[Paper Review] Blockchain technology research and application: a systematic literature review and future trends

Min An, Qiyuan Fan+2 more|arXiv (Cornell University)|Jun 26, 2023

Blockchain Technology Applications and SecurityComputer Science3 citations

TL;DR

This systematic literature review synthesizes blockchain research and applications across finance, IoT, smart grids, and intelligent transportation, identifying key challenges like scalability and security. It proposes a structured framework for blockchain testing and explores AI integration, particularly federated learning and reinforcement learning, to enhance performance and privacy.

ABSTRACT

Blockchain, as the basis for cryptocurrencies, has received extensive attentions recently. Blockchain serves as an immutable distributed ledger technology which allows transactions to be carried out credibly in a decentralized environment. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability, security and other issues waiting to be overcome. This article provides a comprehensive overview of blockchain technology and its applications. We begin with a summary of the development of blockchain, and then give an overview of the blockchain architecture and a systematic review of the research and application of blockchain technology in different fields from the perspective of academic research and industry technology. Furthermore, technical challenges and recent developments are also briefly listed. We also looked at the possible future trends of blockchain.

Motivation & Objective

• To provide a comprehensive, systematic review of blockchain technology research and applications across diverse industries.
• To identify and analyze key technical challenges such as scalability, security, and performance limitations in blockchain systems.
• To explore emerging trends, including blockchain integration with AI (e.g., federated learning, reinforcement learning), and privacy-preserving mechanisms.
• To propose a structured blockchain testing framework covering selection, performance, and operation & maintenance phases.
• To outline future research directions for blockchain in decentralized systems, autonomous networks, and trustless environments.

Proposed method

• Conducted a systematic literature review of academic and industry research on blockchain from 2008 to 2023, focusing on architecture, consensus mechanisms, and cross-domain applications.
• Categorized blockchain applications into financial services, IoT, smart grids, intelligent transportation, and reputation systems based on empirical studies and industry deployments.
• Evaluated consensus algorithms (e.g., PoW, PoS, tree-chain) and proposed enhancements to mitigate 51% and double-spending attacks using weighted mining and random group selection.
• Proposed a three-phase blockchain testing framework: (1) selection based on use-case alignment, (2) performance testing using single-indicator and scenario-based benchmarks, and (3) operation & maintenance evaluation for stability and usability.
• Explored integration of AI with blockchain via reinforcement learning for dynamic optimization and federated learning for privacy-preserving model training on distributed ledgers.
• Reviewed privacy-preserving techniques including zero-knowledge proofs and homomorphic encryption, and evaluated their applicability in real-world blockchain deployments.

Experimental results

Research questions

• RQ1 What are the dominant application domains of blockchain technology, and how do they vary across industries?
• RQ2 What are the key technical challenges—especially scalability, security, and performance—limiting blockchain adoption?
• RQ3 How can consensus algorithms be enhanced to resist 51% and double-spending attacks?
• RQ4 What role does AI, particularly federated learning and reinforcement learning, play in improving blockchain performance and privacy?
• RQ5 How can a standardized, multi-phase testing framework be designed to evaluate blockchain systems objectively across different deployment scenarios?

Key findings

• Blockchain applications are most mature in finance but are rapidly expanding into IoT, smart grids, and intelligent transportation, with growing use of smart contracts and reputation systems.
• Security remains a critical challenge, with 51% and double-spending attacks still viable under low-resource conditions, though enhanced consensus mechanisms can increase attack costs by two orders of magnitude.
• Federated learning (FL) combined with blockchain enables privacy-preserving AI model training across decentralized nodes, reducing data leakage risks.
• Reinforcement learning shows promise in optimizing blockchain performance, such as dynamic consensus selection and resource allocation in distributed networks.
• The proposed three-phase blockchain testing framework—selection, performance, and operation & maintenance—provides a structured approach to evaluating blockchain systems objectively.
• Privacy-preserving techniques like zero-knowledge proofs and homomorphic encryption are increasingly adopted but require further optimization for real-time scalability in production systems.