Packet Loss in Terrestrial Wireless and Hybrid Networks

The presence of both a geostationary satellite link and a terrestrial local wireless link on the same path of a given network connection is becoming increasingly common, thanks to the popularity of the IEEE 802.11 protocol. The most common situation where a hybrid network comes into play is having a Wi-Fi link at the network edge and the satellite link somewhere in the network core. Example of scenarios where this can happen are ships or airplanes where Internet connection on board is provided through a Wi-Fi access point and a satellite link with a geostationary satellite; a small office located in remote or isolated area without cabled Internet access; a rescue team using a mobile ad hoc Wi-Fi network connected to the Internet or to a command centre through a mobile gateway using a satellite link. The serialisation of terrestrial and satellite wireless links is problematic from the point of view of a number of applications, be they based on video streaming, interactive audio or TCP. The reason is the combination of high latency, caused by the geostationary satellite link, and frequent, correlated packet losses caused by the local wireless terrestrial link. In fact, GEO satellites are placed in equatorial orbit at 36,000 km altitude, which takes the radio signal about 250 ms to travel up and down. Satellite systems exhibit low packet loss most of the time, with typical project constraints of 10−8 bit error rate 99% of the time, which translates into a packet error rate of 10−4, except for a few days a year. Wi-Fi links, on the other hand, have quite different characteristics. While the delay introduced by the MAC level is in the order of the milliseconds, and is consequently too small to affect most applications, its packet loss characteristics are generally far from negligible. In fact, multipath fading, interference and collisions affect most environments, causing correlated packet losses: this means that often more than one packet at a time is lost for a single fading even

Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user’s movements to feed back to the RSS-based localization syste

An experimental characterization of reservoir computing in ambient assisted living applications

In this paper, we present an introduction and critical experimental evaluation of a reservoir computing (RC) approach for ambient assisted living (AAL) applications. Such an empirical analysis jointly addresses the issues of efficiency, by analyzing different system configurations toward the embedding into computationally constrained wireless sensor devices, and of efficacy, by analyzing the predictive performance on real-world applications. First, the approach is assessed on a validation scheme where training, validation and test data are sampled in homogeneous ambient conditions, i.e., from the same set of rooms. Then, it is introduced an external test set involving a new setting, i.e., a novel ambient, which was not available in the first phase of model training and validation. The specific test-bed considered in the paper allows us to investigate the capability of the RC approach to discriminate among user movement trajectories from received signal strength indicator sensor signals. This capability can be exploited in various AAL applications targeted at learning user indoor habits, such as in the proposed indoor movement forecasting task. Such a joint analysis of the efficiency/efficacy trade-off provides novel insight in the concrete successful exploitation of RC for AAL tasks and for their distributed implementation into wireless sensor networks

Machine Learning Methods with Decision Forests for Parkinson's Detection

Biomedical engineers prefer decision forests over traditional decision trees to design state-of-the-art Parkinson's Detection Systems (PDS) on massive acoustic signal data. However, the challenges that the researchers are facing with decision forests is identifying the minimum number of decision trees required to achieve maximum detection accuracy with the lowest error rate. This article examines two recent decision forest algorithms Systematically Developed Forest (SysFor), and Decision Forest by Penalizing Attributes (ForestPA) along with the popular Random Forest to design three distinct Parkinson's detection schemes with optimum number of decision trees. The proposed approach undertakes minimum number of decision trees to achieve maximum detection accuracy. The training and testing samples and the density of trees in the forest are kept dynamic and incremental to achieve the decision forests with maximum capability for detecting Parkinson's Disease (PD). The incremental tree densities with dynamic training and testing of decision forests proved to be a better approach for detection of PD. The proposed approaches are examined along with other state-of-the-art classifiers including the modern deep learning techniques to observe the detection capability. The article also provides a guideline to generate ideal training and testing split of two modern acoustic datasets of Parkinson's and control subjects donated by the Department of Neurology in Cerrahpaşa, Istanbul and Departamento de Matemáticas, Universidad de Extremadura, Cáceres, Spain. Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson's disease detector with a little number of decision trees in the forest to score the highest detection accuracy of 94.12% to 95.00%

The Meaning of Sleep Quality: A Survey of Available Technologies

Sleep is an important part of the human daily routine. Restoring sleep is strongly related to a better physical, cognitive, and psychological well-being. By contrast, poor or disordered sleep leads to possible impairments of cognitive and psychological functioning and to a worsened general physical health. In this context, understanding changes in sleep quality becomes a research imperative that leads to the need for the definition of what restoring or quality sleep means. This understanding of what "sleep quality" means requires a cross-domain investigation. It arises the need for a comprehensive study that offers a complete taxonomy of sleep monitoring systems, with a focus on sleep quality, and that gives useful insights about which combination of metrics, signals, and sleep variables is the best in relation to different categories of users. The proposed study is focused on systematically categorizing the methods and approaches for sleep quality understanding, with an emphasis on technological approaches, including wearable, on-bed, and actigraphy devices. It offers a systematic review for researchers who are interested in sleep quality identification tasks, and highlights strengths and weaknesses of state-of-the-art metrics and solutions in order to suggest the best choice for new potential research challenges in the field. Another important outcome of the proposed work is the study of the impact on the identified signal metrics and solutions of the different target user populations with their specific user requirements

Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal

Abstract Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects' age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen's kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel's performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database

Detecting User’s Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behaviour shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments