With the emergence of the 6G technology, integrated sensing and communication (ISAC) has become a hot-spot vertical application. The low-altitude scenario is considered to be a significant use case of the ISAC. However, the existing channel model is hard to meet the demands of the sensing function. The radar-cross-section (RCS) is a critical feature for the sensing part, while accurate RCS data for the typical frequency band of ISAC are still lacking. Therefore, this paper conducts measurements and analysis of the RCS data of the unmanned aerial vehicles (UAVs) under multiple poses and angles in real flying conditions. The echo from a UAV is acquired in an anechoic chamber, and the RCS values are calculated. The results of different flying attitudes are analyzed, providing RCS features for the ISAC applications.

Mobile edge users (MEUs) collect data from sensor devices and report to cloud systems, which can facilitate numerous applications in sensor?cloud systems (SCS). However, because there is no effective way to access the ground truth to verify the quality of sensing devices’ data or MEUs’ reports, malicious sensing devices or MEUs may report false data and cause damage to the platform. It is critical for selecting sensing devices and MEUs to report truthful data. To tackle this challenge, a novel scheme that uses unmanned aerial vehicles (UAV) to detect the truth of sensing devices and MEUs (UAV?DT) is proposed to construct a clean data collection platform for SCS. In the UAV?DT scheme, the UAV delivers check codes to sensor devices and requires them to provide routes to the specified destination node. Then, the UAV flies along the path that enables maximal truth detection and collects the information of the sensing devices forwarding data packets to the cloud during this period. The information collected by the UAV will be checked in two aspects to verify the credibility of the sensor devices. The first is to check whether there is an abnormality in the received and sent data packets of the sensing devices and an evaluation of the degree of trust is given; the second is to compare the data packets submitted by the sensing devices to MEUs with the data packets submitted by the MEUs to the platform to verify the credibility of MEUs. Then, based on the verified trust value, an incentive mechanism is proposed to select credible MEUs for data collection, so as to create a clean data collection sensor?cloud network. The simulation results show that the proposed UAV?DT scheme can identify the trust of sensing devices and MEUs well. As a result, the proportion of clean data collected is greatly improved.