Optimization of wireless resources for personal communications mobility tracking
IEEE/ACM Transactions on Networking (TON)
Movement-based location update and selective paging for PCS networks
IEEE/ACM Transactions on Networking (TON)
MobiCom '97 Proceedings of the 3rd annual ACM/IEEE international conference on Mobile computing and networking
Mobile users: to update or not to update?
Wireless Networks
Minimizing the average cost of paging under delay constraints
Wireless Networks
Mobile user location update and paging under delay constraints
Wireless Networks
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
LeZi-update: an information-theoretic framework for personal mobility tracking in PCS networks
Wireless Networks - Selected Papers from Mobicom'99
An Efficient Priority-Based Dynamic Channel Allocation Strategy for Mobile Cellular Networks
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
An efficient evolutionary algorithm for channel resource managementin cellular mobile systems
IEEE Transactions on Evolutionary Computation
Mobile user tracking using a hybrid neural network
Wireless Networks
IWDC'05 Proceedings of the 7th international conference on Distributed Computing
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The mobile host's mobility profile, in a Personal Communication Network (PCN) environment, is modeled. It is argued that, for a majority of mobile hosts (MHs) for most of the time, the movement profile repeats on a day-to-day basis. The next movement strongly depends on the present location and the time of the day. Such a pattern for individual MHs is learned and modeled at the Home Location Register (HLR), and downloaded to the mobile terminal which can verify its correctness real-time. The model is not static and re-learning is initiated as the behavior of the mobile host changes. The model assumes that the past patterns will repeat in future, and a past causal relationship (i.e., next state depends on previous state) continue into the future. This facilitates the system to predict to a high degree of accuracy the location of the MH. As the model is trained up, the frequency of updates decreases as well as the probability of success in paging improves. The movement-pattern model is continuously verified locally, so that any deviation is immediately detected. The validity of the proposed model is verified through simulations.