An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms

Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon

Contributions to the advancement of wireless indoor localization techniques: signal characterization and distributed computing

The development of wireless networks and devices equipped with multiple sensors, and their connection to storage centres and data processing through the Internet, has led to the implementation of Internet of Things (IoT). The growing interest in the use of IoT technologies has generated the development of numerous and diverse applications. The proper functioning of these applications requires the control of enormous flows of data generated by mobile devices to the intelligent decision-making centres deployed in the cloud. Many of the services provided by the applications are based on knowledge of the location and profile of the end user. The contribution of this Doctoral Thesis focuses on the main components of the development of localization systems for IoT applications: (i) algorithms for processing wireless signals that enable indoor localization; and (ii) a distributed infrastructure to optimize the processing of these algorithms. Regarding the first point, the work of this Doctoral Thesis focuses on the characterization of wireless signals behaviour. To this end, this point this research make use of Non-Linear, Linear and Ensembled models, and optimization using genetic algorithms in order to find the best transmission power levels setup for wireless transmitter. The results of this research show that the use of classification algorithms in the process of signal characterization is able to greatly improve the performance-based indoor localization mechanisms. On the second point, this Doctoral Thesis proposes to make use of distributed systems, specifically, Fog Computing. This architecture has been designed to respond to the needs of a large number of applications. With the aim of improving its performance, this research proposes a robust and energy-efficient distributed infrastructure

Smart cities : Un enfoque práctico sobre una metrópolis y auditoría en Lima (Perú)

Análisis del origen del concepto de smart cities que deriva de la gestión y ahorro energético, comparación de ejemplos de smart cities actuales y realización de un caso práctico sobre Lima analizando exhaustivamente qué componentes tenemos según las diferentes métricas e indicadores y qué componentes faltarían para poder ser una ciudad "smart".Anàlisi de l'origen del concepte d'smart cities que deriva de la gestió i estalvi energètic, comparació d'exemples de smart cities actuals i realització d'un cas pràctic sobre Lima analitzant exhaustivament quins components tenim segons les diferents mètriques i indicadors i quins components faltarien per poder ser una ciutat "smart"

Smart cities : Un enfoque práctico sobre una metrópolis y auditoría en Lima (Perú)

Análisis del origen del concepto de smart cities que deriva de la gestión y ahorro energético, comparación de ejemplos de smart cities actuales y realización de un caso práctico sobre Lima analizando exhaustivamente qué componentes tenemos según las diferentes métricas e indicadores y qué componentes faltarían para poder ser una ciudad "smart".Anàlisi de l'origen del concepte d'smart cities que deriva de la gestió i estalvi energètic, comparació d'exemples de smart cities actuals i realització d'un cas pràctic sobre Lima analitzant exhaustivament quins components tenim segons les diferents mètriques i indicadors i quins components faltarien per poder ser una ciutat "smart"

TINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networks

The growing interest in the use of algorithms-based machine learning for predictive tasks has generated a large and diverse development of algorithms. However, it is widely known that not all of these algorithms are adapted to efficient solutions in certain tidy data format datasets. For this reason, novel techniques are currently being developed to convert tidy data into images with the aim of using Convolutional Neural Networks (CNNs). TINTO offers the opportunity to convert tidy data into images through the representation of characteristic pixels by implementing two dimensional reduction algorithms: Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE). Our proposal also includes a blurring technique, which adds more ordered information to the image and can improve the classification task in CNNs

Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries

Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on sex, age, weight, height, and systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8–6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9–10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in the Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. An ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available

An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications

Abstract Internet of Things (IoT) has posed new requirements to the underlying processing architecture, specially for real-time applications, such as event-detection services. Complex Event Processing (CEP) engines provide a powerful tool to implement these services. Fog computing has raised as a solution to support IoT real-time applications, in contrast to the Cloud-based approach. This work is aimed at analysing a CEP-based Fog architecture for real-time IoT applications that uses a publish-subscribe protocol. A testbed has been developed with low-cost and local resources to verify the suitability of CEP-engines to low-cost computing resources. To assess performance we have analysed the effectiveness and cost of the proposal in terms of latency and resource usage, respectively. Results show that the fog computing architecture reduces event-detection latencies up to 35%, while the available computing resources are being used more efficiently, when compared to a Cloud deployment. Performance evaluation also identifies the communication between the CEP-engine and the final users as the most time consuming component of latency. Moreover, the latency analysis concludes that the time required by CEP-engine is related to the compute resources, but is nonlinear dependent of the number of things connected