Optimizing Update Threshold for Distance-based Location Tracking Strategies in Moving Object Environments

In distance-based location update schemes with a predefined distance threshold d, an object reports its location to the location server, whenever it is located more than a distance of d away from the location expected of by the server. Adopting a small threshold can keep locations maintained in the location server close to exact object locations, but that incurs high location update costs. In this paper, we address the important issue of finding an optimal distance threshold. Our approach exploits a costfunction that takes into account location update and query processing costs, the two key performance costs, based on which an optimal threshold that minimizes the overall cost is derived. In dynamic environments, costs may vary over time, so a threshold good at one moment could become bad at another. To determine an optimal threshold adaptively, we propose two optimization algorithms, namely, conjectural algorithm and progressive algorithm. Conjectural optimization algorithm " guesses" the current system conditions, based on which it directly determines the most probable optimal value. Progressive optimization algorithm starts with a certain threshold value and adjusts it gradually towards the optimal point. To evaluate our proposed algorithms, various simulation studies are conducted and significant performance gain is observed with our algorithms.