一种数据特征敏感的高效数据聚集编码方案

A Data Context-aware High Efficient Data Aggregation Scheme

摘要:

物联网中数据聚集算法设计面临的主要困难是:数据聚集时数据冗余的发现和消除,数据特征的发现均需要周密的思考,数据聚集时通信效率的提升通常需要较高超的数据编码技巧,精巧的算法设计思路；因此,研究数据聚集的高效率编码算法对于降低数据聚集的能耗具有重要的意义；当前研究中对于数据聚集中编码的综合讨论尚不充分,论文首先对问题进行建模,提出数据平滑泛型的概念,并通过分析得到数据平滑泛型将显著提高通信效率提升指数,在此基础上,论文提出了一套数据特征敏感的物联网高效数据聚集编码方案,包括数据聚集前的数据特征敏感的编码方法,基于CRT的数据编码准备方法,以及多节点数据聚集的可聚集变换方法；所给出的方法均经过严格的理论分析和证明论证,表明了方案的可行性。

Abstract:

The main difficulty of data aggregation algorithm design in the Internet of things is the discovery and elimination of redundant data, and the discovery of data characteristics need to be careful. The improvement of communication efficiency in data aggregation is usually required to be more skillful than the encoding technique, and the design of sophisticated algorithm. So the high efficiency of coding algorithm for data aggregation in Internet of Things is of great importance for energy cost in data aggregation. In current researches, the integrated coding method for data aggregation has not been explored thoroughly. Firstly, the problem is modeled, and the concept of data smoothing is proposed, and the data smoothing generic will significantly improve the communication efficiency. On this basis, The paper proposes a data context-aware coding scheme for data aggregation. It includes coding method before data aggregation, CRT-based data coding preparation method, multiple data aggregation transformation that can be aggregative. The methods are justified by rigorous theoretic analysis, which proves its applicability.