FC-SEEDA: fog computing-based secure and energy efficient data aggregation scheme for Internet of healthcare Things

• Published in: Neural computing & applications 2024, Vol.36 (1), p.241-257
• Main Authors: Chakraborty, Chinmay, Othman, Soufiene Ben, Almalki, Faris A., Sakli, Hedi
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cloud computing; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Cybersecurity; Data collection; Data management; Data Mining and Knowledge Discovery; Edge computing; Energy consumption; Health care; Image Processing and Computer Vision; Internet; Internet of Things; Performance assessment; Probability and Statistics in Computer Science; S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics; Sensors; Special Issue on Improving Healthcare outcomes using Multimedia Big Data Analytics
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-023-08270-0

IoT is used in healthcare to monitor patients via wearable sensors to measure different physiological information. This collected information can be stored, processed, and made available to doctors to give a consultation at any time, which improves the efficiency of the traditional medical systems. Secure data collection and transfer to centralized servers in healthcare applications employing IoT is quite challenging to protect against several attacks for illegal data access. Furthermore, the bulk of IoT devices have physical limitations in processing and storage. Securing and effectively aggregating sensitive patient information on the Internet of Healthcare Things remains difficult. This research article proposes FC-SEEDA: Fog Computing-based Secure and Energy-Efficient Data Aggregation scheme for Internet of healthcare Things. By leveraging the distributed nature and other extended capabilities of Fog Computing, the main objective of this proposed scheme is to decrease the communication overhead and energy consumption while maintaining safe and secure aggregation of the healthcare data between medical sensors and cloud servers. The proposed system is experimentally developed using the E-health sensor shield V2.0 platform. Security analysis demonstrates that the proposed aggregation protocol achieves desirable security properties. Performance comparisons show that our newly proposed protocol significantly reduces computing overhead compared to the latest protocols in the field.