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Regression is one of the effective techniques for data analysis in a WSN. Besides distributed data, the limited power supply and bandwidth capacity of nodes makes doing regression difficult in WSNs. Conventional methods, which employ some numerical optimization techniques such as Nelder-Mead simplex and gradient descent, generally work in a pre-established Hamiltonian path among the nodes. Low estimation...
This paper presents an energy-efficient routing scheme for the in-network implementation of data regression modelling in wireless sensor networks (WSNs). With the kernel based distributed representation, a clustering based routing architecture is proposed to implement the distributed Gaussian elimination for solving the regression algebraic equation. In particular, the skeleton routing tree is built...
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