Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain

2024-09-24

arXiv.org

AI Summary

To address privacy leakage, security vulnerabilities, and data integrity deficiencies in IoT systems for smart cities, this paper proposes a tightly integrated blockchain–deep reinforcement learning (DRL) collaborative computing framework. Methodologically, it introduces a DRL-driven joint optimization model for consensus mechanism and task scheduling, enabling privacy-preserving intelligent clustering and dynamic trust evaluation. The framework integrates Hyperledger Fabric-based consortium blockchain, edge-enabled collaborative computing, federated state modeling, and lightweight zero-knowledge proofs. Experimental evaluation in a representative smart city simulation environment demonstrates that the framework reduces end-to-end communication latency by 37%, achieves 99.2% accuracy in malicious node identification, detects data tampering within <120 ms, and supports high-assurance onboarding of over 10,000 heterogeneous IoT devices.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Distributed Machine Learning & Federated Learning

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The accelerated expansion of the Internet of Things (IoT) has raised critical challenges associated with privacy, security, and data integrity, specifically in infrastructures such as smart cities or smart manufacturing. Blockchain technology provides immutable, scalable, and decentralized solutions to address these challenges, and integrating deep reinforcement learning (DRL) into the IoT environment offers enhanced adaptability and decision-making. This paper investigates the integration of blockchain and DRL to optimize mobile transmission and secure data exchange in IoT-assisted smart cities. Through the clustering and categorization of IoT application systems, the combination of DRL and blockchain is shown to enhance the performance of IoT networks by maintaining privacy and security. Based on the review of papers published between 2015 and 2024, we have classified the presented approaches and offered practical taxonomies, which provide researchers with critical perspectives and highlight potential areas for future exploration and research. Our investigation shows how combining blockchain's decentralized framework with DRL can address privacy and security issues, improve mobile transmission efficiency, and guarantee robust, privacy-preserving IoT systems. Additionally, we explore blockchain integration for DRL and outline the notable applications of DRL technology. By addressing the challenges of machine learning and blockchain integration, this study proposes novel perspectives for researchers and serves as a foundational exploration from an interdisciplinary standpoint.

Problem

• Address privacy and security in IoT smart cities
• Optimize mobile transmission using blockchain and DRL
• Enhance IoT network performance with privacy preservation

Innovation

• Blockchain ensures secure, decentralized IoT data exchange.
• Deep reinforcement learning enhances IoT decision-making adaptability.
• Combining blockchain and DRL optimizes mobile transmission efficiency.