Lightweight Health Data Security Protocol with Multi-Block Chaining Principles for Intelligent Wireless IoT-Fog Systems
V.R. Azhaguramyaa and J. Janet
Internet of Things (IoT)-Fog is a suitable environment for processing and analyzing the health data at huge measure. The distributed environment of fog systems and properly tuned data classification techniques assist the efficient health data diagnosis process with optimal response time. However, lack of optimal computation management principles, inefficient multi- modal sensor management, deficiency in the establishment of homogeneous fog computing units, distributed data access problems and dual-end (fog and user) security flaws are considered as crucial research problems under fog operational conditions. Against these research problems, the achievements of current techniques are not considering the suitable solutions under distributed health care computing environment. In this regard, the technical gap is identified as a basic idea to develop proposed health care solution. In this case, the proposed model has been implemented for achieving Multi-Block Fog based Health Care Systems (MBFH) to ensure the given deficits of exiting techniques through this newly developed IoT-Fog environment. In this environment, heterogeneous types of IoT sensors are collecting biosignals and transmit the data in to Node-MCU panel at edge layer. Consequently, the heterogeneous sensor data collected at Node-MCU’s are classified and transmitted as a set of homogeneous data in to multiple fog nodes through wireless communication medium (Wireless-Fidelity (Wi-Fi)). This phase accommodates multi-modal sensor data in to fog units with homogeneous contents to be analyzed. In the next phase, the proposed model executes the dual-end multi-blockchain procedures to enable distributed consensus principles (sensor based data security) at fog level and centralized consensus principles (patient centric data security) at cloud level. During the analysis phase, the proposed model initiates Generative Adversarial Network (GAN) based sampling mechanisms and lightweight Gate Recurrent Units (GRU) at distributed fog centers. The proposed model has been implemented using IoT layer, fog layer and cloud layer functions to diagnose the regular health abnormalities securely and reliably. From the proposed MBFH model, the data collected from multiple biosensors deployed at patient body can be optimally (homogeneous distribution to fog units) collected and analyzed with limited computation load at fog units. Consequently, the analyzed data shall be transferred to cloud storage safely. In addition, the proposed MBFH model assures fog level health data authentication and cloud level data authentication solutions. In the experiment bed, the proposed MBFH model is developed and compared with related techniques such as DL based Health Monitoring System (DLHM), Blockchain and Fog Computing for Health Care Solutions (BFHL) and Health-Fog with Deep Learning Approach (HLF). The implementation section of this article shows the significant performance of proposed MBFH with improved efficiency rate between 8% and 15% than the existing techniques through various performance metrics.
Keywords: Fog Computing, Health Care, IoT, Wireless Networks, Cloud and Blockchain