5G with Fog Computing based Privacy System in Data Analytics for Healthcare System by AI Techniques

• Published in: International journal of communication networks and information security 2022-12, Vol.14 (3), p.313-329
• Main Authors: Solís, Encarnación Maria Torres, Cotrina-Aliaga, Juan Carlos, Castro-Cayllahua, Fidel, Samaniego, Severo Simeón Calderón, Alarcon, Betsy Nordie Pardo, Cruz, Yoni Magali Maita
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Breast cancer; Cloud computing; Computer architecture; Data mining; Data processing; Data transmission; Diabetes; Efficiency; Electronic health records; Energy consumption; Health care; Internet of Things; Machine learning; Magnetic resonance imaging; Medical prognosis; Optimization; Patients; Performance enhancement; Privacy; Scheduling; Task scheduling
• ISSN: 2073-607X; 2076-0930

Fog computing architecture is an extended version of the cloud computing architecture to reduce the load of the data transmission and storage in the cloud platform. The architecture of the fog increases the performance with improved efficiency compared with the cloud environment. The fog computing architecture uses the 5G based Artificial Intelligence (AI) technology for performance enhancement. However, due to vast range of data availability privacy is challenging in the fog environment. This paper proposed a Medical Fog Computing Load Scheduling (MFCLS) model for data privacy enhancement. The developed architecture model of optimization-based delay scheduling for task assignment in the fog architecture. The healthcare data were collected and processed with the 5G technology. The developed MFCLS model uses the entropy-based feature selection for the healthcare data. The proposed MFCLS considers the total attributes of 13 for the evaluation of features. With the provision of service level violation, the fog computing network architecture will be provided with reduced energy consumption. The developed load balancing reduced the service violation count with the provision of desired data privacy in the fog model. The estimation of the time frame is minimal for the proposed MFCLS model compared with the existing DAG model. The performance analysis expressed that SLRVM and ECRVM achieved by the proposed MFCLS are 28 and 43 respectively. The comparative examination of the proposed MFCLS model with the existing DAG model expressed that the proposed model exhibits ~6% performance enhancement in the data privacy for the healthcare data.