The explosive generation of Internet of Things (IoT) data calls for cloud service providers (CSP) to further provide more secure and reliable transmission, storage, and management services. This requirement, however, goes against the honest and curious nature of CSP, to the extent that existing methods introduce the third-party audit (TPA) to check data security in the cloud. TPA solves the problem of unreliable CSP but puts a heavy burden on lightweight users because of the sheer amount of the pre-audit data processing work. In this paper, we establish an audit model based on a designed binary tree assisted by edge computing, which provides computing capability for the resource-constrained terminals. The data pre-processing task is offloaded to the edge, which reduces computing load and improves the efficiency of task processing. We propose an improved correlation mechanism between data blocks and nodes on the binary tree so that all nodes on the binary tree can be fully utilized while existing methods use only leaf nodes and thus are required to establish multiple binary trees. Moreover, to improve audit efficiency, the binary tree in the audit process is designed to be self-balanced. In experiments, we compare our methods with the traditional method and experimental results show that the proposed mechanism is more effective to store and manage big data, which is conducive to providing users with more secure IoT services.

物联网（IoT）数据的爆炸式增长要求云服务提供商（CSP）进一步提供更安全，可靠的传输，存储和管理服务。但是，在现有方法引入第三方审核（TPA）来检查云中数据安全性的程度上，此要求与CSP的诚实和好奇性质背道而驰。TPA解决了CSP不可靠的问题，但由于审核前数据处理工作量巨大，给轻量级用户带来了沉重负担。本文在边缘计算的辅助下，基于设计的二叉树建立了审计模型，为资源受限的终端提供了计算能力。数据预处理任务被转移到边缘，这减少了计算负荷并提高了任务处理效率。我们提出了一种改进的数据块与二叉树上的节点之间的关联机制，从而可以充分利用二叉树上的所有节点，而现有方法仅使用叶节点，因此需要建立多个二叉树。此外，为了提高审计效率，审计过程中的二叉树被设计为自平衡的。在实验中，我们将我们的方法与传统方法进行了比较，实验结果表明，该机制更有效地存储和管理大数据，有利于为用户提供更安全的物联网服务。为了提高审计效率，审计过程中的二叉树被设计为自平衡的。在实验中，我们将我们的方法与传统方法进行了比较，实验结果表明，该机制更有效地存储和管理大数据，有利于为用户提供更安全的物联网服务。为了提高审计效率，审计过程中的二叉树被设计为自平衡的。在实验中，我们将我们的方法与传统方法进行了比较，实验结果表明，该机制更有效地存储和管理大数据，有利于为用户提供更安全的物联网服务。