Confident information coverage in sensor networks for field reconstruction

Coverage is an important performance metric in sensor networks. The traditional disk coverage model uses a very simple geometric relation between a sensor and its surrounding space points to capture the sensor's sensing capability and quality, which are not enough for many practical applications. In this article, motivated from the application of precision agriculture, we propose a new confident information coverage model for field reconstruction, where the objective is to obtain reconstruction maps of some physical phenomena's attribute with a given reconstruction quality for the whole sensor field, including points been sampled and not sampled. The proposed model is downward compatible with the disk coverage model, while it can greatly reduce sensor density for area coverage. Simulation results show that for the same reconstruction quality, the required sensor density based on the proposed new model is much less than that based on the disk model in both the deterministic and random sensor deployment. In practice, the proposed model helps to determine the number of sensors to be deployed for a given farmland and their locations in the deterministic deployment. The proposed model can also help to guide network operations for energy efficient data collection with guaranteed reconstruction quality.