Adaptive Distance Filter-based Traffic Reduction for Mobile Grid

The mobile grid introduces various research challenges distinguished from existing grid computing systems. They are low bandwidth, low processing power, low battery capacity, frequent disconnectivity, and mobility. Mobility of the grid node increases the system load of the mobile grid in a constrained operating environment by increasing the number of communication messages required to confirm the location between the grid broker and mobile grid node. Therefore, this paper proposes an adaptive distance filter that can effectively reduce communication traffic between the mobile grid node and grid broker. This filter constructs clusters based on the mobility and velocity of the grid node and filters the location updates. However, the reduction of location updates generates location errors, which occur when the grid broker cannot acquire the exact location of mobile nodes. To solve this problem, if the location updates are filtered, the grid broker can estimate the location of the mobile node using a statistical estimation method. For the performance evaluation of the adaptive distance filter, we modeled the mobility of the grid nodes. We then measured the reduction in location updates and location errors. In these experiments, we prove that the adaptive distance filter is an effective scheme for reducing location updates and the grid broker can reduce location errors through location estimation.