NDLI: Dual prediction-based reporting for object tracking sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Xu, Y. Winter, J. Lee, W.-C.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA (Xu, Y.; Winter, J.; Lee, W.-C.)
Abstract	As one of the wireless sensor network killer applications, object tracking sensor networks (OTSNs) disclose many opportunities for energy-aware system design and implementations. We investigate prediction-based approaches for performing energy efficient reporting in OTSNs. We propose a dual prediction-based reporting mechanism (called DPR), in which both sensor nodes and the base station predict the future movements of the mobile objects. Transmissions of sensor readings are avoided as long as the predictions are consistent with the real object movements. DPR achieves energy efficiency by intelligently trading off multihop/long-range transmissions of sensor readings between sensor nodes and the base station with one-hop/short-range communications of object movement history among neighbor sensor nodes. We explore the impact of several system parameters and moving behavior of tracked objects on DPR performance, and also study two major components of DPR: prediction models and location models through simulations. Our experimental results show that DPR is able to achieve considerable energy savings under various conditions and outperforms existing reporting mechanisms.
Starting Page	154
Ending Page	163
File Size	349345
Page Count	10
File Format	PDF
ISBN	0769522084
DOI	10.1109/MOBIQ.2004.1331722
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Intelligent sensors Base stations Predictive models Wireless sensor networks Monitoring Energy consumption Energy efficiency History Processor scheduling Dynamic scheduling
Content Type	Text
Resource Type	Article

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