A Novel Direct, Indirect and Mutual Trust-Based Blockchain Modeling for Validation of Data Reliability in Integrated Edge Computing Environment

The tremendous growth of the Internet of Things has led to a rapid increase in data. When there is voluminous data to be processed, the speed of data retrieval, response time, and storage becomes a huge problem. To solve this issue, the integration of edge computing and blockchain technology is proposed in this study. Edge computing and blockchain are two dominants reigning in the data world today. Their integration will eventually result in a paradigm shift from centralized data management to a more decentralized form. While integrating them, security and trust become a problem because, in edge computing, different nodes participate in the link, which may follow different protocols. To resolve this issue, authors propose two blockchain trust models, namely the direct and indirect trust-based blockchain model and the mutual trust chain-based blockchain model. The proposed models are evaluated against standard blockchain techniques like Bitcoin, Ethereum, and Hyperledger Fabric, and it has been proven that the proposed mutual trust chain algorithm outperforms all the other existing technologies. The performance metrics such as throughput, efficiency, packet delivery rate, execution time, delay, packet drop ratio, etc., are calculated under the introduction of attacks like interference and eavesdropping, malicious code injection, and sleep deprivation attacks to validate the reliability of data in an edge computing environment. The proposed algorithms have higher scalability, lower latency, and better efficiency.