Multi-band sub-GHz technology recognition on NVIDIA’s Jetson Nano

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA’s Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware

Edge inference for UWB ranging error correction using autoencoders

Indoor localization knows many applications, such as industry 4.0, warehouses, healthcare, drones, etc., where high accuracy becomes more critical than ever. Recent advances in ultra-wideband localization systems allow high accuracies for multiple active users in line-of-sight environments, while they still introduce errors above 300 mm in non-line-of-sight environments due to multi-path effects. Current work tries to improve the localization accuracy of ultra-wideband through offline error correction approaches using popular machine learning techniques. However, these techniques are still limited to simple environments with few multi-path effects and focus on offline correction. With the upcoming demand for high accuracy and low latency indoor localization systems, there is a need to deploy (online) efficient error correction techniques with fast response times in dynamic and complex environments. To address this, we propose (i) a novel semi-supervised autoencoder-based machine learning approach for improving ranging accuracy of ultra-wideband localization beyond the limitations of current improvements while aiming for performance improvements and a small memory footprint and (ii) an edge inference architecture for online UWB ranging error correction. As such, this paper allows the design of accurate localization systems by using machine learning for low-cost edge devices. Compared to a deep neural network (as state-of-the-art, with a baseline error of 75 mm) the proposed autoencoder achieves a 29% higher accuracy. The proposed approach leverages robust and accurate ultra-wideband localization, which reduces the errors from 214 mm without correction to 58 mm with correction. Validation of edge inference using the proposed autoencoder on a NVIDIA Jetson Nano demonstrates significant uplink bandwidth savings and allows up to 20 rapidly ranging anchors per edge GPU

Automatic equine activity detection by convolutional neural networks using accelerometer data

In recent years, with a widespread of sensors embedded in all kind of mobile devices, human activity analysis is occurring more often in several domains like healthcare monitoring and fitness tracking. This trend did also enter the equestrian world because monitoring behaviours can yield important information about the health and welfare of horses. In this research, a deep learning-based approach for activity detection of equines is proposed to classify seven activities based on accelerometer data. We propose using Convolutional Neural Networks (CNN) by which features are extracted automatically by using strong computing capabilities. Furthermore, we investigate the impact of the sampling frequency, the time series length and the type of underground on which the data is gathered on the recognition accuracy and evaluate the model on three types of experimental datasets that are compiled of labelled accelerometer data gathered from six different subjects performing seven different activities. Afterwards, a horse-wise cross validation is carried out to investigate the impact of the subjects themselves on the model recognition accuracy. Finally, a slightly adjusted model is validated on different amounts of 50 Hz sensor data. A 99% accuracy can be reached for detecting seven behaviours of a seen horse when the sampling rate is 25 Hz and the time interval is 2.1 s. Four behaviours of an unseen horse can be detected with the same accuracy when the sampling rate is 69 Hz and the time interval is 2.4 s. Moreover, the accuracy of the model for the three datasets decreased on average with about 4.75% when the sampling rate was decreased from 200 Hz to 25 Hz and with 5.27% when the time interval was decreased from 3 s to 0.6 s. In addition, the classification performance of the activity "walk" was not influenced by the type of underground the horse was performing this movement on and even the model could conclude from which underground the data was gathered for three out of four undergrounds with accuracies above 93% at time intervals higher than 1.2 s. This ensures the evaluation of activity patterns in real world circumstances. The performance and ability of the model to generalise is validated on 50 Hz data from different horse types, using ten-fold cross validation, reaching a mean classification accuracy of 97.84% and 96.10% when validated on a lame horse and pony, respectively. Moreover, in this work we show that using data from one sensors is at the cost of only 0.24% reduction in accuracy (99.42% vs 99.66%)

Feasibility of wireless horse monitoring using a kinetic energy harvester model

To detect behavioral anomalies (disease/injuries), 24 h monitoring of horses each day is increasingly important. To this end, recent advances in machine learning have used accelerometer data to improve the efficiency of practice sessions and for early detection of health problems. However, current devices are limited in operational lifetime due to the need to manually replace batteries. To remedy this, we investigated the possibilities to power the wireless radio with a vibrational piezoelectric energy harvester at the leg (or in the hoof) of the horse, allowing perpetual monitoring devices. This paper reports the average power that can be delivered to the node by energy harvesting for four different natural gaits of the horse: stand, walking, trot and canter, based on an existing model for a velocity-damped resonant generator (VDRG). To this end, 33 accelerometer datasets were collected over 4.5 h from six horses during different activities. Based on these measurements, a vibrational energy harvester model was calculated that can provide up to 64.04 mu W during the energetic canter gait, taking an energy conversion rate of 60% into account. Most energy is provided during canter in the forward direction of the horse. The downwards direction is less suitable for power harvesting. Additionally, different wireless technologies are considered to realize perpetual wireless data sensing. During horse training sessions, BLE allows continues data transmissions (one packet every 0.04 s during canter), whereas IEEE 802.15.4 and UWB technologies are better suited for continuous horse monitoring during less energetic states due to their lower sleep current

UWB anchor nodes self-calibration in NLOS conditions : a machine learning and adaptive PHY error correction approach

