Sultan Alamri

A connectivity index for moving objects in an indoor cellular space

With the currently available indoor positioning devices such as RFID, Bluetooth and WI-FI, the locations of moving objects constitute an important foundation for a variety of applications such as the tracking of moving objects, security and way finding. Many studies have proven that most individuals spend their lives in indoor environments. Therefore, in this paper, we propose a new index structure for moving objects in cellular space. The index is based on the connectivity (adjacency) between the indoor environment cells and can effectively respond to the spatial indoor queries and enable efficient updates of the location of a moving object in indoor space. An empirical performance study suggests that the proposed indoor-tree in terms of measurements and performance is effective, efficient and robust.

Indexing moving objects for directions and velocities queries

Moving object databases are required to support different types of queries with a large number of moving objects. New types of queries namely directions and velocity queries (DV queries), are to be supported and covered. The TPR-tree and its successors are efficient indexes that support spatio-temporal queries for moving objects. However, neither of them support the new DV queries. In this paper, we propose a new index for moving objects based on the TPR*-tree, named Direction and Velocity of TPR*-tree or DV-TPR*-tree, in order to build data a structure based on the spatial, direction and velocity domains. DV-TPR*-tree obtains an ideal distribution that supports and fulfils the new query types (DV queries). Extensive performance studies show that the query performance of DV-TPR*-tree outperforms the TPR-tree and its successors.

A taxonomy for moving object queries in spatial databases

Abstract In the past decade, many works have focused on the development of moving object database indexing and querying. Most of those works have concentrated on the common spatial queries which are used with static objects as well. However, moving objects have different features from static objects which may lead to a variety of queries. Therefore, it is important to understand the full spectrum of moving object queries, even before starting to build an index structure for such objects. The aim of this paper is to provide a complete picture of the capabilities of moving object queries. Thus motivated, in this paper we propose a taxonomy of moving object queries, comprising five perspectives: (i) Location perspective, (ii) Motion perspective, (iii) Object perspective, (vi) Temporal perspective and (v) Patterns perspective. These give an overall view of what moving object queries are about. In this work, each perspective is described and examples are given.

Spatiotemporal indexing for moving objects in an indoor cellular space

To facilitate a variety of indoor applications, positioning technologies have been developed in indoor spaces (such as WI-FI and RFID). Thus, the requirement for the tracking and monitoring of moving objects in indoor spaces has increased considerably. The indexing of moving objects in indoor spaces is of essential importance, as these are different from outdoor spaces in many respects, such as the measurements and the positioning technologies. Therefore, in this paper, we propose a new adjacency-index structure for objects moving in indoor space which includes both spatial and temporal properties. The spatial index is based on the connectivity (adjacency) between the indoor environment cells. Moreover, we propose two temporal indexes with different methods to store the temporal data, which can support and enable efficient query processing and efficient updates for objects moving in indoor space. The proposed indexes can efficiently serve different types of spatial queries, such as KNN and indoor range, and a variety of temporal queries which are essential in an indoor environment. An empirical performance study suggests that the proposed data structures are effective, efficient, and robust.

On finding safe regions for moving range queries

Abstract The cost of monitoring and keeping the location of a Moving Query updated is very high, as the calculation of the range query needs to be re-evaluated whenever the query moves. Many methods have been proposed to minimize the computation and communication costs for the continuous monitoring of Moving Range Queries. However, because this problem has been only partly solved, more radical efforts are needed. In response, we propose an efficient technique by adopting the concept of a safe region. The safe region is an area where the set of objects of interest does not change. If a moving query is roaming within the safe region then there is no need to update the query. This paper presents efficient techniques to create a competent safe region to reduce the communication costs. We use Monte-Carlo simulation to calculate the area of the safe region due to the irregularity of its shape. As long as the query remains inside its specified safe region, expensive re-computation is not required, which reduces the computational and communication costs in client–server architectures.

Indexing Moving Objects in Indoor Cellular Space

Researchers have proven that most individuals spend most of their lives indoors. With the current appropriate indoor positioning devices such as Bluetooth and RFID, WIFI, the locations of moving objects will be an important foundation for a variety of applications such as the tracking moving objects, way finding, and security. Many studies have considered the moving object in outdoor environments such as TPR-Tree and its successors. In this paper, we propose a new index tree for moving objects in cellular space. The Index will be based on the connectivity (adjacency) between the indoor environment cells. The development index can support and enable efficient query processing and efficient updates of moving objects in indoor space.

Tracking moving objects using topographical indexing

With the increasing popularity of Global Positioning System (GPS) technologies, many applications have been developed that are able to browse and monitor their GPS tracks on mobile objects. However, a large number of applications focus only on the region (not the exact coordinate location) where mobile objects are located. Not only the exact coordinate locations of moving objects are not needed but also the exact coordinate locations may sometime be distorted because of the inaccuracy of tracking systems. Therefore, in this paper, we propose an efficient data structure index for the moving objects based on their regional location. The topographical outdoor‐tree (TO‐tree) is based on the connectivity (adjacency) between outdoor cells space. The proposed index can support and enable efficient query processing and efficient updates of moving objects in outdoor space cells. The TO‐tree can serve spatial, topological, and adjacency queries. Experiments suggest that the TO‐tree performs efficiently and incurs less update cost while maintaining satisfactory performance. Copyright

Indexing of Spatiotemporal Objects in Indoor Environments

With rapid developments in indoor positioning technologies such as wireless communications, RFID and Bluetooth, the tracking of indoor moving objects has become easier. The indexing of moving objects in indoor spaces is different from outdoor spaces in many respects such as positioning technologies and measurements. Therefore, in this paper, we propose a new adjacency index structure for moving objects in indoor spaces that take into account both spatial and temporal properties. The index is based on the idea of connectivity (adjacency)between the indoor space cells. Furthermore, we use a non-leaf node time stamping method to store temporal data, which can enable and support the temporal queries in an indoor space. An empirical performance study suggests that the developed data structure is effective and robust.

Monitoring Moving Queries inside a Safe Region

With mobile moving range queries, there is a need to recalculate the relevant surrounding objects of interest whenever the query moves. Therefore, monitoring the moving query is very costly. The safe region is one method that has been proposed to minimise the communication and computation cost of continuously monitoring a moving range query. Inside the safe region the set of objects of interest to the query do not change; thus there is no need to update the query while it is inside its safe region. However, when the query leaves its safe region the mobile device has to reevaluate the query, necessitating communication with the server. Knowing when and where the mobile device will leave a safe region is widely known as a difficult problem. To solve this problem, we propose a novel method to monitor the position of the query over time using a linear function based on the direction of the query obtained by periodic monitoring of its position. Periodic monitoring ensures that the query is aware of its location all the time. This method reduces the costs associated with communications in client-server architecture. Computational results show that our method is successful in handling moving query patterns.

Dynamic safe regions for moving range queries in mobile navigation

The cost of monitoring and updating the location of Moving Queries is very high, as the calculation of a range query needs to be re-evaluated whenever the query moves. Previous efforts to reduce this cost have proposed reducing the frequency of communication between query and server. However, because all possible objects continue to be surveyed using these approaches, substantial cost reduction is not possible. This paper introduces two novel techniques: the continuous basic safe region, by calculating the closest objects to the border of the moving query, and the continuous extended safe region, by calculating the intersections among several range objects. Inside these safe regions there is no need to update the query as the set of objects of interest does not change. We compare the size of the safe regions obtained using these two methods and show their potential to greatly reduce computations and communications cost in client-server architectures.