Location Dependent Information Services Research Articles

Nearest-neighbour query processing with non-spatial predicates for service allocation in smart space environment

The extensive capability of sensors let sensors autonomously collect various information on smart objects and store them to the spatial database through the wireless sensor networks. Based on spatial database, location-dependent information services (LDISs) can supply the resources according to the user locations. In LDISs, nearest-neighbour queries which return the closest object around the query location is recognised as the key component for searching the easily accessible services in smart spaces. However, existing works only consider the Euclidean distance. Thus, they have limitations to provide user-centric services that require the consideration for not only the distance but also the status of smart objects. Motivated by the issues of nearest-neighbour queries, this study proposes the new type of query called specified nearest-neighbour (SNN). SNN query considers the status and the locations of smart objects. For the SNN, the authors suggest a novel signature-based R-tree (SR-tree) index structure that handles non-spatial information of objects efficiently. Further, the authors propose an SNN query processing technique. Finally, they evaluate the performance of the proposed algorithm in various circumstances. Performance results indicate that SNN algorithm with SR-tree outperforms the existing works in terms of computational cost and disk input/output (I/O).

Movement prediction based cooperative caching for location dependent information service in mobile ad hoc networks

Location Dependent Information Services (LDISs), through which mobile clients can access location sensitive data such as weather information, traffic reports, and local news, are gaining increasing popularity in recent years. Due to limited client power and intermittent connectivity, caching is an important approach to improve the performance of LDISs. In this paper, we propose a cache replacement policy called Location Dependent Cooperative Caching (LDCC). Unlike existing location dependent cache replacement policies, the LDCC strategy applies a prediction model to approximate client movement behavior and a probabilistic transition model to analyze the communication cost. These models are used in the design of a cache replacement policy to improve system overall performance. Simulation results demonstrate that the proposed strategy significantly outperforms existing caching policies in providing LDIS in mobile ad hoc networks.

Prioritised Predicted Region based Cache Replacement Policy for location dependent data in mobile environment

Location Dependent Information Services (LDISs) provide information to mobile users depending on their current location. Data has to be transferred from the server to the mobile device, whenever required, due to mobile client's memory constraint. To improve system performance in mobile environment, frequently accessed data must be cached on the mobile client. In this paper, we propose a new cache replacement policy, the Prioritised Predicted Region based Cache Replacement Policy (PPRRP), which uses a cost function for data eviction based on the client's movement pattern. Earlier policies considered only the data distance. Simulation results show significant improvement in system performance with our policy.

Proactive location-based context aware services using agents

In recent years, there is a growing interest for location-dependent information services among mobile users. Mobile phones are used by most of the users not only to communicate, but also to access the information they need from every place and at anytime. In this article, we introduce an agent-based approach to provide location-dependent and context aware services proactively, which are always relevant to the user's context to provide a complete satisfaction of Location-Based Services. The services are based on the location of the user, the activity that he is involved in at a particular instance (based on user's daily planner), his profile (interest) and the time at which the data is retrieved. This proposed approach also identifies the entities that are more feasible for providing profile data, and a decentralised solution using mobile agents that act intelligently to retrieve all related information for the user's planned activity. Moreover, an efficient caching mechanism called 'History and Priority-based approach' that provides the user with context-oriented data periodically and a cache replacement policy called 'Profile based, Target-Driven Cache Replacement Policy' which filters unwanted data from the cache that is not useful for the user has been proposed in this article.

An Efficient Data Dissemination Scheme for Spatial Query Processing

Due to the personal portable devices and advances in wireless communication technologies, Location Dependent Information Services (LDISs) have received a lot of attention from both the industrial and academic communities. In LDISs, it is important to reduce the query response time, since a late query response may contain out-of-date information. In this paper, we study the issue of LDISs using a Voronoi Diagram. We introduce a new NN search method, called the Exponential Sequence Scheme (ESS), to support NN query processing in periodic broadcast environment. This paper aims to provide research directions towards minimizing both the access latency and energy consumption for the NN-query processing.

Client-side caching for nearest neighbor queries

The Voronoi diagram (VD) is the most suitable mechanism to find the nearest neighbor (NN) for mobile clients. In NN query processing, it is important to reduce the query response time, since a late query response may contain out-of-date information. In this paper, we study the issue of location dependent information services (LDISs) using a VD. To begin our study, we first introduce a broadcast-based spatial query processing methods designed to support NN query processing. In further sections, we introduce a generic method for location-dependent sequential prefetching and caching. The performance of this scheme is studied in different simulated environments. The core contribution of this research, resides in our analytical proof and experimental results.

Query Processing Strategies for Location-Dependent Information Systems

The demand of mobile Location Dependent Information Services (LDIS) has fueled in recent years. However, the request of a correct answer in a mobile environment needs to have more attention. This is due to the scope of query based on a location of users. In this article, we propose a new approach to generate a query result for Location-Dependent Information Services. It argues that choosing a square as a scope brings a bigger chance to get rare targets. In addition, avoiding resubmitting the same queries when the query results missed reduces bandwidth at server and client sides. Some different situation examples and evaluations show the efficiency of our proposed algorithm. The aim of our approach is to reduce a continuous query submission, speed up the processing time, and reduce the server load while a server is processing a user query.

Performance analysis of location-dependent cache invalidation schemes for mobile environments

Mobile location-dependent information services are gaining increasing interest in both academic and industrial communities. In these services, data values depend on their locations. Caching frequently accessed data on mobile clients can help save wireless bandwidth and improve system performance. However, since client location changes constantly, location-dependent data may become obsolete not only due to updates performed on data items but also because of client movements across the network. To the best of the authors' knowledge, previous work on cache invalidation issues focused on data updates only. This paper considers data inconsistency caused by client movements and proposes three location-dependent cache invalidation schemes. The performance for the proposed schemes is investigated by both analytical study and simulation experiments in a scenario where temporal- and location-dependent updates coexist. Both analytical and experimental results show that, in most cases, the proposed methods substantially outperform the NSI scheme, which drops the entire cache contents when hand-off is performed.

Cache invalidation and replacement strategies for location-dependent data in mobile environments

Mobile location-dependent information services (LDISs) have become increasingly popular in recent years. However, data caching strategies for LDISs have thus far received little attention. In this paper, we study the issues of cache invalidation and cache replacement for location-dependent data under a geometric location model. We introduce a new performance criterion, called caching efficiency, and propose a generic method for location-dependent cache invalidation strategies. In addition, two cache replacement policies, PA and PAID, are proposed. Unlike the conventional replacement policies, PA and PAID take into consideration the valid scope area of a data value. We conduct a series of simulation experiments to study the performance of the proposed caching schemes. The experimental results show that the proposed location-dependent invalidation scheme is very effective and the PA and PAID policies significantly outperform the conventional replacement policies.

Data management in location-dependent information services

Location-dependent information services have great promise for mobile and pervasive computing environments. They can provide local and nonlocal news, weather, and traffic reports as well as directory services. Before they can be implemented on a large scale, however, several research issues must be addressed.