Optimal Stochastic Location Updates in Mobile Ad Hoc Networks

loca tion information, where the optimality is in the sense of minimizing the overall costs. In this paper, we develop a stochastic sequ ential decision framework to analyze this problem. Under a Markovian mobility model, the location update decision problem is modeled as a Markov Decision Process (MDP). We first investigate the monotonicity properties of optimal NU and LSU operations with respect to location inaccuracies under a general cost setting. Then, given a separable cost structure, we show that the location update decision s of NU and LSU can be independently carried out without loss of optimality, i.e., a separation property. From the discovered sepa ration property of the problem structure and the monotonicity properties of optimal actions, we find that 1) there always exists a simple optimal threshold-based update rule for LSU operations; 2) for NU operations, an optimal threshold-based update rule exists in a low-mobility scenario. In the case that no a priori knowledge of the MDP model is available, we also introduce a practical model-free lear ning approach to find a near-optimal solution for the problem.