Online GMM Clustering and Mini-Batch Gradient Descent Based Optimization for Industrial IoT 4.0

Abstract

The future fifth-generation (5G) networks are expected to support a huge number of connected devices with various and multitude services having different quality of service (QoS) requirements. Communication in Industry 4.0 is one of the flagships and special applications of the 5G due to the specificity of the industrial environment as well as the variety of its services such as safety communication, robot's communications, and machine monitoring. In this context, we propose a new resource allocation for the future Industry 4.0 based on software-defined networking and network function virtualization technologies, machine learning tools and the slicing paradigm where each slice of the network is dedicated to a category of services having similar QoS requirement level. In this article, the proposed solution ensures the allocation of the resources to the slices depending on their requirements in terms of bandwidth, delay, and reliability. Toward this goal, our solution is performed in three main steps: first, Internet of Things (IoT) devices assignment to the slices step based on online Gaussian mixture model clustering algorithm, second, inter-slices resources reservations step based on mini-batch gradient descent, and third, intra-slices resources allocations based on the max-utility algorithm. We have performed extensive simulations in a realistic industrial scenario using NS3 simulator. Numerical results show the effectiveness of our proposed solution in terms of reducing packet error rate, energy consumption, and in terms of increasing the percentage of served devices in delay comparing to the traditional approaches.