Persona:
Castillo-Cara, Manuel

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0000-0002-2990-7090
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Castillo-Cara
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Manuel
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2016 - 201962020 - 202518
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Mostrando 1 - 10 de 24
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    Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
    (Taylor & Francis, 2020-06-15) Carrillo Larco, Rodrigo M.; Castillo-Cara, Manuel
    Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
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    A novel deep learning approach using blurring image techniques for Bluetooth-based indoor localisation
    (Elsevier, 2022-10-17) Talla Chumpitaz, Reewos; Orozco Barbosa, Luis; García Castro, Raúl; Castillo-Cara, Manuel
    The growing interest in the use of IoT technologies has generated the development of numerous and diverse applications. Many of the services provided by the applications are based on knowledge of the localisation and profile of the end user. Thus, the present work aims to develop a system for indoor localisation prediction using Bluetooth-based fingerprinting using Convolutional Neural Networks (CNN). For this purpose, a novel technique was developed that simulates the diffusion behaviour of the wireless signal by transforming tidy data into images. For this transformation, we implemented the technique used in painting known as blurring, simulating the diffusion of the signal spectrum. Our proposal also includes the use and a comparative analysis of two dimensional reduction algorithms, PCA and t -SNE. Finally, an evolutionary algorithm was implemented to configure and optimise our solution with the combination of different transmission power levels. The results reported in this work present an accuracy of close to 94%, which clearly shows the great potential of this novel technique in the development of more accurate indoor localisation systems .
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    From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability
    (ELSEVIER, 2024-05-10) Huaranga, Edgar; González Gerpe, Salvador; Castillo-Cara, Manuel; Cimmino, Andrea; García Castro, Raúl; https://orcid.org/0000-0002-8087-0940; https://orcid.org/0000-0003-1550-0430; https://orcid.org/0000-0002-1823-4484; https://orcid.org/0000-0002-0421-452X
    In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.
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    BeeGOns!: A Wireless Sensor Node for Fog Computing in Smart City Applications
    (Institute of Electrical and Electronics Engineers, 2024-01) Vera Panez, Michael; Cuadros Claro, Kewin; Orozco Barbosa, Luis; Castillo-Cara, Manuel
    The widespread deployment of sensors interconnected by wireless links and the management and exploitation of the data collected have given rise to the Internet of Things (IoT) concept. In this article, we undertake the design and implementation of a wireless multisensor platform following the fog computing paradigm. Our main contributions are the integration of various off-the-shelf sensors smartly packaged into an air-flow module and the evaluation of the communications services offered implemented on top of two low-power radio communications technologies. Our study is complemented by evaluating the communi- cations services over a wired link. Our results show the superiority of LoRaWAN over ZigBee in terms of power consumption despite its slightly higher computational requirements and an estimation of the gap between the resource usage of the wired link and the two wireless radio technologies.
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    FROG: A Robust and Green Wireless Sensor Node for Fog Computing Platforms
    (Hindawi, 2018-04-12) Huaranga Junco, Edgar; Quispe Montesinos, Milner; Orozco Barbosa, Luis; Arias Antúnez, Enrique; Castillo-Cara, Manuel
    Over the past few years, we have witnessed the widespread deployment of wireless sensor networks and distributed data management facilities: two main building blocks of the Internet of things (IoT) technology. Due to the spectacular increase on the demand for novel information services, the IoT-based infrastructures are more and more characterized by their geographical sparsity and increasing demands giving rise to the need of moving from a cloud to a fog model: a novel deployment paradigm characterized by the provisioning of elastic resources geographically located as close as possible to the end user. Despite the large number of wireless sensor networks already available in the market, there are still many issues to be addressed on the design and deployment of robust network platforms capable of meeting the demand and quality of fog-based systems. In this paper, we undertake the design and development of a wireless sensor node for fog computing platforms addressing two of the main issues towards the development and deployment of robust communication services, namely, energy consumption and network resilience provisioning. Our design is guided by examining the relevant macroarchitecture features and operational constraints to be faced by the network platform. We based our solution on the integration of network hardware platforms already available on the market supplemented by smart power management and network resilience mechanisms
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    An experimental study of fog and cloud computing in CEP-based Real-Time IoT applications
    (SpringerOpen, 2021-06-07) Mondragón Ruiz, Giovanny; Tenorio Trigoso, Alonso; Caminero, Blanca; Carrión, Carmen; Castillo-Cara, Manuel
    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.
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    Ray: Smart indoor/outdoor routes for the blind using Bluetooth 4.0 BLE
    (Elsevier, 2016) Huaranga Junco, Edgar; Mondragón Ruiz, Giovanny; Salazar, Andree; Orozco Barbosa, Luis; Arias Antúnez, Enrique; Castillo-Cara, Manuel
    This work describes the implementation of a cost-effective assistive mobile application aiming to improve the quality of life of visually impaired people. Taking into account the architectural adaptations being done in many cities around the world, such as tactile sidewalks, the mobile application provides support to guide the visually impaired through outdoor/indoor spaces making use of various navigation technologies. The actual development of the application presented herein has been done taking into account that the safety of the end user will very much depend on the robustness, accuracy and timeliness of the information to be provided. Furthermore, we have based our development on open source code: a must for applications to be adapted to the cultural and social characteristics of urban areas across the world.
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    An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study
    (['Oxford University Press', 'BCS, The Chartered Institute for IT'], 2021-10-06) Tenorio Trigoso, Alonso; Mondragón Ruiz, Giovanny; Carrión, Carmen; Caminero, Blanca; Castillo-Cara, Manuel
    Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.
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    On the relevance of the metadata used in the semantic segmentation of indoor image spaces
    (Elsevier, 2021) Vasquez Espinoza, Luis; Orozco Barbosa, Luis; Castillo-Cara, Manuel
    The study of artificial learning processes in the area of computer vision context has mainly focused on achieving a fixed output target rather than on identifying the underlying processes as a means to develop solutions capable of performing as good as or better than the human brain. This work reviews the well-known segmentation efforts in computer vision. However, our primary focus is on the quantitative evaluation of the amount of contextual information provided to the neural network. In particular, the information used to mimic the tacit information that a human is capable of using, like a sense of unambiguous order and the capability of improving its estimation by complementing already learned information. Our results show that, after a set of pre and post-processing methods applied to both the training data and the neural network architecture, the predictions made were drastically closer to the expected output in comparison to the cases where no contextual additions were provided. Our results provide evidence that learning systems strongly rely on contextual information for the identification task process.
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    Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries
    (eLife Sciences Publications, 2022-01-25) Guzman Vilca, Wilmer Cristobal; Carrillo Larco, Rodrigo M.; Castillo-Cara, Manuel
    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.