A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0 (2311.13731v1)
Abstract: Web 3.0, as the third generation of the World Wide Web, aims to solve contemporary problems of trust, centralization, and data ownership. Driven by the latest advances in cutting-edge technologies, Web 3.0 is moving towards a more open, decentralized, intelligent, and interconnected network. However, increasingly widespread data breaches have raised awareness of online privacy and security of personal data. Additionally, since Web 3.0 is a sophisticated and complex convergence, the technical details behind it are not as clear as the characteristics it presents. In this survey, we conduct an in-depth exploration of Web 3.0 from the perspectives of blockchain, artificial intelligence, and edge computing. Specifically, we begin with summarizing the evolution of the Internet and providing an overview of these three key technological factors. Afterward, we provide a thorough analysis of each technology separately, including its relevance to Web 3.0, key technology components, and practical applications. We also propose decentralized storage and computing solutions by exploring the integration of technologies. Finally, we highlight the key challenges alongside potential research directions. Through the combination and mutual complementation of multiple technologies, Web 3.0 is expected to return more control and ownership of data and digital assets back to users.
- Statista, “Worldwide digital population July 2022,” https://www.statista.com/statistics/617136/digital-population-worldwide/#statisticContainer, Sep. 2022.
- G. Korpal and D. Scott, “Decentralization and Web3 technologies,” 2022, techRxiv preprint.
- Web3Foundation, “Web 3.0 technology stack,” https://web3.foundation/about/, 2022.
- S. Vojíř and J. Kučera, “Towards re-decentralized future of the web: Privacy, security and technology development,” Acta Informatica Pragensia, vol. 2021, no. 3, pp. 349–369, 2021.
- J. Zarrin et al., “Blockchain for decentralization of Internet: prospects, trends, and challenges,” Cluster Computing, vol. 24, no. 4, pp. 2841–2866, May 2021.
- Q. Wang et al., “Exploring Web3 from the view of blockchain,” 2022, arXiv preprint arXiv:2206.08821.
- P. Ray, “Web3: A comprehensive review on background, technologies, applications, zero-trust architectures, challenges and future directions,” Internet of Things and Cyber-Physical Systems, 2023.
- W. Gan et al., “Web 3.0: The future of Internet,” 2023, arXiv preprint arXiv:2304.06032.
- X. Ren et al., “Building resilient Web 3.0 with quantum information technologies and blockchain: An ambilateral view,” 2023, arXiv preprint arXiv:2303.13050.
- R. Huang et al., “An overview of Web3. 0 technology: Infrastructure, applications, and popularity,” 2023, arXiv preprint arXiv:2305.00427.
- M. Shen et al., “Artificial intelligence for Web 3.0: A comprehensive survey,” 2023, arXiv preprint arXiv:2309.09972.
- A. Beniiche et al., “Society 5.0: Internet as if people mattered,” IEEE Wireless Communications, vol. 29, no. 6, pp. 160–168, 2022.
- A. Park et al., “Interoperability: Our exciting and terrifying Web3 future,” Business Horizons, vol. 66, no. 4, pp. 529–541, 2023.
- T. O’Reilly, “What is Web 2.0? design patterns and business models for the next generation of software,” https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html, Sep. 2005.
- Ethereum, “Introduction to Web3,” https://ethereum.org/en/web3/, May. 2022.
- T. Berners-Lee et al., “Solid protocol,” https://solidproject.org/TR/protocol#terminology, Dec. 2020.
- World-Wide-Web-Foundation, “Three challenges for the web, according to its inventor,” https://webfoundation.org/2017/03/web-turns-28-letter/, Mar. 2017.
- PewResearchCenter, “Americans and privacy: Concerned, confused and feeling lack of control over their personal information,” https://www.pewresearch.org/internet/2019/11/15/americans-and-privacy-concerned-confused-and-feeling-lack-of-control-over-their-personal-information/pi_2019-11-14_privacy_0-01/, Nov. 2019.
- Statista, “Number of data records exposed worldwide from 1st quarter 2020 to 1st quarter 2023,” https://www.statista.com/statistics/1307426/number-of-data-breaches-worldwide/, Jun. 2023.
