Blockchain Security in Edge Applications with Novel Load Balancing Approach

Abstract

The rapid growth of edge computing and the increasing adoption of block-chain technology have led to the emergence of block-chain-based applications at the edge. However, ensuring security in such edge applications poses unique challenges due to the distributed nature of edge devices and the inherent vulnerabilities of block-chain networks. This research addresses these challenges by proposing a novel load-balancing (NLB) approach for enhancing blockchain security in edge applications. Firstly, the study investigates the security vulnerabilities of blockchain networks deployed at the edge. It analyzes the potential threats such as Sybil attacks, double-spending attacks, and collusion attacks that can exploit the distributed nature of edge devices. The research also explores the limitations of existing load-balancing techniques in ensuring security in edge applications. To mitigate these vulnerabilities, a novel load-balancing approach is proposed. The system leverages machine learning algorithms and block-chain consensus mechanisms to distribute computational tasks across edge devices securely. By considering the trustworthiness and capabilities of individual edge devices, the load-balancing algorithm optimizes the allocation of functions, minimizing the risk of malicious attacks. Furthermore, the proposed approach incorporates a reputation system that evaluates the behavior and reliability of edge devices based on their past performance. The effectiveness of the proposed load-balancing approach is evaluated through extensive simulations and comparative analysis with existing load-balancing techniques. The results demonstrate that the novel approach significantly improves the security and performance of block-chain in edge applications, effectively mitigating the impact of various attacks and ensuring reliable and secure data processing at the edge.