A New Strategy for Reducing Latency with Deep Learning in Fog Computing Environment

The data generated by connected objects is becoming increasingly numerous and often cyclical. Fog computing (FC) has emerged as an attractive solution to bring data closer to the edge, meet requirements, and manage the growing demand for data. However, network congestion produced by connected devices increases latency and energy consumption. In addition, managing similar processes in fog nodes is difficult. Some processes evolve rapidly into complicated, heterogeneous, and dynamic structures. A reduction of latency, bandwidth, and energy consumption represents issues that can be addressed by neural networks. Indeed, Deep learning can offer fast, reliable processing times on huge quantities of data. Therefore, integrating deep learning in a fog environment would be interesting. Therefore, we proposed a new strategy that enables the selection of the best fog node within a given zone by leveraging a deep learning-based LSTM model (BRFC-LSTM) and metrics such as data size, bandwidth, and the number of layers in the node.