Wireless Broadcast Environments Research Articles

SKQAI: A novel air index for spatial keyword query processing in road networks

Spatial keyword query (SKQ) processing is gaining great interest with the proliferation of location-based devices and services. However, most of the existing SKQ processing methods are either focused on Euclidean space or suffer from poor scalability. This paper addresses the problem of SKQ processing in road networks under wireless broadcast environments, and devises a novel air index called SKQAI, which combines a road network weighted quad-tree, several keyword quad-trees and a distance bound array, to facilitate SKQ processing in road networks. Based on SKQAI, efficient algorithms for processing Boolean Range, Top-k and Ranked SKQs are proposed. The proposed methods can efficiently prune irrelevant regions of the road network based on both road network distance and keyword information, and thus improve query processing efficiency significantly. Finally, simulation studies on two real road networks and two geo-textual datasets are conducted to demonstrate the effectiveness and efficiency of the proposed algorithms.

Spatial query processing for skewed access patterns in nonuniform wireless data broadcast environments

Location-based services (LBSs) via wireless data broadcast can provide a huge number of mobile clients for simultaneously accessing spatial data according to their current locations. Under non-uniform wireless data broadcast, more popular data appear more times than regular data, resulting in the decrease of the average client waiting time. In this paper, to efficiently support the processing of continuous window queries for LBSs, we propose a skewed spatial index considering clients' skewed access patterns in the non-uniform wireless broadcast environments. The index information considering the non-uniform broadcast is interleaved with the spatial objects on the wireless channel to support efficient access. From our experimental study, we have shown that our proposed method outperforms the multi-levelled air index scheme.

Quick Window Query Processing Using a Non-Uniform Cell-Based Index in Wireless Data Broadcast Environment

Quick Window Query Processing Using a Non-Uniform Cell-Based Index in Wireless Data Broadcast Environment

Spatial query processing for skewed access patterns in non-uniform wireless data broadcast environments

Spatial query processing for skewed access patterns in non-uniform wireless data broadcast environments

Spatial Air Index Based on Largest Empty Rectangles for Non-Flat Wireless Broadcast in Pervasive Computing

In pervasive computing, location-based services (LBSs) are valuable for mobile clients based on their current locations. LBSs use spatial window queries to enable useful applications for mobile clients. Based on skewed access patterns of mobile clients, non-flat wireless broadcast has been shown to efficiently disseminate spatial objects to mobile clients. In this paper, we consider a scenario in which spatial objects are broadcast to mobile clients over a wireless channel in a non-flat broadcast manner to process window queries. For such a scenario, we propose an efficient spatial air index method to handle window query access in non-flat wireless broadcast environments. The concept of largest empty rectangles is used to avoid unnecessary examination of the broadcast content, thus reducing the processing time for window queries. Simulation results show that the proposed spatial air index method outperforms the existing methods under various settings.

Keyword K-Nearest Neighbor Query of Road Network under Wireless Broadcast Environment

Keyword K-Nearest Neighbor Query of Road Network under Wireless Broadcast Environment

A partial index for distributed broadcasting in wireless mobile networks

To increase the scalability of location-based services (LBSs), researchers have begun to propose methods for processing spatial queries in wireless broadcast environments. In this paper, we examine the issue of spatial query processing within wireless broadcast systems and propose a distributed broadcast system (D-Bcast) model that can be applied to spatial query processing in such environments. Instead of receiving data directly from the server, the client in our proposed system model receives, via a subserver (Ssub), only the portion of the spatial data that has been requested by the querier, which reduces the broadcasting cycle and thus the access time. This distributed data broadcast model represents an efficient method of transmitting data to an arbitrary number of clients, including those that are moving across different main server (Smain) ranges. To implement this model, a hierarchical bitmap-based spatial index (HBI) employing a tree-based indexing scheme is proposed that divides the broadcast space into grids according to the distributions of objects. The grids are constructed such that an index tree for each grid can easily be configured. A full index of the broadcast program is constructed by applying the HBI to each Ssub, with the indexes of the Ssubs corresponding to the respective portions of the broadcast program used to broadcast data to the clients. In this manner, the total broadcasting cycle required to be read by the client is reduced and the query processing is decreased. Our proposed method is more effective than existing wireless broadcasting methods in reducing the location error that occurs when an update is induced by object movement. A cost model and experimental results are used to verify the efficacy of the proposed method.

Heuristic Algorithm for Efficient Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server’s point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. The most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Cost Data Retrieval Problem (MCDR) and Large Number Data Retrieval (LNDR) Problem. We also develop a heuristic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time.

