Deep-Learning-Based SDN Model for Internet of Things: An Incremental Tensor Train Approach

Abstract

The Internet of Things (IoT) has emerged as a revolution for the design of smart applications like intelligent transportation systems, smart grid, healthcare 4.0, Industry 4.0, and many more. These smart applications are dependent on the faster delivery of data which can be used to extract their inherent patterns for further decision making. However, the enormous data generated by IoT devices are sufficient to choke the entire underlying network infrastructure. Most of the data attributes present little or no relevance to the prospective relationships and associations with the projected benefits foreseen. Therefore, order-based generalization mechanisms, known as tensors, can be used to represent these multidimensional data, thereby minimizing the flow table (FT) lookup time and reducing the storage occupancy. So, a novel IoT-train-deep approach for intelligent software-defined networking is designed in this article. The proposed approach works in four phases: 1) tensor representation; 2) deep Boltzmann machine-based classification; 3) subtensor-based flow matching process; and 4) incremental tensor train network for FT synchronization. The proposed model has been extensively tested, and it illustrates significant improvements with respect to delay, throughput, storage space, and accuracy.