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We utilize tools from information theory to develop adaptive algorithms for two key problems in cellular networks: location tracking and resource management. The use of information theory is motivated by the fundamental observation that overheads in many aspects of mobile computing can be traced to the randomness or uncertainty in an individual user's movement behavior. We present a model-independent information-theoretic approach for estimating and managing this uncertainty, and relate it to the entropy or information content of the user's movement process. Information-theoretic mobility management algorithms are very simple, yet reduce overhead by ∼80 percent in simulated scenarios by optimally adapting to each individual's movement. These algorithms also allow for flexible tradeoff between location update and paging costs. Simulation results demonstrate how an information-theory-motivated resource provisioning strategy can meet QoS bounds with very small wastage of resources, thus dramatically reducing the overall blocking rate.