  One of the major task of sensor nodes in wireless sensor networks is to
transmit a subset of sensor readings to the sink node estimating a desired data
accuracy. Therefore in this paper, we propose an accuracy model using Steepest
Decent method called Adaptive Data Accuracy (ADA) model which doesn't require
any a priori information of input signal statistics to select an optimal set of
sensor nodes in the network. Moreover we develop another model using LMS filter
called Spatio-Temporal Data Prediction (STDP) model which captures the spatial
and temporal correlation of sensing data to reduce the communication overhead
under data reduction strategies. Finally using STDP model, we illustrate a
mechanism to trace the malicious nodes in the network under extreme physical
environment. Computer simulations illustrate the performance of ADA and STDP
models respectively.
