mlsanityData to 14 Oct 2026

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

arXiv 2409.16444 ↗First posted 24 Sep 2024cs.AI cs.CRcited by 2

Not on the map. Why?

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.

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

22 of 156 references.

References · 22

  1. 2017A Brief Survey of Deep Reinforcement Learningcited by 483
  2. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 167
  3. 2019Deep Reinforcement Learning for Cyber Securitycited by 95
  4. 2021A Comprehensive Survey of Incentive Mechanism for Federated Learningcited by 38
  5. 2019Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challengescited by 37
  6. 2016End-to-End Deep Reinforcement Learning for Lane Keeping Assistcited by 27
  7. 2023Application of Segment Anything Model for Civil Infrastructure Defect Assessmentcited by 27
  8. 2022Artificial Intelligence for the Metaverse: A Surveycited by 25
  9. 2017Machine Learning for Networking: Workflow, Advances and Opportunitiescited by 20
  10. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  11. 2021DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clusteringcited by 7
  12. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
10 more
  1. 2023Supervised Multi-Regional Segmentation Machine Learning Architecture for Digital Twin Applications in Coastal Regionscited by 7
  2. 2020Fog Computing Approaches in Smart Cities: A State-of-the-Art Reviewcited by 6
  3. 2021Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learningcited by 5
  4. 2024Comparative Analysis of Gradient-Based Optimization Techniques Using Multidimensional Surface 3D Visualizations and Initial Point Sensitivitycited by 4
  5. 2019Machine Learning in/for Blockchain: Future and Challengescited by 2
  6. 2018Blockchain based Proxy Re-Encryption Scheme for Secure IoT Data Sharingcited by 1
  7. 2019Blockchain-based Data Provenance for the Internet of Thingscited by 1
  8. 2023A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systemscited by 1
  9. 2024AI-Protected Blockchain-based IoT environments: Harnessing the Future of Network Security and Privacycited by 1
  10. 2024Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaignscited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025PINN-DT: Optimizing Energy Consumption in Smart Building Using Hybrid Physics-Informed Neural Networks and Digital Twin Framework with Blockchain Securitycited by 2
  2. 2024Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Securitycited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Blockchain and AI: Securing Intelligent Networks for the Future
  2. 2025Adaptive Trust Consensus for Blockchain IoT: Comparing RL, DRL, and MARL Against Naive, Collusive, Adaptive, Byzantine, and Sleeper Attacks
  3. 2023A Blockchain Solution for Collaborative Machine Learning over IoT
  4. 2025(DEMO) Deep Reinforcement Learning Based Resource Allocation in Distributed IoT Systems
  5. 2026Blockchain-Enabled Artificial Intelligence and AI Agents for Secure Data Sharing and Cybersecurity Applications
  6. 2024A Deep Reinforcement Learning Approach for Security-Aware Service Acquisition in IoT
  7. 2024Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach
  8. 2026An AI-Based Solution for Secure Service Provisioning in IoT