Evaluation of a criticality-based method for generating location updates

Abstract

In a mobile computing environment, the result of a location-dependent query is determined by the current location of the user who has issued the query, as well as the locations of moving objects on which the query has been issued. If the location-dependent query is submitted as a continuous query, the result of the query changes as the user and/or the queried moving objects move. The result of a locationdependent continuous query (LDCQ) can be provided to the user as a set of tuples < S, begin, end > indicating that object S satisfies the query from time begin to time end. The accuracy and timeliness of results of LDCQs have been studied in a previous work by employing an adaptive monitoring method (AMM) that manages the locations of moving objects. In this paper, we propose a categorized adaptive monitoring method (CAMM) which is based on AMM, and which allows users to specify various criticality levels for LDCQs. The aim of CAMM is to provide higher levels of accuracy for query results as the criticality of the query increases, without significantly increasing the wireless bandwidth requirements. We provide a simulation model with multiple criticality levels for LDCQs and evaluate the performance of the proposed method.