Summary of Artificial Intelligence For Secured Information Systems in Smart Cities: Collaborative Iot Computing with Deep Reinforcement Learning and Blockchain, by Amin Zakaie Far et al.
Artificial Intelligence for Secured Information Systems in Smart Cities: Collaborative IoT Computing with Deep Reinforcement Learning and Blockchain
by Amin Zakaie Far, Mohammad Zakaie Far, Sonia Gharibzadeh, Hajar Kazemi Naeini, Leila Amini, Shiva Zangeneh, Morteza Rahimi, Saeed Asadi
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the integration of blockchain and deep reinforcement learning (DRL) to optimize mobile transmission and secure data exchange in IoT-assisted smart cities. The combination of DRL and blockchain enhances the performance of IoT networks by maintaining privacy and security, while also addressing challenges associated with privacy, security, and data integrity. The study reviews papers published between 2015 and 2024, classifying approaches and providing practical taxonomies for researchers. By combining blockchain’s decentralized framework with DRL, the paper proposes novel perspectives for addressing privacy and security issues in IoT systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how combining blockchain technology with deep reinforcement learning (DRL) can make the Internet of Things (IoT) more secure and private. The study looks at how to use this combination to improve mobile transmission efficiency and create robust, privacy-preserving IoT systems. The authors also review previous research in this area and provide a way to classify different approaches. |
Keywords
» Artificial intelligence » Reinforcement learning
