Internet of Things (IoT) has been widely applied in various domains, e.g. environmental monitoring, intelligent transport system, video surveillance, etc. In most of the IoT applications, the IoT data is generated from a number of data sources, not just only one source. In addition, IoT data has various types with different processing requirements. The high-priority IoT data should have better storage and processing manners than the low-priority IoT data. The objective of this paper is to propose an efficient cloud storage approach for considering the multi-aspect requirements of IoT data. In the approach, a light-weight data structure is used to depict the distribution and calculate the size of each IoT subset (type) in all data sources. Then, we form a number of storage-locality groups from cloud storage blocks. However, the storage-locality groups have different storage sizes and locality capabilities. We would like to place the high-priority IoT subset in the storage-locality group with a strong locality capability. Therefore, there is the placement-combinational problem between IoT subsets and the storage-locality groups. To efficiently solve the IoT placement problem, we propose a bottom-up tree based approach associated with the solution of the well-known combinatorial problem: knapsack. Considering the knapsack problem with the NP-hard computational complexity, we also propose a heuristic placement approach.

物联网（IoT）已广泛应用于各个领域，例如环境监控，智能运输系统，视频监控等。在大多数IoT应用程序中，IoT数据是由许多数据源生成的，而不仅仅是一个数据源。来源。此外，物联网数据具有各种类型，具有不同的处理要求。高优先级的物联网数据应比低优先级的物联网数据具有更好的存储和处理方式。本文的目的是提出一种有效的云存储方法，以考虑物联网数据的多方面需求。在该方法中，轻量级数据结构用于描述所有数据源中每个物联网子集（类型）的分布并计算其大小。然后，我们从云存储块中形成许多存储位置组。然而，存储位置组具有不同的存储大小和位置功能。我们希望将具有高定位能力的高优先级IoT子集放置在存储本地组中。因此，物联网子集和存储位置组之间存在放置组合问题。为了有效地解决物联网放置问题，我们提出了一种基于自下而上的树的方法，该方法与解决众所周知的组合问题：背包问题相关联。考虑到具有NP难计算复杂性的背包问题，我们还提出了一种启发式放置方法。IoT子集和存储位置组之间存在放置组合问题。为了有效地解决物联网放置问题，我们提出了一种基于自下而上的树的方法，该方法与解决已知的组合问题（背包）相关。考虑到具有NP难计算复杂性的背包问题，我们还提出了一种启发式放置方法。IoT子集和存储位置组之间存在放置组合问题。为了有效地解决物联网放置问题，我们提出了一种基于自下而上的树的方法，该方法与解决已知的组合问题（背包）相关。考虑到具有NP难计算复杂性的背包问题，我们还提出了一种启发式放置方法。