Speaker
Description
Opportunistic sensing is an unconventional approach to data collection in practical applications such as environmental monitoring, weather forecast, climatology, surveillance, etc., which uses devices that are not purposely dedicated to this task. In the last 15 years, opportunistic sensing gained a steadily increasing attention by researchers and nowadays a broad and solid literature is available on this topic. In particular, the opportunistic use of pre-existing microwave links, either terrestrial or satellite, emerged as an effective and promising technique for inferring accurate real-time estimates of precipitation intensity from the measurement of signal attenuation at the receiver site. Furthermore, the opportunistic use of signals received by ground terminals of satellite services users/subscribers, mainly TV broadcasting (but also broadband access and IoT), revealed to be particularly appealing due to the low-cost and the ease of deployment of the receiving devices, which are acting as sensors. To this respect, this tutorial contribution is aimed at: (1) illustrating, step by step, the data processing chain of a satellite-based opportunistic rain sensing system, from the measurement of the received satellite signal strength (briefly addressed to as "raw data") to the estimation of the precipitation intensity; (2) addressing the disturbances affecting collection of the data and the technical challenges involved in their processing; (3) identifying the key performance indicators to assess the accuracy of opportunistic estimates against measurements collected by conventional sensors, such as rain gauges or radars;(4) illustrating some practical case studies and outlining some future perspectives.
The author greatly acknowledges SCORE project (funded by EC H2020, G.A. no. 101003534), Space It Up project (funded by ASI and MUR, Contract no. 2024-5-E.0, CUP no. I53D24000060005), FoReLab project (Funded by MUR in the framework of the Departments of Excellence), and COST Action CA20136 OPENSENSE.
