Deep Neural Network Supervised Bandwidth Allocation Decisions for Low-Latency Heterogeneous E-Health Networks

This paper proposes the first use of a deep neural network (DNN) to characterize the dependence of end-to-end latency on key bandwidth allocation decision parameters and diverse network features over heterogeneous body area network (BAN), wireless local area network (WLAN), and passive optical access network (PON) toward ubiquitous e-health. Using the association between latency and bandwidth allocation decisions, the DNN supervises bandwidth values that significantly reduce end-to-end latency. In this paper, we present the proposed DNN architecture and the selection of input features and output targets based on latency tradeoff analysis. We show that with supervised training, the DNN can accurately predict end-to-end latency corresponding to varying bandwidth allocation decisions, thereby critically analyzing the impact that multiple bandwidth decision parameters and network features have on latency. Using the trained DNN, bandwidth allocation decisions, i.e., bandwidth values that reduce the latency in WLAN and PON can be obtained. Our simulation results show that by using such bandwidth values supervised by the DNN in allocating bandwidth in WLAN and PON, the latency in each network segment over a heterogeneous BAN + WLAN + PON can be effectively reduced, regardless of variations in network features during network operation. Overall, an end-to-end latency <200 mu s with a significant 40%-70% improvement is achieved as compared to conventional bandwidth allocation algorithms.