C4.5: programs for machine learning
C4.5: programs for machine learning
Boosting a weak learning algorithm by majority
Information and Computation
Information theory
LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Mobility Prediction's Influence on QoS in Wireless Networks: A Study on a Call Admission Algorithm
WIOPT '05 Proceedings of the Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Exploiting user profiles to support differentiated services in next-generation wireless networks
IEEE Network: The Magazine of Global Internetworking
Mobility prediction based on an ant system
Computer Communications
An empirical study of bandwidth predictability in mobile computing
Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
Fast RSVP: Efficient RSVP Mobility Support for Mobile IPv6
Wireless Personal Communications: An International Journal
Hi-index | 0.00 |
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.