Abstract
Sensor deployment routines for environment sensing applications make various assumptions about the underlying spatiotemporal field. These assumptions render the deployment ineffective in a practical scenario. This article proposes a two-step process: initially, the sensors are deployed based on geographical covariates. Then, after a fixed period, the data collected from sensors are used to find optimal locations for sensors. The spatiotemporal representation of sensor values has been modeled as the sum of a systematic trend component and a residual process. The trend component is modeled as the sum of deterministic functions, and the residual component is modeled using support vector regression. The locations with maximum support vector count in the residual model are identified as optimal for the deployment of sensors. The method can be used for both static and dynamic deployments. The proposed strategy has been applied to a specific case study of air pollution dataset.
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