EFFICIENT A LIGHTWEIGHT MULTI-ZONE DAG BLOCKCHAIN FRAMEWORK FOR SECURING MEDICAL DATA IN FOG-BASED IOT APPLICATIONS

Abstract

Blockchain technology is regarded as a promising security solution for medical data applications linked to the Internet of Things (IoT). The fundamental purpose of blockchain networks in medical care is to ensure security. Blockchain has a linear structure that expands with the amount of transactions entered. This rise in size is the fundamental hurdle to blockchain adoption, making it unsuitable for resource-constrained IoT applications. Furthermore, conventional consensus algorithms such as PoW, PoS, and safer agreement techniques based on the Proof-of-Work (PoW) paradigm are being developed to solve these difficulties. This engineering framework prioritizes the security of information from IoT device sensors. This paper solves these concerns by introducing a novel lightweight blockchain topology and consensus approach. The Multi-Zone Direct Acyclic Graph (DAG) Blockchain (Multizone-DAG-Blockchain) framework is proposed for fog-based IoT applications. In this application, fog computing technology is combined with IoT to offload IoT workloads to fog nodes, lowering IoT device power consumption. A non-clone able physical function-based validation technique (DPUF-VM) verifies multiple authentication certificates on the blockchain to ensure that both IoT and fog nodes are who they claim to be. Each transaction on the blockchain is saved as a hash function using the lightweight Cube Hash algorithm and signed with the Four-Q-Curve method. Sensitive data is encrypted and stored in the cloud. Fog nodes protect data while lowering IoT nodes' energy consumption and complexity. To begin, the fog node does a redundancy analysis using the Jaccard Similarity (JS) metric. It then does a sensitivity analysis with the Neutrosophic Neural Intelligent Network (N2IN) algorithm. A simple proof-of-authentication (PoAh) method is given for transaction validation, and the bi-objective spiral optimization (BoSo) algorithm picks the best consensus node to do it. This approach is optimized for energy consumption, storage cost, response time, and throughput.