The effectiveness of radar interferometric techniques in non-urban areas can often be compromised due to the lack of stable natural targets. This drawback can be partially compensated through the installation of reference targets, characterized by a bright and stable radar response. The installation of passive corner reflectors (PCR) often represents a valid aid, but these objects are usually cumbersome, and suffer from severe weather conditions; furthermore, the installation of a PCR can be difficult and costly, especially in places with hard accessibility. Active reflectors (AR) represent a less cumbersome alternative to PCRs, while still providing a stable phase response. This paper describes the design, implementation, and test of an AR prototype, designed to operate with the Sentinel-1 synthetic aperture radar (SAR), aimed at providing a fair performance/cost benefit. These characteristics, obtained through a tradeoff between the use of off-the-shelf components and a simple architecture, can make the setup of a dense network (i.e., tens of devices) in the monitored areas feasible. The paper reports the design, implementation, and the analysis of different tests carried out in a laboratory, and in a real condition in the field, to illustrate AR reliability and estimate its phase stability.

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The introduction of AI-based smart-sensors on the network might suppose stringent requirements for the network edge, including the necessity to process real-time video feeds. Moreover, the introduction of vehicular communications allows the multiple location placement of necessary computational processes. To this end, we have proposed an AI-based smartsensor solution that is able to be deployed at the extreme edge of the network (i.e., on the vehicle). The architecture for the connected vehicle is presented and accuracy results are provided for the proposed smart-sensor.