An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

Abstract

Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard communication protocols. The prevalence of M2M is bound to generate a massive volume of heterogeneous, multisource, dynamic, and sparse data, which leads a system towards major computational challenges, such as, analysis, aggregation, and storage. Moreover, a critical problem arises to extract the useful information in an efficient manner from the massive volume of data. Hence, to govern an adequate quality of the analysis, diverse and capacious data needs to be aggregated and fused. Therefore, it is imperative to enhance the computational efficiency for fusing and analyzing the massive volume of data. Therefore, to address these issues, this article proposes an efficient, multidimensional, big data analytical architecture based on the fusion model. The basic concept implicates the division of magnitudes (attributes), i.e., big datasets with complex magnitudes can be altered into smaller data subsets using five levels of the fusion model that can be easily processed by the Hadoop Processing Server, resulting in formalizing the problem of feature extraction applications using earth observatory system, social networking, or networking applications. Moreover, a four-layered network architecture is also proposed that fulfills the basic requirements of the analytical architecture. The feasibility and efficiency of the proposed algorithms used in the fusion model are implemented on Hadoop single-node setup on UBUNTU 14.04 LTS core i5 machine with 3.2GHz processor and 4GB memory. The results show that the proposed system architecture efficiently extracts various features (such as land and sea) from the massive volume of satellite data.