A Tensor-based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems

Abstract

Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may generate issues such as-congestion, and available bandwidth on the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for the problem of dimensionality reduction in big data generated from various smart devices. In the proposed scheme, firstly the Frobenius norm is applied on high-order tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks (SDN) for minimization of the load and effective bandwidth utilization on the network infrastructure. The proposed scheme minimizes the transmission delay occurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces (power consumption of appliances in different smart homes) using ’R’ programming and Matlab. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such as- network delay, accuracy, and throughput.