Optimizing Security Protocols for IoT in Healthcare to Ensure Privacy in Smart Patient Monitoring Systems
The Internet of Things (IoT) might improve healthcare. Smart patient tracking systems may benefit. There are many networked gadgets, creating new security risks. Privacy and accessibility of patient data require strong security measures. This paper addresses this requirement by proposing a comprehensive hospital IoT security approach for patient-tracking smart devices. Our solution's stringent methodology uses contemporary encryption, blockchain, and machine learning-based anomalous detection. This combination aims to provide a full security system for all assaults. Healthcare IoT is complicated and ever-changing; hence, this is essential. The introduction emphasizes the need for patient data security in smart tracking systems to set the stage for constructing and evaluating the recommended security design. The study's security design outperforms current techniques in several key aspects. The recommended approach outperforms Triple DES (88%), RSA (85%), AES (90%), and SHA-256 (88%), with a security level of 95%. This new approach computes in 4 milliseconds, much faster than Triple DES (7 milliseconds), RSA (5 milliseconds), AES (6 milliseconds), and SHA-256 (8 milliseconds). These data demonstrate that the proposed security architecture is superior and can secure hospital IoT. In the ever-changing world of healthcare IoT, the recommended response protects patient data safety and accuracy in smart patient monitoring systems.
Authors
- Sorabh Lakhanpal (ORCID: https://orcid.org/0000-0001-6188-4266)
- A.I. Saeed (ORCID: https://orcid.org/0000-0001-8660-9578)
- Eakta Kumari (ORCID: https://orcid.org/0000-0003-4909-4581)
- J Karthiyayini
- Amit Kumar Sharma (ORCID: https://orcid.org/0000-0001-6922-8757)
- Y. Krishna Bhargavi
Institutions
- Lovely Professional University (IN)
- Galgotias University (IN)
- Iraqi University (IQ)
- Birla Institute of Technology and Science - Hyderabad Campus (IN)
Publication Details
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1109/ic363308.2025.10956713
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00