With the continuous innovation of network technology, emerging network service models have begun to be proposed, which also puts forward higher requirements for business settlement. Business settlement, as the foundation of network services, involves the interests of users and service providers. The traditional centralized settlement schemes no longer respond to the needs of both parties in terms of transparency, security, and fairness. Therefore, some blockchain-based settlement solutions have been proposed to address these issues. However, due to the additional overhead brought by blockchain, it is difficult for these solutions to ensure both security and efficiency. In this paper, we propose a state channels-based business settlement scheme (SCBS) to complete network service settlement securely and efficiently. In SCBS, the settlement process can be effectively carried out off-chain. When non-cooperative behavior occurs, both parties can create, resolve, and refute the dispute through the state channel to assure safety. Finally, the test results from Hyperledger Fabric platform demonstrate the feasibility and effectiveness of our SCBS.

As the Internet of Things gathers pace and popularity, more and more data is collected at the edge. To unleash the value of data and make it tradable, data markets have been proposed. However, existing data markets generally depend on broker or blockchain, which inevitably raises concerns about one or more aspects of fairness, security, or efficiency. In addition, to promote data trading in the data market, a data trading incentive mechanism is also essential. In this paper, we propose a novel data trading scheme based on state channels and Stackelberg game. First, we propose aStateChannels-basedDataTrading (SCDT) framework to support non-repudiable and efficient data trading. The framework can arbitrate disputes arising from off-chain data trading through state channels, enabling traders to conduct efficient transactions off-chain without worrying about security issues. Second, we propose an optimal incentive mechanism to solve the pricing and purchasing problems. The tripartite interactions among the data seller, resource seller, and user service platform are formulated as a Stackelberg game to maximize the profits of all participants. Finally, we implement the data trading framework and analyze the incentive mechanism, which reveals the feasibility of the framework and the rationality of the incentive mechanism.

With the development of edge computing technology, the resource-limited Internet of Things (IoT) devices can offload computation-intensive artificial intelligence tasks, such as model training and inference to edge servers through resource trading. However, due to the increase in the number of intelligent applications and the rise of peer-to-peer (P2P) resource trading, the existing resource trading schemes based on the blockchain can no longer meet the needs of efficiency and security at the same time. In this article, a new state channels-based resource trading scheme is proposed for IoT, which can improve scalability without sacrificing security and fairness. In our scheme, most of the trading process could be completed off-chain, and the blockchain is used as an adjudicator to determine malicious behavior according to the users’ actions. Moreover, a method without being reliant on support from third parties is presented to defend against execution forks that must be faced when using the state channels. Finally, the feasibility and efficiency of our scheme are experimentally verified in the realistic testbed.