- S. Duan, M. Reiter, and H. Zhang, “BEAT: Asynchronous BFT made practical,” in Proceedings of ACM SIGSAC Conference on Computer and Communications Security, Jan. 2018, pp. 2028–2041.
- M. Yin, D. Malkhi, M. Reiter, G. Gueta, and I. Abraham, “Hotstuff: BFT consensus with linearity and responsiveness,” in Proceedings of the ACM Symposium on Principles of Distributed Computing (PODC), Jul. 2019, pp. 347–356.
- A. Miller, Y. Xia, K. Croman, E. Shi, and D. Song, “The Honey Badger of BFT protocols,” in ACM SIGSAC Conference on Computer and Communications Security, Oct. 2016.
- M. Jalalzai, C. Busch, and G. Richard, “Proteus: A scalable BFT consensus protocol for blockchains,” in IEEE International Conference on Blockchain (Blockchain), Jul. 2019.
- G. Gueta, I. Abraham, S. Grossman, D. Malkhi, B. Pinkas, M. Reiter, D. Seredinschi, O. Tamir, and A. Tomescu, “SBFT: A scalable and decentralized trust infrastructure,” in Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Jun. 2019.
- P. Li, G. Wang, X. Chen, F. Long, and W. Xu, “Gosig: a scalable and high-performance Byzantine consensus for consortium blockchains,” in Proceedings of the 11th ACM Symposium on Cloud Computing, 2020, pp. 223–237.
- S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008.
- G. Wood, “Polkadot: Vision for a heterogeneous multi-chain framework,” White Paper, vol. 21, pp. 2327–4662, 2016.
- A. Kiayias, A. Russell, B. David, and R. Oliynykov, “Ouroboros: A provably secure Proof-of-Stake blockchain protocol,” in Advances in Cryptology – CRYPTO. Lecture Notes in Computer Science, vol. 10401, 2017.
- Y. Gilad, R. Hemo, S. Micali, G. Vlachos, and N. Zeldovich, “Algorand: Scaling Byzantine agreements for cryptocurrencies,” in Proceedings of Symposium on Operating Systems Principles, Oct. 2017, pp. 51–68.
- I. Bentov, C. Lee, A. Mizrahi, and M. Rosenfeld, “Proof of Activity: Extending Bitcoin’s Proof of Work via Proof of Stake,” ACM SIGMETRICS Performance Evaluation Review, vol. 42, no. 3, pp. 34–37, 2014.
- A. Yakovenko, “Solana: A new architecture for a high performance blockchain v0. 8.13,” Whitepaper, 2018.
- K. Karantias, A. Kiayias, and D. Zindros, “Proof-of-Burn,” in International conference on financial cryptography and data security. Springer, 2020, pp. 523–540.
- NEM, “Nem whitepaper,” https://whitepaper.io/document/583/nem-whitepaper, 2018.
- B. Fisch, “Poreps: Proofs of Space on useful data,” Cryptology ePrint Archive, 2018.
- S. Dziembowski, S. Faust, V. Kolmogorov, and K. Pietrzak, “Proofs of Space,” in Annual Cryptology Conference. Springer, Nov. 2015, pp. 585–605.
- E. Agbozo et al., “Applying multi-criteria decision making to prioritization of Web 3.0 development factors,” in E-business technologies conference proceedings, vol. 3, no. 1, 2023, pp. 229–232.
- N. Chaudhry and M. Yousaf, “Consensus algorithms in blockchain: comparative analysis, challenges and opportunities,” in International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2018, pp. 54–63.
- M. Ferdous, M. Chowdhury, M. Hoque, and A. Colman, “Blockchain consensus algorithms: A survey,” 2020, arXiv preprint arXiv:2001.07091.
- S. Bamakan, A. Motavali, and A. Bondarti, “A survey of blockchain consensus algorithms performance evaluation criteria,” Expert Systems with Applications, vol. 154, p. 113385, 2020.
- I. C. Education, “Artificial intelligence (AI),” https://www.ibm.com/cloud/learn/what-is-artificial-intelligence, Jun. 2020.
