A support vector machine for terrain classification in on-demand deployments of wireless sensor networks

Terrain characteristics can significantly alter the quality of the results provided by the deployment methodology of large-scale wireless sensor networks. For example, transmissions between nodes that are heavily obstructed will require additional transmission power to establish connection between nodes. In some cases, heavily obstructed areas may prevent nodes from establishing a connection at all. Therefore, terrain analysis and classification of specific deployment areas should be incorporated in the methodology process for evaluation and optimization of the performance of wireless sensor networks upon deployment. Although there exists radio frequency (RF) models capable of modeling obstructions, such as vegetation, foliage, etc., automatic assignment of parameter values for these models may be troublesome, specifically in highly irregular deployments terrains, where proximity of poor and optimal conditions for signal propagation may be adjacent to each other. In these situations, parameter estimation for modeling terrain obstruction may result in overly optimistic or pessimistic results, causing characterizations or predictions that deviate from the true performance of the WSN once deployed. This paper presents the results of employing a support vector machine for automatic terrain classification. The approach can be used to automatically determine areas of high obstruction, which is essential to estimate obstruction parameters in simulations and enhancing the overall decision-making process during pre-deployment of large-scale and irregular deployment terrains.