Machine Learning for the Blockchain
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 23285
Special Issue Editors
Interests: artificial intelligence; machine learning; natural language processing; information retrieval; evolutionary algorithms
Interests: deep learning; financial decision making; security and fraud detection; forecasting
Interests: deep learning; image and video analysis; information retrieval; knowledge manipulation; watermarking; blockchain for social impact and ride-sharing economy
Special Issue Information
Dear Colleagues,
Machine learning (ML) and distributed ledgers (DL) are two of today's most innovative technologies. The first is the evolution of statistics, artificial intelligence, and big data analysis, and the second is a decentralized database paradigm that has significantly disrupted the financial industry. Blockchain is currently the prominent but not the only DL solution and at the basis of the flourishing crypto ecosystem.
Blockchain technology, while still in its infancy, is maturing rapidly. As it is growing, so does its userbase and the abundance and variety of available applications. However, scaling problems are an issue when the amount of data passing hits a limitation due to the insufficient capacities of the blockchain.
Layer 1 blockchain solutions help to improve the base protocols by changing their way of processing data. For example, the Ethereum network is now moving to a proof-of-stake (PoS) consensus algorithm. This new method of mining supports faster transaction speeds and more efficient energy use in the mining process. Sharding is another layer 1 scaling solution that breaks down authenticating and validating transactions into smaller pieces. Layer 2 scaling solutions increase throughput without tampering with any of the original decentralized or security characteristics integral to the original blockchain. Sidechains are blockchains linked to the main chain with a two-way peg. Parachains are chains that run parallel to one another in a system of interconnected blockchains. Sidechains and parachains are created within the same framework, with the same security attributes, and connected to the central relay chain. However, they can all also act independently to address their specific applications. ML-backed Layer 1 and 2 solutions could play a crucial role in orchestrating the interoperability between different networks and chains and help to optimize the scaling process of various blockchains.
On to the applications layer, blockchain technology could do more than document transactions. Smart contracts work by following simple “if–then” statements written into code on a blockchain. A network of computers executes the actions when predetermined conditions have been met and verified. Decentralized finance (DeFi) and decentralized apps (DApps) are emerging application areas that could benefit from ML.
ML could also be applied in more traditional crypto-finance tasks: automatic trading (trading bots), transaction fee optimization, and asset price prediction, including non-fungible tokens (NFT), among others.
Finally, ML could be employed to tackle security and privacy issues explicitly related to the cryptoverse: blockchain attacks (Sybil, Race, etc.), wallet and transaction anonymization/deanonymization, or other privacy concerns.
Dr. Georgios Siolas
Dr. Georgios Alexandridis
Dr. Paraskevi Tzouveli
Guest Editors
Manuscript Submission Information
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Keywords
- Machine learning
- Distributed ledger
- Blockchain
- Scaling
- Sharding
- Sidechain
- Parachain
- Network Interoperability
- DeFi
- DApps
- Cryptofinance
- Automatic trading
- Transaction fee optimization
- Asset price prediction
- NFT
- Security
- Privacy
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Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Blockchain-Based Deep Learning to Process IoT Data Acquisition in Smart Aeroponics Systems
Authors: Rachida Ait Abdelouahid; Olivier Debauche; Abdelaziz Marzak; Frédéric Lebeau
Affiliation: Hassan II University - Casablanca, Faculty of sciences Ben M’sik, LTIM
Abstract: Aeroponics is a new technology for growing plants outside of the substrate and is particularly well suited to growing plants in zero gravity. Aeroponics is a very sensitive growing method because the roots are in the open air, which requires decisions to be made based on verified data and in a rapid manner because the plants, being out of the substrate, die in a few tens of minutes in the event of a breakdown or in the event of late detection of disease. The criticality of this mode of cultivation requires the distribution and security of sabotage control systems. IoT, AI, and blockchain are new technologies that in combination allow new secure approaches to data collection and decision-making. The Internet of Things allows us to measure the environmental data of plants and to act on the environment in which it evolves. The blockchain allows to the storage of data in a chronological and unforgeable way while artificial intelligence allows prediction, detection, and making decisions based on the data stored in the blockchain. In this paper, we develop an architecture based on the coupling of IoT, machine learning, and blockchain for the strategic control of aeroponics-based vital food production systems.