LaPS

Abstract

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment; this especially holds for forests, the focus of this article, where trees greatly affect communication. We present LaPS (<underline>L</underline>iDAR-assisted <underline>P</underline>lacement for wireless <underline>S</underline>ensor networks), an approach exploiting remote sensing to identify the best node placement automatically and prior to deployment . Airborne Light Detection and Ranging (LiDAR) data acquired for the target forest are automatically processed to estimate its properties (e.g., tree position and diameter) that, once incorporated into a specialized path loss model, enable per-link estimates of the radio signal attenuation induced by trees. Finally, a genetic algorithm explores placement options by evolving toward a (sub-)optimal solution while satisfying the user’s spatial and network requirements, whose formulation is very flexible and broadly applicable. Our experiments, focused on a real forest, confirm that LaPS yields topologies of significantly higher quality w.r.t. approaches using a regular placement or a standard path loss model. Further, the ability to quickly explore the impact that changes in user requirements have on topology is invaluable to improve the operation of WSNs and reduce the effort of their in-field deployment.