Location and mobility-aware routing for multimedia streaming in disaster telemedicine

Abstract

Disaster telemedicine leverages communications networks to provide remote diagnosis of injured persons in areas affected by disasters such as earthquakes. However, telemedicine relies heavily on infrastructure, and in a disaster scenario there is no guarantee that such infrastructure will be intact. In an ad-hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad-hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad-hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage Quality of Service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Machine Learning algorithms have been deployed for predicting locations in infrastructure networks with some success, but such algorithms require modifications for us in ad-hoc networks. This paper outlines the use of an Artificial Neural Network (NN) to perform location-prediction in an ad-hoc network.