- H. Hua et al., “Edge computing with artificial intelligence: A machine learning perspective,” ACM Computing Surveys, vol. 55, no. 9, pp. 1–35, Apr. 2023.
- J. Bambacht and J. Pouwelse, “Web3: A decentralized societal infrastructure for identity, trust, money, and data,” 2022, arXiv preprint arXiv:2203.00398.
- G. Huang et al., “Efficient and low overhead website fingerprinting attacks and defenses based on TCP/IP traffic,” in Proceedings of the ACM Web Conference 2023, Apr. 2023, pp. 1991–1999.
- W. D. la Cadena et al., “Trafficsliver: Fighting website fingerprinting attacks with traffic splitting,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, Oct. 2020, pp. 1971–1985.
- P. Kasireddy, “The architecture of a Web 3.0 application,” https://www.preethikasireddy.com/post/the-architecture-of-a-web-3-0-application, Sep. 2021.
- H. Xu et al., “deController: a Web3 native cyberspace infrastructure perspective,” IEEE Communications Magazine, 2023.
- C. Bassi, “Sustainable blockchain: Estimating the carbon footprint of algorand’s pure Proof-of-Stake,” https://algorand.com/resources/blog/sustainable-blockchain-calculating-the-carbon-footprint, Apr. 2021.
- V. Kohli et al., “An analysis of energy consumption and carbon footprints of cryptocurrencies and possible solutions,” Digital Communications and Networks, vol. 9, no. 1, pp. 79–89, Jun 2022.
- Digiconomist, “Bitcoin energy consumption index,” https://digiconomist.net/bitcoin-energy-consumption, Nov. 2023.
- ——, “Ethereum energy consumption index,” https://digiconomist.net/ethereum-energy-consumption, Nov. 2023.
- L. Breidenbach et al., “Chainlink 2.0: Next steps in the evolution of decentralized oracle networks,” Chainlink Labs, vol. 1, pp. 1–136, 2021.
- V. Buterin, “Chain interoperability,” R3 Research Paper, vol. 9, 2016.
- R. Belchior, A. Vasconcelos, S. Guerreiro, and M. Correia, “A survey on blockchain interoperability: Past, present, and future trends,” ACM Computing Surveys, vol. 54, no. 8, pp. 1–41, 2021.
- G. Wang, Q. Wang, and S. Chen, “Exploring blockchains interoperability: A systematic survey,” ACM Computing Surveys, 2023.
- M. Borkowski, D. McDonald, C. Ritzer, and S. Schulte, “Towards atomic cross-chain token transfers: State of the art and open questions within tast,” Distributed Systems Group TU Wien (Technische Universit at Wien), Report, vol. 8, 2018.
- Multichain, “Multichain bridge,” https://docs.multichain.org/getting-started/introduction.
- Threshold, “tBTC bridge,” https://threshold.network/earn/btc/.
- Parity, “Bridging the dapp-scaling now with parity bridge,” https://www.parity.io/blog/tag/parity-bridge, Mar. 2018.
- Solana, “Wormhole,” https://solana.com/ecosystem/wormhole, Apr. 2020.
- C. Smith et al., “Scaling,” https://ethereum.org/en/developers/docs/scaling/, Apr. 2023.
- Ethereum, “Danksharding,” https://ethereum.org/en/roadmap/danksharding/, Nov. 2023.
- P. Joseph and D. Thaddeus, “The bitcoin lightning network: Scalable off-chain instant payments,” 2016.
- Raiden-Network, “What is the raiden network?” https://raiden.network/101.html.
- A. Cahill and S. Deshpande, “Layer-2 scaling solutions: A framework for comparison,” https://www.tbstat.com/wp/uploads/2022/05/20220505_Layer2ScalingSolutions_TheBlockResearch.pdf, May 2022.
- Liquid-Network, “The liquid network,” https://liquid.net/.
- A. Tripathi, “Introduction to Polygon PoS,” https://wiki.polygon.technology/docs/pos/polygon-architecture/, Jun 2023.
- J. Benet, “IPFS - content addressed, versioned, P2P file system,” 2014, arXiv preprint arXiv:1407.3561.
