Redução de dados em redes de sensores sem fio tolerantes a atrasos
Abstract
This paper presents a delay-tolerant sensor network model for environmental applications where a data-aware drop strategy is applied to improve the phenomenon coverage. Our model was designed for applications that monitor the forest temperature incidence for a wildlife observation. The proposed solution modeling is comprised of i. Phenomenon generation based on Gaussian random field; ii. Sensing nodes with a mobile sink; iii. Data processing based on a data-aware drop strategy; and iv. Phenomenon reconstruction based on simple kriging interpolation. Besides the satisfactory application of our model, the results show that the performance of data-aware strategy is approximately twice as better than traditional approaches in all evaluated scenarios.
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