Intelligent Routing in Directional Ad Hoc Networks Through Predictive Directional Heat Map From Spatio-Temporal Deep Learning

Abstract

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routing paths meet at the same relay node. A path may be unable to provide high throughput since some of its path nodes are helping other paths to forward the heavy traffic. Therefore, those busy nodes should be avoided when a new path is to be established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses spatio-temporal deep learning algorithm to predict the next-time snapshot of directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. Such a DHM will be used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It is able to automatically split a single path into multiple ones that later on get converged again into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than traditional routing mechanisms such as AODV and OLSR.