- P. Drakatos et al., “Triastore: A Web 3.0 blockchain datastore for massive IoT workloads,” in 2021 22nd IEEE International Conference on Mobile Data Management (MDM), 2021, pp. 187–192.
- Z. Liu et al., “Make Web 3.0 connected,” IEEE Transactions on Dependable and Secure Computing, vol. 19, pp. 2965–2981, 2022.
- A. Chopra et al., “Va3: A Web 3.0 based I2I power transaction platform,” in 2022 7th IEEE Workshop on the Electronic Grid (eGRID), 2022, pp. 1–5.
- Y. Lin et al., “A unified blockchain-semantic framework for wireless edge intelligence enabled Web 3.0,” IEEE Wireless Communications, 2023.
- D. Palanikkumar et al., “An enhanced decentralized social network based on Web3 and IPFS using blockchain,” in 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 2023, pp. 616–623.
- A. Petcu et al., “A secure and decentralized authentication mechanism based on Web 3.0 and Ethereum blockchain technology,” Applied Sciences, vol. 13, no. 4, 2023.
- A. Razzaq et al., “IoT data sharing platform in Web 3.0 using blockchain technology,” Electronics, vol. 12, no. 5, 2023.
- Y. Lin et al., “A blockchain-based semantic exchange framework for Web 3.0 toward participatory economy,” IEEE Communications Magazine, Aug. 2023.
- S. Guo et al., “Blockchain-assisted privacy-preserving data computing architecture for Web3,” IEEE Communications Magazine, vol. 61, no. 8, pp. 28–34, Aug. 2023.
- Y. Qiu et al., “Fog-assisted blockchain radio access network for Web3,” IEEE Communications Magazine, Aug. 2023.
- G. Yu et al., “Towards Web3 applications: Easing the access and transition,” 2022, arXiv preprint arXiv:2210.05903.
- Y. Lin et al., “Blockchain-aided secure semantic communication for AI-generated content in Metaverse,” IEEE Open Journal of the Computer Society, vol. 4, pp. 72–83, 2023.
- ——, “A unified framework for integrating semantic communication and AI-generated content in Metaverse,” 2023, arXiv preprint arXiv:2305.11911.
- G. Liu et al., “Semantic communications for artificial intelligence generated content (AIGC) toward effective content creation,” 2023, arXiv preprint arXiv:2308.04942.
- L. Xia et al., “Generative AI for semantic communication: Architecture, challenges, and outlook,” 2023, arXiv preprint arXiv:2308.15483.
- R. Cheng et al., “A wireless AI-generated content (AIGC) provisioning framework empowered by semantic communication,” 2023, arXiv preprint arXiv:2310.17705.
- H. Du et al., “Enabling AI-generated content (AIGC) services in wireless edge networks,” 2023, arXiv preprint arXiv:2301.03220.
- Y. Liu et al., “Blockchain-empowered lifecycle management for AI-generated content (AIGC) products in edge networks,” 2023, arXiv preprint arXiv:2303.02836.
- M. Xu et al., “Joint foundation model caching and inference of generative AI services for edge intelligence,” 2023, arXiv preprint arXiv:2305.12130.
- H. Du et al., “Generative AI-aided optimization for AI-generated content (AIGC) services in edge networks,” 2023, arXiv preprint arXiv:2303.13052.
- J. Wang et al., “A unified framework for guiding generative AI with wireless perception in resource constrained mobile edge networks,” 2023, arXiv preprint arXiv:2309.01426.
- Y. Cao et al., “A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to ChatGPT,” 2023, arXiv preprint arXiv:2303.04226.
- C. Zhang et al., “A complete survey on generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 all you need?” 2023, arXiv preprint arXiv:2303.11717.
- I. C. Education, “Natural language processing (NLP),” https://www.ibm.com/cloud/learn/natural-language-processing, Jul. 2020.
- M. Treviso et al., “Efficient methods for natural language processing: A survey,” Transactions of the Association for Computational Linguistics, vol. 11, pp. 826–860, 2023.
- C. Qin et al., “Is ChatGPT a general-purpose natural language processing task solver?” 2023, arXiv preprint arXiv:2302.06476.
- D. Khurana, A. Koli, K. Khatter, and S. Singh, “Natural language processing: State of the art, current trends and challenges,” Multimedia tools and applications, vol. 82, no. 3, pp. 3713–3744, 2023.
- N. Le et al., “Deep reinforcement learning in computer vision: A comprehensive survey,” Artificial Intelligence Review, pp. 1–87, 2022.
- Y. Bi et al., “A survey on evolutionary computation for computer vision and image analysis: Past, present, and future trends,” IEEE Transactions on Evolutionary Computation, 2022.
- A. Vaswani et al., “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
- I. Goodfellow et al., “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672–2680.
- D. Kingma et al., “Auto-encoding variational bayes,” 2013, arXiv preprint arXiv:1312.6114.
- K. Gregor et al., “Deep autoregressive networks,” in International Conference on Machine Learning, 2014, pp. 1242–1250.
- AletheaAI, “Robert Alice,” https://www.sothebys.com/en/buy/auction/2021/natively-digital-a-curated-nft-sale-2/to-the-young-artists-of-cyberspace, Jun. 2021.
- J. Pregelj, “Web3 plugin for ChatGPT,” https://github.com/jernejpregelj/web3-chatgpt-plugin, May 2023.
- D. Sathavara et al., “SuperCool-AI,” https://supercool.vercel.app/, Jun 2023.
- M. Sokoli et al., “Web3GPT,” https://w3gpt.ai/, May 2023.
- J. Savage, “ETHGPT,” https://github.com/Jsavage1325/ETHGPT, May 2023.
- C. Yang et al., “FlashGPT,” https://github.com/alt-research/flashGPT, Mar. 2023.
- TokenGPT, “Tokengpt,” https://ethglobal.com/showcase/tokengpt-ko8x6, May 2023.
- M. Zhang, “CoinGPT,” https://github.com/mccowanzhang/coingpt, Jun. 2023.
- Memosys and Greg, “Defi-companion-covalent,” https://github.com/gregfromstl/defi-companion, Aug. 2023.
- Quantstamp, “Quantstamp,” https://quantstamp.com/, 2017.
- ChainSecurity, “Chainsecurity,” https://chainsecurity.com/, 2017.
- K. Gupta et al., “Secure Semantic Snap,” https://nozk.kanavgupta.xyz/, Dec 2022.
- A. Alam et al., “MedDAO,” https://github.com/Arsalaan-Alam/hackfs, Jun. 2023.
- A. Shamir, “How to share a secret,” Communications of the ACM, vol. 22, no. 11, pp. 612–613, 1979.
- S. Das et al., “Practical asynchronous high-threshold distributed key generation and distributed polynomial sampling,” in 32nd USENIX Security Symposium (USENIX Security 23), Aug. 2023, pp. 5359–5376.
- ——, “A new paradigm for verifiable secret sharing,” 2023, cryptology ePrint Archive.
- C. Ge et al., “Revocable identity-based broadcast proxy re-encryption for data sharing in clouds,” IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 3, pp. 1214–1226, 2019.
- W. Zhang et al., “A secure revocable fine-grained access control and data sharing scheme for SCADA in IIoT systems,” IEEE Internet of Things Journal, vol. 9, no. 3, pp. 1976–1984, 2021.
- J. Zhang et al., “Revocable and privacy-preserving decentralized data sharing framework for fog-assisted Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 13, pp. 10 446–10 463, 2021.
- X. Yang, W. Li, and K. Fan, “A revocable attribute-based encryption EHR sharing scheme with multiple authorities in blockchain,” Peer-to-peer Networking and Applications, vol. 16, no. 1, pp. 107–125, 2023.
- N. Keizer et al., “The case for AI based Web3 reputation systems,” in 2021 IFIP Networking Conference (IFIP Networking), 2021, pp. 1–2.
- J. Lorenz et al., “Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity,” in Proceedings of the first ACM international conference on AI in finance, Oct. 2021, pp. 1–8.
- M. Weber et al., “Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics,” 2019, arXiv preprint arXiv:1908.02591.