A Caching Strategy for Spatial Queries in Mobile Networks

As a result of the recent developments in mobile terminals such as smartphones and laptop computers, as well as in wireless communication technologies such as GPS, location- based services are coming closer and closer to our daily life. However, the problems associated with the limited resources-such as limited wireless bandwidth, limited battery life, and small memory space-of mobile terminals remain unsolved. Research on cache utilization in mobile terminals, the development of efficient query processing algorithms, and improvement of the index structure in order to support efficient location-based service is geared towards alleviating these problems. In this paper, we propose location- based cache maintenance strategies for wireless broadcast environments in which a mobile client prefetches data that are expected to be used in the near future, and caches and maintains the data at a location close to the client's location. We also propose a hierarchical tree-based privacy approach for supporting anonymous location-based queries in wireless mobile data delivery systems. The results of experiments conducted using our proposed algorithms indicate that they help to reduce communication costs and support rapid spatial query processing.

Data broadcasting for dependent information using multiple channels in wireless broadcast environments

Data broadcasting is an effective approach to disseminating information to mobile clients and has attracted much research attention in recent years. In many applications, the access pattern among the data can be represented by a weighted DAG. In this paper, we consider the problem of efficiently generating the broadcast schedules on multiple channels when the data set has a DAG access pattern. We show that it is NP-hard to find an optimal broadcast schedule which not only minimizes the latency but also satisfies the ancestor property that retains the data dependency. We further derive a condition for the input DAGs under which one can generate an optimal broadcast schedule in linear time and propose an algorithm to generate the schedule. Due to the NP-completeness, we provide three heuristics for general DAGs based on the level of a vertex in the input DAGs and each heuristic uses a different policy to place vertices into the broadcast channels. There are two categories for the policies. The first category mainly considers the probability for a process to stop at a considered vertex. The second category takes the vertices which are affected most when assigning a vertex into consideration. We analyze and discuss these heuristics. A short experimental simulation is given for supporting and validating the discussion. In particular, the experimental results indicate that roughly considering the whole posterior vertices of each vertex is not precise and may not lead to good results and considering the vertices affected most when assigning a vertex will help reducing the latency.

Efficient Data Access for Location-Dependent Spatial Queries

When the mobile environment consists of light-weight devices, the loss of network connectivity and scarce resources, e.g., low battery power and limited memory, become primary issues of concern in order to efficiently support portable wireless devices. In this paper, we propose an index-based peer-to-peer data access method that uses a new Hierarchical Location-Based Sequential (HLBS) index. We then propose a novel distributed Nearest First Broadcast (NFB) algorithm. Both HLBS and NFB are specifically designed for mobile peer-to-peer service in wireless broadcast environments. The system has a lower response time, because the client only contacts a qualified service provider by accessing the HLBS and quickly retrieves the data to answer the query by using NFB. HLBS and NFB design the index for spatial objects according to the positions of individual clients and transfer the index in the order arranged so that the spatial query can be processed even after the user tunes the partial index. Hence, this design can support rapid and energy-efficient service. A performance evaluation is conducted to compare the proposed algorithms with algorithms based on R-tree and Hilbert-curve air indexes. The results show that the proposed data dissemination algorithm with the HLBS index is scalable and energy efficient in both range queries and nearest neighbor queries.

무선방송환경에서 클라이언트의 공간질의 수를 고려한 효율적인 데이터 스케줄링

무선방송환경에서 클라이언트의 공간질의 수를 고려한 효율적인 데이터 스케줄링

Data Retrieval Scheduling for Multi-Item Requests in Multi-Channel Wireless Broadcast Environments

Wireless data broadcast is a popular data dissemination method in mobile computing environments because of its capability of concurrently disseminating data to multiple users. In this paper, we study the data retrieval scheduling problem for multi-item requests in multi-channel broadcast environments. To maximize the number of downloads given a deadline, we define a problem called largest number data retrieval (LNDR). We prove the decision problem of LNDR is NP-hard, and we investigate approximation algorithm for it. We also define another problem called minimum cost data retrieval (MCDR), which aims at downloading a set of requested data items with the least response time and energy consumption. We prove MCDR is NP-hard to approximate to within any non-trivial factor. Therefore, we investigate heuristic algorithm for it. Finally we provide simulation results to demonstrate the practical efficiency of the proposed algorithms.

Efficient Processing of Spatial Range Queries on Wireless Broadcast Streams

With advances in wireless networks and hand-held computing devices equipped with location sensing capability (e.g., PDAs, laptops, and smart phones), a large number of location based services (LBSs) have been successfully deployed. In LBSs, wireless broadcast is an efficient method to support the large number of users. In wireless broadcast environment, existing research proposed to support range queries search, may tune into unnecessary indexes or data object. This paper addresses the problem of processing range queries on wireless broadcast streams. In order to support range queries efficiently, we propose a novel indexing scheme called Distributed Space-Partitioning Index (DSPI). DSPI consists of hierarchical grids that provide mobile clients with the global view as well as the local view of the broadcast data. The algorithm for processing range queries based on DSPI is also proposed. Simulation experiments demonstrate DSPI is superior to the existing index schemes.

Searching continuous nearest neighbors in road networks on the air

Recently, people have begun to deal with location-based queries (LBQs) under broadcast environments. To the best of our knowledge, most of the existing broadcast-based LBQ methods are aimed at Euclidean spaces and cannot be readily extended to road networks. This paper takes the first step toward processing Continuous Nearest Neighbor queries in road Networks under wireless Broadcast environments (CN3B). Our method leverages the key properties of Network Voronoi Diagram (NVD). We first present an efficient method to partition the NVD structure of the underlying road networks into a set of grid cells and number the grid cells obtained, based on which we further propose an NVD-based Distributed air Index (NVD-DI) to support CN3B query processing. Finally, we propose an efficient algorithm on the client side to process CN3B queries. Extensive simulation experiments have been conducted to demonstrate the efficiency of our approach. The results show that our proposed method is about 4 and 7.6 times more efficient than a less-sophisticated D-tree air index based method, in access latency and tuning time, respectively.

Optimal Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server's point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. We prove the decision version of this problem is NP-complete, and we devise an algebraic algorithm to search for the best solution. We also develop a heuristic that can employ the algebraic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time. The performances of proposed algorithms are analyzed theoretically and evaluated through simulation. The experimental results show that our algorithms can significantly reduce the access time for multi-item requests.

Location-based grid-index for spatial query processing

Researchers have studied the various index structures and query processing algorithms to support efficient spatial queries in wireless communication environments. However, most previous studies have focused on server-side methods. In a server-side method, the server executes queries from individual clients and then relays the results. In our proposed method, the server transmits an index called DGI (Distributed Grid Index), and the client examines the received index to process spatial queries. The proposed index structure and search algorithm support efficient spatial queries in a wireless broadcast environment, shortening query search times.

Broadcasting on-demand data with time constraints using multiple channels in wireless broadcast environments

Data Broadcasting is an effective approach to provide information to a large group of clients in ubiquitous environments. How to generate the data broadcast schedule to make the clients’ average waiting time as short as possible is an important issue. In particular, when the data access pattern is dynamic and data have time constraints, such as traffic and stock information, scheduling the broadcast for such data to fulfill the requests is challenging. Since the content of the broadcast is dynamic and the request deadlines should be met, such data broadcasting is referred to as on-demand data broadcasting with time constraints. Many papers have discussed this type of data broadcasting with a single broadcast channel. In this paper, we investigate how to schedule the on-demand broadcast for the data with time constraints using multiple broadcast channels and provide two heuristics to schedule the data broadcast. The objective of the proposed heuristics is to minimize the miss rate (i.e., ratio of the number of requests missing deadlines to the number of all requests) and latency (i.e., time between issuing and termination of the request). We show that the offline version of the considered problem is NP-hard and present a competitive analysis on the proposed heuristics. More discussion about the proposed heuristics is given through extensive simulation experiments. The experimental results validate that the proposed heuristics achieve the objectives.

Spatial query processing in road networks for wireless data broadcast

Recently, wireless broadcast environments have attracted significant attention due to its high scalability to broadcast information to a large number of mobile subscribers. It is especially a promising and desirable dissemination method for the heavily loaded environment where a great number of the same type of requests are sent from the users. There have been many studies on processing spatial queries via broadcast model recently. However, not much attention is paid to the spatial queries in road networks on wireless broadcast environments. In this paper, we focus on three common types of spatial queries, namely, k nearest neighbor (kNN) queries, range queries and reverse nearest neighbor (RNN) queries in spatial networks for wireless data broadcast. Specially, we propose a novel index for spatial queries in wireless broadcast environments (ISW). With the reasonable organization and the effectively pre-computed bounds, ISW provides a powerful framework for spatial queries. Furthermore, efficient algorithms are designed to cope with kNN, range and RNN queries separately based on ISW. The search space can be obviously reduced and subsequently the client can download as less as possible data for query processing, which can conserve the energy while not significantly influence the efficiency. The detailed theory analysis of cost model and the experimental results are presented for verifying the efficiency and effectiveness of ISW and our methods.

An Efficient Adaptive Bitmap-based Selective Tuning Scheme for Spatial Queries in Broadcast Environments

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B?DSI) technique.