AR Modeling for Temporal Extension of Correlated Sensor Network Data

In this paper, a model based on autoregressive (AR) method for modeling and generating data in sensor networks is proposed. For this purpose, spatial and temporal correlation of real data is exploited. In addition, estimation of correlation coefficients is used for temporal extension. Availability of a suitable data set is the fundamental need for validation of algorithms and protocols that try to minimize energy consumption in sensor networks. Moreover, so far, a few real systems have been implemented and hence researchers have many limitations in accessing appropriate data. Considering these problems, the spatial and temporal AR model is introduced. This model utilizes temporal and spatial attributes simultaneously to initiate a general method for generating data with proper dimensions and qualities from real configurations both in space and in time