Data-Driven Resource Pricing and Trust Analytics in Blockchain-Enabled Vehicular Edge Markets
Main article
Abstract
Vehicular edge markets are becoming an important organizational form for intelligent transportation because vehicles, roadside gateways, and mobile edge servers may exchange computation, storage, sensing, and communication resources in real time. Existing studies emphasize secure resource sharing, blockchain consensus, and incentive-compatible auctions, but fewer studies examine how transaction data can be transformed into operational pricing rules and trust analytics for market governance. This article develops a data-driven resource pricing and trust analytics framework for blockchain-enabled vehicular edge markets. Building on the research direction of hybrid blockchain assisted vehicular resource sharing, the study repositions the problem from a pure security architecture to a business and data analytics problem: how edge-market operators price resources, evaluate provider reliability, identify opportunistic participants, and recover market efficiency under information asymmetry. A conceptual market architecture is proposed, followed by a simulated transaction dataset involving 1,400 vehicle-edge trading records across three resource categories: computation cycles, temporary storage, and sensing bandwidth. The analysis combines dynamic pricing, trust scoring, service-level compliance analysis, and scenario comparison. Results show that trust-adjusted pricing increases market-clearing efficiency by 8.7%, reduces failed resource matches by 12.4%, and improves provider selection stability compared with a price-only mechanism. The findings suggest that blockchain records should not only serve as immutable evidence but also operate as a structured data asset for pricing intelligence, risk monitoring, and platform governance in smart mobility markets.
