Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm

Secure positioning in wireless sensor networks through enlargement miscontrol detection

Wireless sensor networks enable a wealth of new applications in areas such as military, medical, environmental, transportation, smart city, and so on. In many of these scenarios, we need to measure in a secure way the positions of the sensors. Existing range-based techniques for secure positioning require a burdensome infrastructure, with many fixed anchors. Reducing the infrastructure would reduce deployment cost and foster the adoption of secure positioning solutions in wireless sensor networks. In this article, we propose SPEM, a secure positioning system based on multilateration and ultra-wideband (UWB) distance bounding protocols. The key idea behind SPEM is to leverage the low probability that an adversary has of controlling enlargement attacks against UWB. We estimate such a probability by a thorough study and signal-level simulations of the UWB physical layer. We test SPEM both in a simulated environment and in a real indoor environment using real UWB transceivers. We show that SPEM needs far less infrastructure than state-of-the-art solutions (-22% to -93%, depending on the anchor deployment method), while achieving high levels of security against smart and determined adversaries