atwa, W., Bahgat, A., refaie, M. (2020). simple missing data estimation algorithm in wsn based on spatial and temporal correlation. IJCI. International Journal of Computers and Information, 7(1), 42-54. doi: 10.21608/ijci.2020.26944.1016
walid atwa; Ashraf Bahgat; mariem refaie. "simple missing data estimation algorithm in wsn based on spatial and temporal correlation". IJCI. International Journal of Computers and Information, 7, 1, 2020, 42-54. doi: 10.21608/ijci.2020.26944.1016
atwa, W., Bahgat, A., refaie, M. (2020). 'simple missing data estimation algorithm in wsn based on spatial and temporal correlation', IJCI. International Journal of Computers and Information, 7(1), pp. 42-54. doi: 10.21608/ijci.2020.26944.1016
atwa, W., Bahgat, A., refaie, M. simple missing data estimation algorithm in wsn based on spatial and temporal correlation. IJCI. International Journal of Computers and Information, 2020; 7(1): 42-54. doi: 10.21608/ijci.2020.26944.1016
simple missing data estimation algorithm in wsn based on spatial and temporal correlation
1computer sciences department,faculty of computers and information, Menoufia university
2Faculty of Computer and Information Menoufia University
3computer science faculty of computers and informations menofia university
Abstract
we have a common problem in wireless sensor networks which is the missing data problem due to the nature of the wireless communication and the limited resources of the sensor nodes. This problem can't be ignored because it has a negative effect on the applications that use the sensor data. Estimating these missing data is important for the applications that concern with the sensor data. However, the traditional estimation techniques failed to be applied with the sensor data and the existing techniques have high computation complexity, high computation time, or low accuracy. So we introduce the simplified Spatial and Temporal Correlation (STC) estimation algorithm which uses the most related surrounding previous data to increase the accuracy of the estimation and reduce incremental error. The proposed algorithm utilizes the time correlation by using the closet data before the time of missing and utilizes the space correlation by using the data of the nearest sensor depending on the missing pattern. The experimental results show that our algorithm can reduce the error in the estimating process compared with the other algorithms in most of the missing patterns.
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