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

arXiv: https://arxiv.org/abs/2409.16444 · HTML: https://mlsanity.com/paper/2409.16444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 156 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 22 of 22)

- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Reinforcement Learning for Cyber Security](https://mlsanity.com/paper/1906.05799/) (2019)
- [A Comprehensive Survey of Incentive Mechanism for Federated Learning](https://mlsanity.com/paper/2106.15406/) (2021)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [End-to-End Deep Reinforcement Learning for Lane Keeping Assist](https://mlsanity.com/paper/1612.04340/) (2016)
- [Application of Segment Anything Model for Civil Infrastructure Defect Assessment](https://mlsanity.com/paper/2304.12600/) (2023)
- [Artificial Intelligence for the Metaverse: A Survey](https://mlsanity.com/paper/2202.10336/) (2022)
- [Machine Learning for Networking: Workflow, Advances and Opportunities](https://mlsanity.com/paper/1709.08339/) (2017)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering](https://mlsanity.com/paper/2111.04105/) (2021)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regions](https://mlsanity.com/paper/2305.14460/) (2023)
- [Fog Computing Approaches in Smart Cities: A State-of-the-Art Review](https://mlsanity.com/paper/2011.14732/) (2020)
- [Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14263/) (2021)
- [Comparative Analysis of Gradient-Based Optimization Techniques Using Multidimensional Surface 3D Visualizations and Initial Point Sensitivity](https://mlsanity.com/paper/2409.04470/) (2024)
- [Machine Learning in/for Blockchain: Future and Challenges](https://mlsanity.com/paper/1909.06189/) (2019)
- [Blockchain based Proxy Re-Encryption Scheme for Secure IoT Data Sharing](https://mlsanity.com/paper/1811.02276/) (2018)
- [Blockchain-based Data Provenance for the Internet of Things](https://mlsanity.com/paper/1905.06852/) (2019)
- [A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems](https://mlsanity.com/paper/2307.06659/) (2023)
- [AI-Protected Blockchain-based IoT environments: Harnessing the Future of Network Security and Privacy](https://mlsanity.com/paper/2405.13847/) (2024)
- [Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns](https://mlsanity.com/paper/2409.18976/) (2024)

## Built on by (most cited first, 2 of 2)

- [PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Security](https://mlsanity.com/paper/2503.00331/) (2025)
- [Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security](https://mlsanity.com/paper/2410.01016/) (2024)

## Abstract (arXiv)

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.