to the risks of data interception and the need for low-latency processing. This research introduces a novel architecture, the Fog-Based Secured Chaotic Wireless Sensor Network (WSN), specifically designed to address these challenges by integrating fog computing with chaotic encryption methods to enhance data security and efficiency. In this system, fog nodes positioned at the network’s edge serve as intermediary processors, performing pre-processing, data encryption, and storage functions before the data is transmitted to central servers. This approach reduces reliance on cloud infrastructure and minimizes data transmission time, which is critical for real-time applications. The results reveal that the proposed framework enhances data transmission security and achieves a 30% latency reduction examined to conventional cloud-based systems. This fog-based chaotic WSN framework provides a scalable, secure, and efficient solution for ECG data transmission, meeting the evolving demands of connected healthcare and real-time patient monitoring applications.
References
[1] Attaoui, E., Kaissari, S., Jilbab, A., & Bourouhou, A. (2020). Wearable wireless sensors node for heart activity telemonitoring. 2020 International Conference on Electrical and Information Technologies (ICEIT), Rabat, Morocco, 1-6. https://doi.org/10.1109/ICEIT48248.2020.9113208.
[2] Xu, G. (2020). IoT-assisted ECG monitoring framework with secure data transmission for healthcare applications. IEEE Access, 8, 74586-74594. https://doi.org/10.1109/ACCESS.2020.2988059
[3] Kaur, P., Saini, H. S., & Kaur, B. (2022). Modelling of IoT-WSN enabled ECG monitoring system for patient queue updation. International Journal of Advanced Computer Science and Applications, 13(8), 298-304.
[4] Djelouat, H., Al Disi, M., Boukhenoufa, I., Amira, A., Bensaali, F., Kotronis, C., Politi, E., Nikolaidou, M., & Dimitrakopoulos, G. (2020). Real-time ECG monitoring using compressive sensing on a heterogeneous multicore edge-device. Microprocessors and Microsystems, 72, 102839. https://doi.org/10.1016/j.micpro.2019.06.009
[5] Kalpana, P., Kodati, S. S., Smitha, L., Sreekanth, D., Smerat, N., & Akram, M. (2025). Explainable AI-driven gait analysis using wearable Internet of Things (WIoT) and human activity recognition. Journal of Intelligent Systems and Internet of Things, 15(2), 55–75. https://doi.org/10.54216/JISIoT.150205
[6] Sivapalan, G., Nundy, K. K., Dev, S., Cardiff, B., & John, D. (2022). ANNet: A lightweight neural network for ECG anomaly detection in IoT edge sensors. IEEE Transactions on Biomedical Circuits and Systems, 16(1), 24-35. https://doi.org/10.1109/TBCAS.2021.3137646
[7] Rincon, J. A., Guerra-Ojeda, S., Carrascosa, C., & Julian, V. (2020). An IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors, 20(24), 7353. https://doi.org/10.3390/s20247353
[8] K, P. K., Malleboina, M., Nikhitha, M., Saikiran, P., & Kumar, S. N. (2024). Predicting cyberbullying on social media in the big data era using machine learning algorithm. In 2024 International Conference on Data Science and Network Security (ICDSNS) (pp. 1–7). IEEE. https://doi.org/10.1109/ICDSNS62112.2024.10691297
[9] Kalpana, P., Narayana, P., Smitha, L., Madhavi, D., Keerthi, K., Smerat, A., & Akram, M. (2025). Health-Fots: A latency aware fog-based IoT environment and efficient monitoring of body’s vital parameters in smart health care environment. Journal of Intelligent Systems and Internet of Things, 15(1), 144–156. https://doi.org/10.54216/JISIoT.150112
[10] Bhattarai, A., & Peng, D. (2024). An intelligent wearable ECG sensor in intra-medical virtual chain network and inter-medical virtual chain network. SN Computer Science, 5, 329. https://doi.org/10.1007/s42979-024-02696-6
[11] Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7, 81542-81554. https://doi.org/10.1109/ACCESS.2019.292370
[12] Amalraj, J. R., & Lourdusamy, R. (2024). Secure transmission of healthcare data in heterogeneous networks. International Journal for Multidisciplinary Research, 6(2).
