Pricing and Budget Allocation for IoT Blockchain With Edge Computing
Pricing and Budget Allocation for IoT Blockchain With Edge Computing
复制标题
DOI：
10.1109/tcc.2022.3150766
复制
发表时间：
2023-04-01
影响因子：
6.5
通讯作者：
Wu, Weili
中科院分区：
文献类型：
--
作者：
Ding, Xingjian;Guo, Jianxiong;Wu, Weili
受区块链固有的安全性和隐私保护的吸引，将区块链融入物联网（IoT）近年来得到了广泛的研究。然而，挖矿过程需要较高的计算能力，这阻碍了物联网设备直接参与区块链建设。因此，引入边缘计算服务来帮助构建物联网区块链，物联网设备可以从边缘服务器购买计算资源。在本文中，我们考虑物联网设备还有其他需要边缘服务器帮助的任务的情况，例如数据分析和数据存储。他们从这些任务中获得的利润与他们从边缘服务器购买的资源量密切相关。在这种场景下，物联网设备将分配有限的预算从不同的边缘服务器购买不同的资源，从而实现利润最大化。而且，边缘服务器会设定“最好”的价格，这样他们就能获得最大的利益。因此，边缘服务器和物联网设备之间出现了定价和预算分配问题。我们将边缘服务器和物联网设备之间的交互建模为多领导者多追随者 Stackelberg 游戏，其目标是达到 Stackelberg 均衡（SE）。我们证明了SE点的存在性和唯一性，并设计了有效的算法来达到SE点。最后，我们通过大量的仿真验证了我们的模型和算法，结果表明了我们设计的正确性和有效性。
Attracted by the inherent security and privacy protection of the blockchain, incorporating blockchain into Internet of Things (IoT) has been widely studied in these years. However, the mining process requires high computational power, which prevents IoT devices from directly participating in blockchain construction. For this reason, edge computing service is introduced to help build the IoT blockchain, where IoT devices could purchase computational resources from the edge servers. In this paper, we consider the case that IoT devices also have other tasks that need the help of edge servers, such as data analysis and data storage. The profits they can get from these tasks is closely related to the amounts of resources they purchased from the edge servers. In this scenario, IoT devices will allocate their limited budgets to purchase different resources from different edge servers, such that their profits can be maximized. Moreover, edge servers will set "best" prices such that they can get the biggest benefits. Accordingly, there raise a pricing and budget allocation problem between edge servers and IoT devices. We model the interaction between edge servers and IoT devices as a multileader multi-follower Stackelberg game, whose objective is to reach the Stackelberg Equilibrium (SE). We prove the existence and uniqueness of the SE point, and design efficient algorithms to reach the SE point. In the end, we verify our model and algorithms by performing extensive simulations, and the results show the correctness and effectiveness of our designs.