Credit-Based Payments for Fast Computing Resource Trading in Edge-Assisted Internet of Things

The introduction of edge computing into blockchain-enabled Internet of Things (IoT) for offloading computational tasks is attracting increasing attention. Computing resource trading unavoidably happens in edgeassisted IoT. However, efficient computing resource trading cannot be achieved because of the "cold start" and "long return" problems. To address these challenges, we propose to use a credit-based payment for fast computing resource trading in edge-assisted blockchain-enabled IoT; therefore, the IoT nodes can finish fast payment and frequent trading by borrowing resource coins from other IoT nodes based on their credit values. In our resource-coin loan problem, we propose an iterative double-auction-based algorithm, where a broker is introduced to solve the loan allocation problem and to determine the size of the loan each lender would provide to each borrower. Furthermore, the broker enforces specific loan pricing rules to induce the borrowers and lenders to bid truthfully. Then, the hidden privacy information could be extracted to achieve the optimal resource-coin allocation and loan pricing. The proposed algorithm can maximize the economic benefits while protecting privacy. Simulations showed that the proposed algorithm can maximize social welfare. In addition, we compared the proposed algorithm with the credit-bank-based method in terms of the satisfaction function and payments. The experimental results demonstrated that the proposed algorithm was individually rational, truthful, and budget-balanced.