Efficient Storage of Heterogeneous IoT data in a Blockchain using an Indexing Method in Metric Space

Abstract

In this work, we proposed a IoT data indexing method to surpass some challenges encountered during the use of hashing in the storage of data in a blockchain. The indexing method was developed in metric space in which no dimensions are considered and only distance between objects is taken into account. The proposed method consisted on putting the index in the inner of a block. The index, called GHB-tree is based on space partitioning using hyperplane. The proposed approach was tested using two datasets of close size and different dimensions. The experimental results showed that the proposed method is efficient and competitive to other storing methods since the queries retrieve time is very reduced to be expressed by millisecond compared with that of other blockchains.