- I. Alarab and S. Prakoonwit, “Graph-based LSTM for anti-money laundering: Experimenting temporal graph convolutional network with bitcoin data,” Neural Processing Letters, vol. 55, no. 1, pp. 689–707, 2023.
- W. Lo et al., “Inspection-l: self-supervised GNN node embeddings for money laundering detection in bitcoin,” Applied Intelligence, vol. 53, pp. 19 406–19 417, Aug 2023.
- M. Unzeelah and Z. Memon, “Fighting against fake news by connecting machine learning approaches with Web3,” in 2022 International Conference on Emerging Trends in Smart Technologies (ICETST), 2022, pp. 1–6.
- J. Kim et al., “A machine learning approach to anomaly detection based on traffic monitoring for secure blockchain networking,” IEEE Transactions on Network and Service Management, vol. 19, no. 3, pp. 3619–3632, 2022.
- M. Xu et al., “When quantum information technologies meet blockchain in Web 3.0,” IEEE Network, 2023.
- G. Yu et al., “Predicting NFT classification with GNN: A recommender system for Web3 assets,” in 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2023, pp. 1–5.
- R. Madhwal and J. Pouwelse, “Web3recommend: Decentralised recommendations with trust and relevance,” 2023, arXiv preprint arXiv:2307.01411.
- Z. Xiong et al., “When mobile blockchain meets edge computing,” IEEE Communications Magazine, vol. 56, no. 8, pp. 33–39, 2018.
- A. Singh and K. Chatterjee, “Securing smart healthcare system with edge computing,” Computers & Security, vol. 108, 2021.
- T. Qiu et al., “Edge computing in industrial Internet of Things: Architecture, advances and challenges,” IEEE Communications Surveys & Tutorials, vol. 22, no. 4, pp. 2462–2488, 2020.
- L. Lin et al., “Computation offloading toward edge computing,” Proceedings of the IEEE, vol. 107, no. 8, pp. 1584–1607, 2019.
- T. Silva, “Cloud computing or edge computing: Cost comparison,” https://www.azion.com/en/blog/cloud-computing-or-edge-computing-cost/, Dec. 2022.
- M. Liu et al., “Computation offloading and content caching in wireless blockchain networks with mobile edge computing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 11, pp. 11 008–11 021, 2018.
- Y. Zhu et al., “Blockchain-enabled access management system for edge computing,” Electronics, vol. 10, no. 9, 2021.
- D. Doe et al., “Promoting the sustainability of blockchain in Web 3.0 and the Metaverse through diversified incentive mechanism design,” IEEE Open Journal of the Computer Society, 2023.
- X. Wang et al., “Convergence of edge computing and deep learning: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 869–904, 2020.
- S. Deng et al., “Edge intelligence: The confluence of edge computing and artificial intelligence,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7457–7469, 2020.
- L. Cao, “Decentralized AI: Edge intelligence and smart blockchain, metaverse, Web3, and desci,” IEEE Intelligent Systems, vol. 37, no. 3, pp. 6–19, 2022.
- N. Luong et al., “Optimal auction for edge computing resource management in mobile blockchain networks: A deep learning approach,” in 2018 IEEE international conference on communications (ICC), 2018, pp. 1–6.
- R. Wang et al., “A video surveillance system based on permissioned blockchains and edge computing,” in 2019 IEEE international conference on big data and smart computing (BigComp), 2019, pp. 1–6.
- N. Kuznetsov, “Facebook’s centralized metaverse a threat to the decentralized ecosystem?” https://cointelegraph.com/news/facebook-s-centralized-metaverse-a-threat-to-the-decentralized-ecosystem, Nov. 2021.
- A. Jeffries, “Can Facebook align with the values of the metaverse?” https://www.marketingdive.com/news/can-facebook-align-with-values-metaverse/608768/, Oct. 2021.
- M. Lodge, “What is decentraland?” https://www.investopedia.com/what-is-decentraland-6827259, Nov. 2022.
- Ethereum, “Non-fungible tokens (NFT),” https://ethereum.org/en/nft/#what-are-nfts.
- Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” 2021, arXiv preprint arXiv:2105.07447.
