Wireless sensor networks offer unprecedented opportunities to monitor natural ecosystems. However, despite the growing number of applications (e.g., forest fire detection, wildlife monitoring), the deployment challenges posed by the real-world natural environment still hinder the widespread adoption of this technology. In particular, the unpredictability of the low-power wireless channel in the presence of vegetation requires costly trial-and-error pilot campaigns to understand where and how to place the wireless nodes. In this paper, we propose a technique based on remote sensing for accurately estimating low-power radio signal attenuation in forest environments. We leverage airborne Light Detection and Ranging (LiDAR) instruments and related automatic data analysis systems to determine local forest attributes (e.g., tree density) that, once factored into a specialized radio path loss model, enable accurate estimation of the received signal power. Our approach is i) automatic, i.e., it does not require in-field campaigns, and ii) fine-grained, i.e., it enables per-link estimates. Our validation from deployments in a real forest shows that the error of our per-link estimates of the received signal power is around ± 6 dBm - the accuracy of RSSI readings from the radio transceiver.
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|Titolo:||Estimating Low-power Radio Signal Attenuation in Forests: A LiDAR-based Approach|
|Autori:||Demetri, Silvia; Picco, Gian Pietro; Bruzzone, Lorenzo|
|Titolo del volume contenente il saggio:||Proceedings of the 11th IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 2015)|
|Luogo di edizione:||Piscataway|
|Anno di pubblicazione:||2015|
|Appare nelle tipologie:||04.1 Saggio in atti di convegno (Paper in proceedings)|