journal article·2020Open access

Intelligent Tunicate Swarm-Optimization-Algorithm-Based Lightweight Security Mechanism in Internet of Health Things

Gia Nhu Nguyen, Nin Ho Le Viet, Gyanendra Prasad Joshi, Bhanu Shrestha

Computers, Materials & Continua· Volume 66· Issue 1

Abstract

Fog computing in the Internet of Health Things (IoHT) is promising owing to the increasing need for energy- and latency-optimized health sector provisioning. Additionally, clinical data (particularly, medical image data) are a delicate, highly protected resource that should be utilized in an effective and responsible manner to fulfil consumer needs. Herein, we propose an energy-effi- cient fog-based IoHT with a tunicate swarm-optimization-(TSO)-based lightweight Simon cipher to enhance the energy efficiency at the fog layer and the security of data stored at the cloud server. The proposed Simon cipher uses the TSO algorithm to select the optimal keys that will minimize the deterioration of quality between the original and reconstructed (decrypted) images. In this study, the decrypted image quality is preserved by the peak signal-to-noise ratio (PSNR) such that consumers can generate precise medical reports from IoHT devices at the application level. Moreover, a lightweight encryption step is implemented in the fog to improve energy efficiency and reduce additional computations at the cloud server. Experimental results indicate that the TSO-Simon model achieved a high PSNR of 61.37 dB and a pixel change rate of 95.31.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: IoT and Edge/Fog Computing · Internet of Things and AI · Advanced Computing and Algorithms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (30)

Show source-provided references
1. Fog assisted healthcare architecture for pre-operative support to reduce latency10.1016/j.procs.2020.03.447 ↗
2. Toward a heterogeneous mist, fog, and cloud-based framework for the internet of healthcare things10.1109/JIOT.2018.2876088 ↗
3. Internet of health things: Toward intelligent vital signs monitoring in hospital wards10.1016/j.artmed.2018.05.005 ↗
4. Blockchain platform for industrial healthcare: Vision and future opportunities10.1016/j.comcom.2020.02.058 ↗
5. Accelerating smart eHealth services execution at the fog computing infrastructure10.1016/j.future.2018.07.001 ↗
6. An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services10.1016/j.procs.2020.03.424 ↗
7. Fog computing for healthcare 4.0 environment: Opportunities and challenges10.1016/j.compeleceng.2018.08.015 ↗
8. Lightweight ciphertext-policy attribute-based encryption scheme for data privacy and security in cloud-assisted IoT10.1016/B978-0-12-818014-3.00005-X ↗
9. A fog based middleware for automated compliance with OECD privacy principles in internet of healthcare things10.1109/ACCESS.2016.2631546 ↗
10. Flexible IoT security middleware for end-to-end cloud-fog communication10.1016/j.future.2017.12.031 ↗
11. FogIoHT: A weighted majority game theory based energy-efficient delay-sensitive fog network for internet of health things10.1016/j.iot.2020.100181 ↗
12. Enabling technologies for fog computing in healthcare IoT systems10.1016/j.future.2018.07.049 ↗
13. Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook10.1016/j.inffus.2019.06.004 ↗
14. Security, privacy & efficiency of sustainable cloud computing for big data & IoT
15. Hash polynomial two factor decision tree using IoT for smart health care scheduling10.1016/j.eswa.2019.112924 ↗
16. Shankar · Lecture Notes in Electrical Engineering · 2019 · 56410.1007/978-3-030-20816-5 ↗
17. An efficient optimal key based chaos function for medical image security10.1109/ACCESS.2018.2874026 ↗
18. Optimal feature selection-based medical image classification using deep learning model in internet of medical things10.1109/ACCESS.2020.2981337 ↗
19. Adaptive optimal multi key based encryption for digital image security10.1002/cpe.5122 ↗
20. Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model10.1016/j.patrec.2020.02.026 ↗
21. Electrocardiogram soft computing using hybrid deep learning CNN-ELM10.1016/j.asoc.2019.105778 ↗
22. A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection10.1007/s00500-014-1332-7 ↗
23. Page-sharing-based virtual machine packing with multi-resource constraints to reduce network traffic in migration for clouds10.1016/j.future.2019.02.043 ↗
24. Communication-efficient data aggregation tree construction for complex queries in IoT applications10.1109/JIOT.2018.2882820 ↗
25. Software defined networking for energy harvesting Internet of Things10.1109/JIOT.2018.2799936 ↗
26. LogEvent-to-vector based anomaly detection for large-scale logs in Internet of things
27. Edge-computing-based trustworthy data collection model in the internet of things10.1109/JIOT.2020.2966870 ↗
28. Privacy-enhanced data collection based on deep learning for internet of vehicles10.1109/TII.2019.2962844 ↗
29. Lightweight deep network for traffic sign classification10.1007/s12243-019-00731-9 ↗
30. Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization10.1016/j.engappai.2020.103541 ↗