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C. Guestrin, P. Bodik, R. Thibaux, M. Paskin and S. Madden, “Distributed Regression: An Efficient Framework for Modeling Sensor Network Data,” ACM/IEEE IPSN, 2004, pp. 1-10.
has been cited by the following article:
TITLE: Approximate Continuous Aggregation via Time Window Based Compression and Sampling in WSNs
AUTHORS: Lei Yu, Jianzhong Li, Siyao Cheng
KEYWORDS: Approximate Aggregation, Continuous Aggregation, Sampling, Sensor Network
JOURNAL NAME: Wireless Sensor Network, Vol.2 No.9, September 30, 2010
ABSTRACT: In many applications continuous aggregation of sensed data is usually required. The existing aggregation schemes usually compute every aggregation result in a continuous aggregation either by a complete aggregation procedure or by partial data update at each epoch. To further reduce the energy cost, we propose a sampling-based approach with time window based linear regression for approximate continuous aggregation. We analyze the approximation error of the aggregation results and discuss the determinations of parameters in our approach. Simulation results verify the effectiveness of our approach.
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