Ultra-Wideband (UWB) is a Radio Frequency technology that is currently used for accurate indoor localization. However, the cost of deploying such a system is large, mainly due to the need for ...manually measuring the exact location of the installed infrastructure devices (“anchor nodes”). Self-calibration of UWB reduces deployment costs, because it allows for automatic updating of the coordinates of fixed nodes when they are installed or moved. Additionally, installation costs can also be reduced by using collaborative localization approaches where mobile nodes act as anchors. This article surveys the most significant research that has been done on self-calibration and collaborative localization. First, we find that often these terms are improperly used, leading to confusion for the readers. Furthermore, we find that in most of the cases, UWB-specific characteristics are not exploited, so crucial opportunities to improve performance are lost. Our classification and analysis provide the basis for further research on self-calibration and collaborative localization in the deployment of UWB indoor localization systems. Finally, we identify several research tracks that are open for investigation and can lead to better performance, e.g., machine learning and optimized physical settings.
LoRaWAN is one of the low power wide area network (LPWAN) technologies that have received significant attention by the research community in the recent years. It offers low-power, low-data rate ...communication over a wide range of covered area. In the past years, the number of publications regarding LoRa and LoRaWAN has grown tremendously. This paper provides an overview of research work that has been published from 2015 to September 2018 and that is accessible via Google Scholar and IEEE Explore databases. First, a detailed description of the technology is given, including existing security and reliability mechanisms. This literature overview is structured by categorizing papers according to the following topics: (i) physical layer aspects; (ii) network layer aspects; (iii) possible improvements; and (iv) extensions to the standard. Finally, a strengths, weaknesses, opportunities and threats (SWOT) analysis is presented along with the challenges that LoRa and LoRaWAN still face.
Radio frequency (RF)-based indoor positioning systems (IPSs) use wireless technologies (including Wi-Fi, Zigbee, Bluetooth, and ultra-wide band (UWB)) to estimate the location of persons in areas ...where no Global Positioning System (GPS) reception is available, for example in indoor stadiums or sports halls. Of the above-mentioned forms of radio frequency (RF) technology, UWB is considered one of the most accurate approaches because it can provide positioning estimates with centimeter-level accuracy. However, it is not yet known whether UWB can also offer such accurate position estimates during strenuous dynamic activities in which moves are characterized by fast changes in direction and velocity. To answer this question, this paper investigates the capabilities of UWB indoor localization systems for tracking athletes during their complex (and most of the time unpredictable) movements. To this end, we analyze the impact of on-body tag placement locations and human movement patterns on localization accuracy and communication reliability. Moreover, two localization algorithms (particle filter and Kalman filter) with different optimizations (bias removal, non-line-of-sight (NLoS) detection, and path determination) are implemented. It is shown that although the optimal choice of optimization depends on the type of movement patterns, some of the improvements can reduce the localization error by up to 31%. Overall, depending on the selected optimization and on-body tag placement, our algorithms show good results in terms of positioning accuracy, with average errors in position estimates of 20 cm. This makes UWB a suitable approach for tracking dynamic athletic activities.
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•Convolutional neural network (CNN) for behaviour detection using accelerometer data.•No feature engineering required.•High accuracies reached at low sampling rates and small time ...intervals.•Decreasing sampling rate and time interval reduced the classification accuracy.•Classification performance of walk was not influenced by the type of underground.
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%).
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.
Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union ...(EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions.
Dynamic Spectrum Sharing (DSS) is an enabler for a seamless transition from 4G Long Term Evolution (LTE) to 5G New Radio (NR) by utilizing existing LTE bands without static spectrum re-farming. In ...this paper, we propose a cross-band DSS scheme that utilizes the Multimedia Broadcast Multicast Service over a Single Frequency Network (MBSFN) feature of an LTE network and the Multicast Broadcast Service (MBS) feature of an NR network. The proposed DSS scheme utilizes LTE and NR resource controllers to assign muted MBSFN subframes on the LTE band and muted MBS subframes on the NR band based on traffic needs. In contrast to the state-of-the-art, the proposed DSS scheme does not require a coordination signaling channel between the LTE and NR networks. Instead, a machine learning-based Technology Recognition and Traffic Characterization (TRTC) system is used to identify and characterize traffic patterns. The LTE and NR resource controllers use the TRTC to sense the muted subframes and offload traffic accordingly. On average, the proposed DSS, as compared to static band configuration, improves the LTE throughput, NR throughput, LTE band spectrum utilization efficiency, and NR band spectrum utilization efficiency by 13.5%, 8.3%, 11.8%, and 20.7%, respectively.
Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and ...more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.
This paper presents end-to-end learning from spectrum data-an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural ...networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.
Accurate radio frequency (RF)-based indoor localization systems are more and more applied during sports. The most accurate RF-based localization systems use ultra-wideband (UWB) technology; this is ...why this technology is the most prevalent. UWB positioning systems allow for an in-depth analysis of the performance of athletes during training and competition. There is no research available that investigates the feasibility of UWB technology for indoor track cycling. In this paper, we investigate the optimal position to mount the UWB hardware for that specific use case. Different positions on the bicycle and cyclist were evaluated based on accuracy, received power level, line-of-sight, maximum communication range, and comfort. Next to this, the energy consumption of our UWB system was evaluated. We found that the optimal hardware position was the lower back, with a median ranging error of 22 cm (infrastructure hardware placed at 2.3 m). The energy consumption of our UWB system is also taken into account. Applied to our setup with the hardware mounted at the lower back, the maximum communication range varies between 32.6 m and 43.8 m. This shows that UWB localization systems are suitable for indoor positioning of track cyclists.
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