- W. Rehman, H. Zainab, J. Imran, and N. Bawany, “NFTs: Applications and challenges,” in International Arab Conference on Information Technology (ACIT). IEEE, 2021, pp. 1–7.
- U. Chohan, “Non-fungible tokens: Blockchains, scarcity, and value,” Critical Blockchain Research Initiative (CBRI) Working Papers, 2021.
- L. Yang et al., “Generic-NFT: A generic non-fungible token architecture for flexible value transfer in Web3,” 2023, techRxiv preprint.
- E. Lopez, “Securechain,” https://github.com/eduardfina/Securechain, Jun. 2023.
- J. Zhu et al., “NFTool,” https://github.com/Bobliuuu/NFTool, Aug. 2023.
- C. Adiloglu, “StoryChain,” https://storychain.ai, Mar. 2023.
- K. Busch, “Non-fungible tokens (NFTs),” https://crsreports.congress.gov/product/pdf/R/R47189, Jul. 2022.
- Q. Wang et al., “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” 2021, arXiv preprint arXiv:2105.07447.
- R. Kräussl and A. Tugnetti, “Non-fungible tokens (NFTs): A review of pricing determinants, applications and opportunities,” Applications and Opportunities, 2022.
- D. Zetzsche, D. Arner, and R. Buckley, “Decentralized finance,” Journal of Financial Regulation, vol. 6, no. 2, pp. 172–203, 2020.
- S. Werner et al., “SoK: Decentralized finance (DeFi),” 2021, arXiv preprint arXiv:2101.08778.
- P. Winter, A. Lorimer, P. Snyder, and B. Livshits, “What’s in your wallet? privacy and security issues in Web 3.0,” 2021, arXiv preprint arXiv:2109.06836.
- E. Jiang et al., “Decentralized finance (DeFi): A survey,” 2023, arXiv preprint arXiv:2308.05282.
- Q. Kaihua et al., “CeFi vs. DeFi–comparing centralized to decentralized finance,” 2021, arXiv preprint arXiv:2106.08157.
- Ethereum, “Decentralized finance (DeFi),” https://ethereum.org/en/defi/.
- F. Schär, “Decentralized finance: On blockchain-and smart contract-based financial markets,” FRB of St. Louis Review, 2021.
- Y. Chen and C. Bellavitis, “Blockchain disruption and decentralized finance: The rise of decentralized business models,” Journal of Business Venturing Insights, vol. 13, p. e00151, 2020.
- X. Zhao et al., “FinBrain: when finance meets AI 2.0,” Frontiers of Information Technology & Electronic Engineering, vol. 20, no. 7, pp. 914–924, 2019.
- L. Cao, “AI in finance: A review,” Available at SSRN 3647625, 2020.
- N. Sadman et al., “Promise of AI in DeFi, A systematic review,” Digital, vol. 2, no. 1, pp. 88–103, 2022.
- EasyFi, “Artificial intelligence (AI) & decentralized finance (DeFi): A match made in heaven,” https://medium.com/easify-network/artificial-intelligence-ai-decentralized-finance-defi-a-match-made-in-heaven-483d24129481, Feb. 2023.
- Binance, “How AI will influence DeFi: Promises and delusions.”
- Y. Goyal et al., “AgroSurance,” https://github.com/agrosurance, Mar. 2023.
- A. Kondaurova and K. Orlov, “Prompt DeFi,” https://github.com/Digberi/promptdefi-web, Mar. 2023.
- A. Dubyk et al., “RoboFI,” https://robofi.482.solutions/, Jul. 2023.
- G. Iredale, “Pros and cons of decentralized finance (DeFi),” https://101blockchains.com/pros-and-cons-of-decentralized-finance/, Jul. 2021.
- C. Chen et al., “When digital economy meets Web 3.0: Applications and challenges,” IEEE Open Journal of the Computer Society, 2022.
- W. Ma et al., “A comprehensive study of governance issues in decentralized finance applications,” 2023, arXiv preprint arXiv:2311.01433.
- R. Qin et al., “Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2073–2082, 2022.
