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Carbon trading is an effective way to achieve carbon neutrality. It is a market mechanism aimed at reducing global greenhouse gas emissions and carbon dioxide emissions. Blockchain technology can be applied to the carbon-trading scenario using characteristics that guarantee the security, decentralization, data immutability, and data traceability of the carbon-trading process. It would be difficult to implement carbon trading on blockchains for all enterprises and individuals, as the current performance of blockchains does not meet the requirements. There has been extensive research conducted on blockchain performance optimization, and the off-chain payment channel is one of the more mature solutions. This approach involves the transfer of transactions to off-chain transactions, thus avoiding high transaction fees. Existing research has addressed the problem of routing security and efficiency, with less emphasis on factors such as routing transaction costs, node reputation, and routing path considerations. This paper researches the optimization of payment routing in Payment Channel Networks (PCNs) and proposes the Multi-Factor Routing Payment Scheme (MFPS), which integrates factors such as the node reputation, transaction fee cost, and distance to select the route for payment transactions. In order to improve the success ratio of routing transactions, the transaction-splitting algorithm is proposed. To ensure the security and privacy of the transaction process, the Asymmetric Time-Lock Contract (ATLC) protocol is proposed. The results of extensive experimental simulations show that the MFPS proposed in this paper outperforms the ShortestPath, Cheapest, and SplitDistance algorithms. It achieves an approximately 13.8%∼49% improvement in the transaction success ratio and reduces the average transaction processing cost. The security and privacy measures can defend against wormhole and double-flower attacks and exhibit better performance in terms of computational verification and message overhead. [ABSTRACT FROM AUTHOR]

The emergence of computing power networks has improved the flexibility of resource scheduling. Considering the current trading scenario of computing power and network resources, most resources are no longer subject to change after being allocated to users until the end of the lease. However, this practice often leads to idle resources during resource usage. To optimize resource allocation, a trading mechanism is needed to encourage users to sell their idle resources. The Myerson auction mechanism precisely aims to maximize the seller's benefits. Therefore, we propose a resource allocation scheme based on the Myerson auction. In the scenario of the same user bidding distribution, we first combine the Myerson auction with Hyperledger Fabric by introducing a reserved price, which creates conditions for the application of blockchain in auction scenarios. Regarding different user bidding distributions, we propose a Myerson auction network model based on clustering algorithms, which makes the auction adaptable to more complex scenarios. The experimental findings show that the revenue generated by the auction model in both scenarios is significantly higher than that of the traditional sealed bid second-price auction, and can approach the expected revenue in the real Myerson auction scenario. [ABSTRACT FROM AUTHOR]

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm. [ABSTRACT FROM AUTHOR]

In wireless communication networks, the stock of spectrum, computing, storage and other resources at the edge was limited. The traditional decentralized and exclusive resource allocation resulted in weak resource reuse capability and low utilization rate. At the same time, it was difficult to ensure the fairness of resource sharing in the traditional resource scheduling process due to the lack of trust between the owners. First, a double auction model of computing and wireless communication resource joint management based on blockchain was proposed. In the model, a resource market was established, in which resource buyers and sellers allocated resources through double auctions. Secondly, blockchain was used to store the resource information of buyers and sellers to solve the mutual trust problem of all parties in the network. Finally, the experimental simulation was carried out to verify that the proposed model effectively improved the system performance and resource utilization efficiency. [ABSTRACT FROM AUTHOR]

To meet the execution requirements of delay-sensitive services in vehicular edge computing (VEC) networks, vehicular services need to be offloaded to edge computing nodes. For complex, large-scale services, the services need to be migrated if the services are not completed before the vehicles leave the coverage of edge computing nodes. Trust and resource matching between areas thus become major problems. This paper studies the decision model of vehicular service offloading and migration. First, software-defined network (SDN) technology is introduced into the traditional network architecture, and a two-layer distributed SDN-controlled VEC network architecture is designed, which is divided into a domain control layer and an area control layer. In this framework, we use the consortium blockchain as a carrier to share network topology information between SDN controllers to prevent information leakage. We then established a service offloading and migration optimization problem model to minimize service execution delay, reduce energy consumption and maximize the throughput of the blockchain system. We describe the problem model as a Markov Decision Process (MDP), introduce a deep reinforcement learning (DRL) algorithm named asynchronous advantage actor-critic (A3C) and design a dynamic service offloading and migration algorithm (DSOMA) based on A3C to solve the problem. Simulation results show that DSOMA can increase the throughput of the blockchain system, and DSOMA is superior to the deep Q-learning (DQN) algorithm and greedy offloading algorithm in reducing service execution delay and system energy consumption. [ABSTRACT FROM AUTHOR]