[13] Masdari, M., Band, S. S., Qasem, S. N., Sayed, B. T., & Pai, H.-T. (2024). ECG signals-based security and steganography approaches in WBANs: A comprehensive survey and taxonomy. Sustainable Computing: Informatics and Systems, 41, 100937. https://doi.org/10.1016/j.suscom.2023.100937
[14] Said, G., Ghani, A., Ullah, A., Alzahrani, A., Azeem, M., Ahmad, R., & Kim, D. H. (2024). Fog-assisted de-duplicated data exchange in distributed edge computing networks. Scientific Reports, 14(1), 20595. https://doi.org/10.1038/s41598-024-71682-y
[15] Mishra, I., Jain, S., & Maik, V. (2023). Secured ECG signal transmission using optimized EGC with chaotic neural network in WBSN. Computer Systems Science and Engineering, 44, 1109-1123. https://doi.org/10.32604/csse.2023.025999
[16] Das, R., & Inuwa, M. M. (2023). A review on fog computing: Issues, characteristics, challenges, and potential applications. Telematics and Informatics Reports, 10, 100049. https://doi.org/10.1016/j.teler.2023.100049
[17] Mishra, I., Jain, S., & Maik, V. (2023). Secured ECG signal transmission using optimized EGC with chaotic neural network in WBSN. Computer Systems Science and Engineering, 44, 1109-1123. https://doi.org/10.32604/csse.2023.025999
[18] Elhadad, A., Alanazi, F., Taloba, A. I., & Abozeid, A. (2022). Fog computing service in the healthcare monitoring system for managing the real-time notification. Journal of Healthcare Engineering, 2022, 5337733. https://doi.org/10.1155/2022/5337733
[19] Idrees, A. K., & Al-Qurabat, A. K. M. (2021). Energy-efficient data transmission and aggregation protocol in periodic sensor networks based on fog computing. Journal of Network and Systems Management, 29(1). https://doi.org/10.1007/s10922-020-09567-4
[20] Rincon, J. A., Guerra-Ojeda, S., Carrascosa, C., & Julian, V. (2020). An IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors, 20(24), 7353. https://doi.org/10.3390/s20247353.
[21] M, P., B, J., B, B., G, S., & S, P. (2024). Energy-efficient and location-aware IoT and WSN-based precision agricultural frameworks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.480
[22] Radhi, M., & Tahseen, I. (2024). An Enhancement for Wireless Body Area Network Using Adaptive Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(3). https://doi.org/10.22399/ijcesen.409
[23] Kosaraju Chaitanya, & Gnanasekaran Dhanabalan. (2024). Precise Node Authentication using Dynamic Session Key Set and Node Pattern Analysis for Malicious Node Detection in Wireless Sensor Networks. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.613
[24] Vishwanath Pradeep B. (2025). Ethnobotanical perspectives: conventional fever treatments of the gond tribe. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.23
[25] K. Yasotha, K. Meenakshi Sundaram, & J. Vandarkuzhali. (2025). Optimizing Energy Efficiency and Network Performance in Wireless Sensor Networks: An Evaluation of Routing Protocols and Swarm Intelligence Algorithm. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.830
[26] García, R., Carlos Garzon, & Juan Estrella. (2025). Generative Artificial Intelligence to Optimize Lifting Lugs: Weight Reduction and Sustainability in AISI 304 Steel. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.22
[27] M. Devika, & S. Maflin Shaby. (2024). Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.708
[28] Hafez, I. Y., & El-Mageed, A. A. A. (2025). Enhancing Digital Finance Security: AI-Based Approaches for Credit Card and Cryptocurrency Fraud Detection. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.21
[29] M. Karthik, & R. Balakrishna. (2025). Simple Key Distribution For Secure And Energy Efficient Communication In Wireless Sensor Networks. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1461
[30] Fowowe, O. O., & Agboluaje, R. (2025). Leveraging Predictive Analytics for Customer Churn: A Cross-Industry Approach in the US Market. International Journal of Applied Sciences and Radiation Research , 2(1). https://doi.org/10.22399/ijasrar.20
[31] Reddy, S., A. Kamala Kumari, & B. Satish Kumar. (2025). Soft Computing Techniques for Minimizing and Predicting Average Localization Error in Wireless Sensor Networks. International Journal of Computational and Experimental Science and Engineering, 11(2). https://doi.org/10.22399/ijcesen.1035
[32] Ibeh, C. V., & Adegbola, A. (2025). AI and Machine Learning for Sustainable Energy: Predictive Modelling, Optimization and Socioeconomic Impact In The USA. International Journal of Applied Sciences and Radiation Research, 2(1). https://doi.org/10.22399/ijasrar.19
[33] Chui, K. T. (2025). Artificial Intelligence in Energy Sustainability: Predicting, Analyzing, and Optimizing Consumption Trends. International Journal of Sustainable Science and Technology, 1(1). https://doi.org/10.22399/ijsusat.1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering

This work is licensed under a Creative Commons Attribution 4.0 International License.