Entropy-based knowledge spreading and application to mobility prediction

The low quality of service provided by wireless networks does not facilitate the setup of long-awaited services, such as video conversations. In a cellular network, handoffs are an important cause of packet losses and delay jitter. These problems can be mitigated if proactive measures are taken. This requires each cell to guess the next handoff of each mobile terminal, a problem known as mobility prediction. This prediction can occur thanks to some clues (such as signal strength measurements) giving information about the terminals motion. For example, a clue that locates on which road a mobile is moving is likely to be interesting for all the prediction-enabled cells along that road ---and should therefore be sent to them. This paper proposes a new method aimed at selecting the most relevant clues and finding where to propagate those clues so as to optimize mobility predictions. The pertinence of a clue is measured using information theory and by means of decision trees. This pertinence estimation is exchanged between the cells and allows to build a"relevance map" that helps determine where clues should be sent. It is adapted to the characteristics of wireless terminals such as low bandwidth and processing power.