Blockchain economical models, delegated proof of economic value and Delegated Adaptive Byzantine fault tolerance and their implementation in Artificial Intelligence Blockcloud

Artikel

The Artificial Intelligence BlockCloud (AIBC) is an artificial intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer (the latter three are the collective "upper-layers". The AIBC layers have distinguished responsibilities and thus performance and robustness requirements. The upper layers need to follow a set of economic policies strictly and run on a deterministic and robust protocol. While the fundamental layer needs to follow a protocol with high throughput without sacrificing robustness. As such, the AIBC implements a two-consensus scheme to enforce economic policies and achieve performance and robustness: Delegated Proof of Economic Value (DPoEV) incentive consensus on the upper layers, and Delegated Adaptive Byzantine Fault Tolerance (DABFT) distributed consensus on the fundamental layer. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to enforce the DPoEV, as well as to achieve the best balance of performance, robustness, and security. The DPoEV-DABFT dual-consensus architecture, by design, makes the AIBC attack-proof against risks such as double-spending, short-range and 51% attacks; it has a built-in dynamic sharding feature that allows scalability and eliminates the single-shard takeover. Our contribution is four-fold: that we develop a set of innovative economic models governing the monetary, trading and supply-demand policies in the AIBC; that we establish an upper-layer DPoEV incentive consensus algorithm that implements the economic policies; that we provide a fundamental layer DABFT distributed consensus algorithm that executes the DPoEV with adaptability; and that we prove the economic models can be effectively enforced by AIBC's DPoEV-DABFT dual-consensus architecture.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 12 ; Year: 2019 ; Issue: 4 ; Pages: 1-27 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Artificial Intelligence ; blockchain ; BlockCloud ; consensus algorithms

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Deng, Qi
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2019

Weitere Informationen

• DOI: doi:10.3390/jrfm12040177
• Handle: http://hdl.handle.net/10419/239065
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