Mobility Prediction in MANETs

As mobile devices, such as laptops, PDAs or mobile phones, are getting more and more ubiquitous and are able to communicate with one another using technologies like wireless LAN (WLAN), the paradigm of wireless mobile ad hoc networks (MANETs) is gaining popularity. MANETs impose new challenges to the design of applications and network protocols because of their self-organizing, mobile and error-prone nature. Mobility prediction is a tool to deal with the problems emerging from the nodes’ mobility by predicting future changes in the network topology. This is crucial for different tasks such as routing and distributed server selection. This thesis presents an approach to mobility prediction based on pattern matching. Each node monitors the Signal to Noise Ratio (SNR) of its links to obtain a time series of past measurements. When a prediction is requested, the node tries to detect situations similar to the current one in the history of its links by computing the normalized cross-correlation function of the recent past with the collected training data. The found matches are then used as a base of the prediction. As an application of the SNR prediction, an extension to the formerly developed Priority Based Selection (PBS) algorithm was defined. PBS is used for distributed server selection in mobile ad hoc networks by computing and maintaining a Dominating Set of the network graph. The extension introduces a link stability criterion, which requires that a Client accepts only a node as Server to which it has a link predicted to be stable for a certain time. In order to evaluate the developed prediction algorithm, it has been implemented in the network simulator ns-2. Simulations have shown that the predictions are highly accurate. Furthermore, the application to server selection has been proved successfully, as the stability of the computed Dominating Sets increased significantly using the link stability criterion.