- S. Wang et al., “Decentralized autonomous organizations: Concept, model, and applications,” IEEE Transactions on Computational Social Systems, vol. 6, no. 5, pp. 870–878, 2019.
- R. Qin et al., “Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022.
- Ethereum, “Decentralized autonomous organizations (DAOs),” https://ethereum.org/en/dao/.
- Y. E. Faqir, J. Arroyo, and S. Hassan, “An overview of decentralized autonomous organizations on the blockchain,” in Proceedings of the 16th international symposium on open collaboration, 2020, pp. 1–8.
- X. Zhao et al., “Task management in decentralized autonomous organization,” Journal of Operations Management, vol. 68, no. 6-7, pp. 649–674, 2022.
- C. Santana and L. Albareda, “Blockchain and the emergence of decentralized autonomous organizations (DAOs): An integrative model and research agenda,” Technological Forecasting and Social Change, vol. 182, p. 121806, 2022.
- M. Haque and M. H. S. Hossain, “A comprehensive review and architecture of a decentralized automated direct government system using artificial intelligence and blockchain,” International Journal of Scientific & Engineering Research, vol. 13, 2022.
- K. Nayan, “OmniGovern DAO,” https://github.com/kamalbuilds/OmniGovern-DAO/, Aug. 2023.
- D. Duportail, “Rooster DAO,” https://github.com/RoosterDao, Jul. 2022.
- S. Kapadia, A. Aghadi, and N. Lionis, “DAOasis,” https://github.com/Suhel-Kap/DAOasis, Mar. 2023.
- W. Ding et al., “Desci based on Web3 and DAO: A comprehensive overview and reference model,” IEEE Transactions on Computational Social Systems, vol. 9, no. 5, pp. 1563–1573, 2022.
- G. Yu et al., “Leveraging architectural approaches in Web3 applications-a DAO perspective focused,” in 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 2023, pp. 1–6.
- D. Gogel, B. Kremer, A. Slavin, and K. Werbach, “Decentralized autonomous organizations: Beyond the hype,” White Paper, Jun. 2022.
- J. Tan et al., “Open problems in DAOs,” 2023, arXiv preprint arXiv:2310.19201.
- R. Rudman and R. Bruwer, “Defining Web 3.0: Opportunities and challenges,” The Electronic Library, 2016.
- D. Sheridan et al., “Web3 challenges and opportunities for the market,” 2022, arXiv preprint arXiv:2209.02446.
- Y. Fan et al., “The current opportunities and challenges of Web 3.0,” 2023, arXiv preprint arXiv:2306.03351.
- C. Barabas, N. Narula, and E. Zuckerman, “Defending Internet freedom through decentralization: Back to the future,” The Center for Civic Media & The Digital Currency Initiative MIT Media Lab, 2017.
- SolanaStatus, “An incident resulted in approximately 8,000 wallets being drained,” https://twitter.com/SolanaStatus, Aug. 2022.
- M. Egkolfopoulou and A. Gardner, “Even in the Metaverse, not all identities are created equal,” https://www.bloomberg.com/news/features/2021-12-06/cryptopunk-nft-prices-suggest-a-diversity-problem-in-the-metaverse#xj4y7vzkg, Dec. 2021.
- J. Apotheker et al., “Web3 already has a gender diversity problem,” https://www.bcg.com/publications/2023/how-to-unravel-lack-of-gender-diversity-web3, Feb. 2023.
- Reuters, “Amazon scrapped a secret AI recruitment tool that showed bias against women,” https://venturebeat.com/ai/amazon-scrapped-a-secret-ai-recruitment-tool-that-showed-bias-against-women/, Oct. 2018.
- H. Ledford, “Millions of black people affected by racial bias in health-care algorithms,” https://www.nature.com/articles/d41586-019-03228-6, Oct. 2019.
- M. Antonakakis et al., “Understanding the mirai botnet,” in 26th USENIX security symposium (USENIX Security 17), 2017, pp. 1093–1110.
- X. Jin et al., “Edge security: Challenges and issues,” 2022, arXiv preprint arXiv:2206.07164.
- Jianjun Zhu (6 papers)
- Fan Li (190 papers)
- Jinyuan Chen (25 papers)