Llocus

Abstract

We present LLOCUS, a novel learning-based system that uses mobile crowdsourced RF sensing to estimate the location and power of unknown mobile transmitters in real time, while allowing unrestricted mobility of the crowdsourcing participants. We carefully identify and tackle several challenges in learning and localizing, based on RSS, in such a dynamic environment. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the power of a transmitter at an unknown location, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using three experiments in different indoor and outdoor environments. We find that LLOCUS reduces the localization error by 17-68% compared to several non-learning methods. CCS CONCEPTS • Networks → Location based services; Mobile ad hoc networks; Mobile and wireless security.

REM-U-Net: Deep Learning Based Agile REM Prediction With Energy-Efficient Cell-Free Use Case

“…While obtaining synthetic training data for REM prediction is significantly easier than measurement data, it is still externally demanding in terms of computational resources needed [14] and even impractical to obtain in all environment scenarios and all possible locations in a timely manner. Alternatively, exploiting a limited-size dataset to obtain a generalized model that can predict REM for unseen environments is a promising solution that can be employed in a reactive [2] or proactive [10] manner. When sparse LSF data are available from the targeted active transmitter, i.e., for the case of reactive REM prediction, several methods have been investigated.…”

“…One of the simplest methods is to perform the predictions as a weighted average of the available LSF measurements. For instance, in inverse distance weighting (IDW) methods, the weighting is done using a heuristic approach based on the inverse of the distance between the target location and the measurement location [2]. Another weighted-average-based example is the Kriging interpolation method, which obtains weights based on an optimization approach [23,24].…”

“…Predicting radio environment maps (REMs) that capture LSF could facilitate deployment planning and operation optimization of wireless networks. For instance, REM prediction can play an essential role in spectrum sharing [1], localization [2], path planning for unmanned aerial vehicles (UAVs) [3], finding coverage holes [4], optimizing resource allocation in dense networks [5], etc.…”