Similarity search in sensor networks using semantic-based caching

Sensor networks build temporary wireless connections in environments where the stationary infrastructures are either destroyed or too expensive to construct. Most of the previous research in sensor networks focuses on routing protocols that adapt to the dynamic network topologies, and not much work has been done on data accessing. One important data accessing application is similarity search, which provides the foundation of content-based retrieval. Many traditional similarity search algorithms are based on centralized or flooding mechanisms, which are not effective in wireless sensor network environments due to the multiple limitations such as bandwidth and power. In this paper we tackle the problem of similarity search by using semantic-based caching to reflect the data content distribution in the network. The basic idea is analyzing the cached results of earlier queries and trying to resolve the later queries within a small collection of content-related mobile nodes. Based on a Hilbert space-filling curve, the data points in a multi-dimensional semantic space are described as a linear representation. These data points are further cached to facilitate query processing. Through extensive simulations, we show that our method can perform similarity search with improved performance in terms of search cost and response time.

► Similarity search in sensor networks is a challenging task due to the dynamic network topology, limited system resources, and infrastructure-free nature.
► We tackle the problem of similarity search by using semantic-based caching to reflect the data content distribution.
► Based on a Hilbert space-filling curve, the data points in a multi-dimensional semantic space are described as a linear representation.
► These data points are further cached to facilitate query processing.