Adaptive schemes for location update generation in execution location-dependent continuous queries

Cataloged from PDF version of article.An important feature that is expected to be owned by today's mobile computing systems is the ability of processing location-dependent continuous queries on moving objects. The result of a location-dependent query depends on the current location of the mobile client which has generated the query as well as the locations of the moving objects on which the query has been issued. When a location-dependent query is specified to be continuous, the result of the query can continuously change. In order to provide accurate and timely query results to a client, the location of the client as well as the locations of moving objects in the system has to be closely monitored. Most of the location generation methods proposed in the literature aim to optimize utilization of the limited wireless bandwidth. The issues of correctness and timeliness of query results reported to clients have been largely ignored. In this paper, we propose an adaptive monitoring method (AMM) and a deadline-driven method (DDM) for managing the locations of moving objects. The aim of our methods is to generate location updates with the consideration of maintaining the correctness of query evaluation results without increasing location update workload. Extensive simulation experiments have been conducted to investigate the performance of the proposed methods as compared to a well-known location update generation method, the plain dead-reckoning (pdr). © 2005 Elsevier Inc. All rights reserved

Indexing continuously changing data with mean-variance tree

Traditional spatial indexes like R-tree usually assume the database is not updated frequently. In applications like location-based services and sensor networks, this assumption is no longer true since data updates can be numerous and frequent. As a result these indexes can suffer from a high update overhead, leading to poor performance. In this paper we propose a novel index structure, the Mean Variance Tree (MVTree), which is built based on the mean and variance of the data instead of the actual data values that can change continuously. Since the mean and variance are relatively stable features compared to the actual values, the MVTree significantly reduces the index update cost. The mean and the variance of the data item can be dynamically adjusted to match the observed fluctuation of the data. Our experiments show that the MVTree substantially improves index update performance while maintaining satisfactory query performance. Copyright © 2008, Inderscience Publishers.link_to_subscribed_fulltex

ABSTRACT Indexing Continuously Changing Data with Mean-Variance Tree ∗

Constantly evolving data arise in various mobile applications such as location-based services and sensor networks. The problem of indexing the data for efficient query processing is of increasing importance. Due to the constant changing nature of the data, traditional indexes suffer from a high update overhead which leads to poor performance. In this paper, we propose a novel index structure, the MVTree, which is built based on the mean and variance of the data instead of the actual data values that are in constant flux. Since the mean and variance are relatively stable features compared to the actual values, the MVTree significantly reduces the index update cost. The distribution interval and probability distribution function of the data are not required to be known a priori. The mean and variance for each data item can be dynamically adjusted to match the observed fluctuation of the data. Experiments show that compared to traditional index schemes, the MVTree substantially improves index update performance while maintaining satisfactory query performance

Context Data Management for Large Scale Context-Aware Ubiquitous Systems

Ph.DDOCTOR OF PHILOSOPH