By Topic

SDS: Distributed Spatial-Temporal Similarity Data Storage in Wireless Sensor Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Haiying Shen ; Dept. of Comput. Sci. & Comput. Eng., Univ. of Arkansas, Fayetteville, AR, USA ; Ting Li ; Lianyu Zhao ; Ze Li

Since centralized data storage and search schemes often lead to high overhead and latency, distributed data centric storage becomes a preferable approach in large-scale WSNs. However, most of existing methods lack optimization for spatial- temporal search and similarity search for multi-attribute data. Some methods are optimized under circumstances where nodes are equipped with locating systems (e.g., GPS) which consumes high energy. This paper proposes a distributed spatial-temporal similarity data storage scheme (SDS). It disseminates event data in such a way that the distance between WSN neighborhoods represents the similarity of data stored in them. In addition, SDS carpooling routing algorithm efficiently routes messages without the aid of a locating system. SDS provides efficient spatial- temporal and similarity data searching service. Experimental results show that SDS yields significant improvements on the efficiency of data querying compared with existing approaches.

Published in:

Computer Communications and Networks, 2009. ICCCN 2009. Proceedings of 18th Internatonal Conference on

Date of Conference:

3-6 Aug. 2009