Location-Aware Caching for Semantic-Based Image Queries in Mobile AD HOC Networks

Location-Aware Caching for Semantic-Based Image Queries in Mobile AD HOC Networks

Bo Yang (Bowie State University, USA) and Manohar Mareboyana (Bowie State University, USA)
DOI: 10.4018/jmdem.2012010102
OnDemand PDF Download:
No Current Special Offers


Mobile image information retrieval, i.e., the processing of mobile image queries, has attracted research attention due to the recent technological advances in mobile and ubiquitous computing, network infrastructures, and multimedia streaming. The previous research focuses on data delivery, while few works have reported on content-based mobile information retrieval. Therefore, it is important to devise effective means to describe the semantics as well as content distribution of mobile data. Caching is an attractive solution that helps reveal semantic relationships among mobile data sources. However, traditional caching techniques rely on exact match of fixed values and are not efficient in dealing with imprecisely described image data. To address these issues, the authors propose a location-aware caching model which reflects the distribution of images based on the analysis of earlier queries. Through extensive simulations, the authors show that the proposed model can perform search with less cost for voluminous data.
Article Preview

1. Introduction

A mobile ad hoc network is a collection of mobile nodes communicating with each other without the intervention of accessing points. These mobile nodes are capable of not only storing and processing data, but also performing complex operations through their communications, such as location lookup (Zhang, 2003; Tao, 2005) or multimedia data streaming (Aggelou, 2004). Within the scope of ad hoc networks, most of the previous research focuses on the routing protocols that adapt to the dynamic network topology (Sucec, 2002; Borcea, 2003; Guven, 2004); however, information retrieval is becoming an important issue in a variety of recent applications (Hu, 2005). Consequently, there is a great need for novel methods that facilitate efficient accessing of voluminous data in ad hoc networks.

One important data accessing application for ad hoc networks is content-based image retrieval (CBIR). To get continuous information about proximity area, a client may issue queries repeatedly. Traditional distributed systems rely on data servers to broadcast the location information of image data objects, which not only drastically increases the workload of servers, but also incurs long query response time. Although some improvements on image indexing (Nam, 2004) were proposed, the performance is highly dependent on the system resources such as bandwidth and energy.

When a content-based query (e.g., nearest-neighbor image query) is submitted to the network, the data source nodes are unknown at the requesting node, and flooding strategy is employed in the traditional mobile data accessing algorithms for ad hoc networks (Lee, 2005). The flooding approach drastically consumes system resources — storage, bandwidth, and energy.

Caching is an important mechanism that records the data retrieval history in a network. However, earlier caching schemes only support equi-select queries on image data objects because the descriptions of query results are not cached. To improve the utilization of cached data, semantic caching approach (Ren, 2000) was proposed to organize the cached data at the query-level granularity. However, the semantic descriptions are defined based on earlier queries instead of data contents, ignoring the image or semantic locality between the results of different queries. Moreover, the semantic caching methodology does not integrate the validity and popularity of data objects in the semantic descriptions, which makes it inefficient in providing QoS-related services.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing