Device mobility is an issue that affects both Mobile ad hoc networks (MANETs) and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. 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. Location prediction in combination with geographic routing has been explored in previous literature. Most of these location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in noninfrastructure networks, we investigate the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of \1 m in normal circumstances.
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.
The increasing availability and decreasing cost of mobile devices equipped with WiFi radios has led to increasing demand for multimedia applications in both professional and entertainment contexts. The streaming of multimedia however requires strict adherence to QoS levels in order to guarantee suitable quality for end users. MANETs lack the centralised control, coordination and infrastructure of wireless networks as well as presenting a further element of complexity in the form of device mobility. Such constraints make achieving suitable QoS a nontrivial challenge and much work has already been presented in this area. This paper proposes a bottom-up routing protocol which specifically takes into account mobility and other unique characteristics of MANETs in order to improve QoS for multimedia streaming. Geographic Predictive Routing (GPR) uses Artificial Neural Networks to accurately predict the future locations of neighbouring devices for making location and mobility-aware routing decisions. GPR is intended as the first step towards creating a fully QoS-aware networking framework for enhancing the performance of multimedia streaming in MANETs. Simulation results comparing GPR against well-established ad-hoc routing protocols such as AODV and DSR show that GPR is able to achieve an improved level of QoS in a variety of multimedia and mobility scenarios.
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