Ultra-wideband (UWB) positioning performance is highly related to the accuracy of the coordinates of the fixed anchor nodes, which form the system infrastructure. The process of determining the position of the anchors is called calibration. In an anchor-based system, it is crucial for the fixed nodes to know their locations with the highest possible accuracy. However, in certain situations, it is almost impossible to perform the calibration manually, e.g., during emergency interventions. Moreover, calibration is always delicate and time-consuming. We designed an effortless and accurate self-calibration algorithm that does not require any manual intervention to precisely pinpoint the position of the anchors. This paper presents an innovative algorithm that combines machine learning and exploits the time resolution capabilities of UWB with adaptive physical settings to enable the automatic calibration of the fixed anchor nodes, even in realistic NLOS (non-line-of-sight) conditions. The self-calibration algorithm combines iterative gradient descent to pinpoint the positions of the anchors and uses error detection and correction from a convolutional neural network. Moreover, the algorithm can use a different set of settings for each anchor pair. This is done to ensure the most robust and accurate communication between nodes. Extensive measurements were carried out to allow anchors to estimate distances among each others. Distances were then combined and processed by the self-calibration algorithm. Experimental evaluation in two complex and large environments with many obstacles and reflections shows that accuracy reached by the algorithm is about 2.4 cm on average and 95th percentile is 5.7 cm, in best case. The results refer to the relative positions among the anchors. Results prove that in order to precisely calibrate the anchors nodes in an UWB positioning system, high correctness can be obtained by combining the accuracy of UWB together with deep learning and adaptive PHY modulation schemes

Badminton activity recognition using accelerometer data

A thorough analysis of sports is becoming increasingly important during the training process of badminton players at both the recreational and professional level. Nowadays, game situations are usually filmed and reviewed afterwards in order to analyze the game situation, but these video set-ups tend to be difficult to analyze, expensive, and intrusive to set up. In contrast, we classified badminton movements using off-the-shelf accelerometer and gyroscope data. To this end, we organized a data capturing campaign and designed a novel neural network using different frame sizes as input. This paper shows that with only accelerometer data, our novel convolutional neural network is able to distinguish nine activities with 86% precision when using a sampling frequency of 50 Hz. Adding the gyroscope data causes an increase of up to 99% precision, as compared to, respectively, 79% and 88% when using a traditional convolutional neural network. In addition, our paper analyses the impact of different sensor placement options and discusses the impact of different sampling frequenciess of the sensors. As such, our approach provides a low cost solution that is easy to use and can collect useful information for the analysis of a badminton game

Towards low-complexity wireless technology classification across multiple environments

To cope with the increasing number of co-existing wireless standards, complex machine learning techniques have been proposed for wireless technology classification. However, machine learning techniques in the scientific literature suffer from some shortcomings, namely: (i) they are often trained using data from only a single measurement location, and as such the results do not necessarily generalise and (ii) they typically do not evaluate complexity/accuracy trade-offs of the proposed solutions. To remedy these shortcomings, this paper investigates which resource-friendly approaches are suitable across multiple heterogeneous environments. To this end, the paper designs and evaluates classifiers for LTE, Wi-Fi and DVB-T technologies using multiple datasets to investigate the complexity/accuracy trade-offs between manual feature extraction and automatic feature learning techniques. Our wireless technology classification reaches an accuracy up to 99%. Moreover, we propose the use of data augmentation techniques to extend these results to unseen environments at the cost of only 2% reduction in accuracy. When concerning generalisation capabilities, complex automatic learning techniques surpass simple manual feature extraction approaches. Finally, the complexity of these automatic learning techniques can be significantly reduced by using computationally less intensive received signal strength indicator data while reaching acceptable accuracies in unseen environments (92% vs 97%). (C) 2019 Elsevier B.V. All rights reserved

Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System

Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naive Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method

Adaptive CNN-based private LTE solution for fair coexistence with Wi-Fi in unlicensed spectrum

Recently, the expansion of wireless network deployments is resulting in increased scarcity of available licensed radio spectrum. As the domain of wireless communications is progressing rapidly, many industries are looking into wireless network solutions that can increase their productivity. Private LTE is a promising wireless network solution as it can be customised independently without the control of a mobile network operator while providing reliable and spectrum efficient services. For this reason, the deployment of Private LTE in the unlicensed spectrum and its coexistence with Wi-Fi is becoming a popular topic in research. In this paper, we propose a coexistence scheme for private LTE network in unlicensed spectrum that enables a fair spectrum sharing with co-located Wi-Fi networks. This is achieved by exploiting various LTE frame configurations consisting of different combinations of downlink, uplink, special subframe and muted subframes. The configuration of a single frame is decided based on a rule based algorithm that exploits Wi-Fi spectrum occupancy statistics that is obtained from a technology recognition system which is based on a Convolutional Neural Network. The performance of the proposed private LTE scheme and its coexistence with Wi-Fi is investigated for different traffic scenarios showcasing how the proposed scheme can lead to a harmless coexistence of LTE and Wi-Fi

Multi-Static UWB Radar-based Passive Human Tracking Using COTS Devices

Due to its high delay resolution, the ultra-wideband (UWB) technique has been widely adopted for fine-grained indoor localization. Instead of active positioning, UWB radar-based passive human tracking is explored using commercial off-the-shelf (COTS) devices. To extract the time-of-flight (ToF) reflected by the moving person, the accumulated channel impulse responses (CIR) and the corresponding variances are used to train the convolutional neural networks (CNN) model. Particle filter algorithm is adopted to track the moving person based on the extracted ToFs of all pairs of links. Experimental results show that the proposed CIR- and variance-based CNN models achieve less than 30-cm root-mean-square errors (RMSEs). Especially, the variance-based CNN model is robust to the scenario changing and promising for practical applications