A Deep Learning Based Explainable Control System for Reconfigurable Networks of Edge Devices

Abstract

Edge devices that operate in real-world environments are subjected to unpredictable conditions caused by environmental forces such as wind and uneven surfaces. Since most edge systems such as autonomous vehicles exhibit dynamic properties, it is clear that reinforcement learning can be a powerful tool for improving system accuracy. Successful maintenance of the position of a vehicle in such environments has been recently achieved with the aid of Deep Reinforcement Learning (DRL) that dynamically adjusts the Reconfigurable Wireless Network (RWN) response. Deep Neural Networks (DNNs) are often seen as black boxes, as neither the acquired knowledge nor the decision rationale can be explained. This raises issues with the transparency and trustworthiness of the system because the underlying AI models are not governed by any mathematical or physics laws. In this paper, we explain the process of a DNN utilisation in an autonomous dynamic positioning system by gauging reactions of the DNN to predefined constraints and capturing the associated conditions that influence the DNN in a time series. We introduce a novel digitisation technique that reduces interesting patterns of time series data into single-digits to obtain a cross comparable view of the conditions. By analysing the clusters formed on this cross comparable view, we discovered multiple intensities of environmental conditions spanning across 44% of moderate conditions and 33% and 23% of harsh and mild conditions respectively. Our analysis showed that the proposed system can provide stable responses to uncertain conditions by predicting randomness.