33 research outputs found

    A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

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    [EN] The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.This research received no external funding. The APC was funded by Research group Information and Communication Technologies against Climate Change (!CTCC) of the Universitat Politecnica de Valencia, Spain.Lemus Zúñiga, LG.; Félix, JM.; Fides Valero, Á.; Benlloch-Dualde, J.; Martinez-Millana, A. (2022). A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards. Sensors. 22(4):1-17. https://doi.org/10.3390/s2204164611722

    Thinger.io: an open source platform for deploying data fusion applications in IoT environments

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    In The Last Two Decades, Data And Information Fusion Has Experienced Significantdevelopment Due Mainly To Advances In Sensor Technology. The Sensors Provide A Continuousflow Of Data About The Environment In Which They Are Deployed, Which Is Received And Processed Tobuild A Dynamic Estimation Of The Situation. With Current Technology, It Is Relatively Simple To Deploya Set Of Sensors In A Specific Geographic Area, In Order To Have Highly Sensorized Spaces. However, Tobe Able To Fusion And Process The Information Coming From The Data Sources Of A Highly Sensorizedspace, It Is Necessary To Solve Certain Problems Inherent To This Type Of Technology. The Challengeis Analogous To What We Can Find In The Field Of The Internet Of Things (Iot). Iot Technology Ischaracterized By Providing The Infrastructure Capacity To Capture, Store, And Process A Huge Amountof Heterogeneous Sensor Data (In Most Cases, From Different Manufacturers), In The Same Way That Itoccurs In Data Fusion Applications. This Work Is Not Simple, Mainly Due To The Fact That There Is Nostandardization Of The Technologies Involved (Especially Within The Communication Protocols Usedby The Connectable Sensors). The Solutions That We Can Find Today Are Proprietary Solutions Thatimply An Important Dependence And A High Cost. The Aim Of This Paper Is To Present A New Opensource Platform With Capabilities For The Collection, Management And Analysis Of A Huge Amount Ofheterogeneous Sensor Data. In Addition, This Platform Allows The Use Of Hardware-Agnostic In A Highlyscalable And Cost-Effective Manner. This Platform Is Called Thinger.Io. One Of The Main Characteristicsof Thinger.Io Is The Ability To Model Sensorized Environments Through A High Level Language Thatallows A Simple And Easy Implementation Of Data Fusion Applications, As We Will Show In This Paper.This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), references TEC2017-88048-C2-2-R, TEC2014-57022-C2-2-RRTC-2016-5595-2, RTC-2016-5191-8 and RTC-2016-5059-8

    A Fair Downlink Scheduling Algorithm for 3GPP LTE Networks

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    Dynamic and heterogeneous wireless sensor network for virtual instrumentation services

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    En el presente Trabajo Fin de Master se ha llevado a cabo el desarrollo de un sistema orientado a la adquisición de información sensorial, a través del uso de redes de sensores inalámbricas (WSN, del inglés Wireless Sensor Networks), de un sistema dinámico cuyo comportamiento se desea caracterizar. Para la gestión de la información de los sensores heterogéneos presentes en la red se han aplicado los conceptos de SOA (Service Oriented Architecture) a dicha red inalámbrica, de manera que cada uno de los sensores presentes en la red se trata como un servicio de medida. La arquitectura propuesta incorpora un mecanismo de "Plug & Play" para la reconfiguración dinámica de la red así como un proceso de composición de servicios que permite la creación de los denominados instrumentos virtuales a través de la asociación de diferentes sensores. Estos instrumentos virtuales agrupan las capacidades de varios sensores heterogeneos de forma que pueden ofrecer al usuario final información de alto nivel complementada con indicios de calidad de dicha información. Para la obtención de este sistema, las tareas que se han llevado a cabo en este trabajo han sido: se han realizado estudios previos de la utilizacion actual de las redes de sensores inalámbricas y de las arquitecturas SOA aplicadas a WSN. Se ha diseñado la arquitectura de la WSN más adecuada para esta sistema así como el mecanismo "Plug & Play" necesario para el descubrimiento de dispositivos y servicios. Se han estudiado y evaluado los criterios más adecuados para la agrupación de sensores para formar el instrumento virtual de forma automática y transparente. Por último, se ha evaluado la validez de la arquitectura propuesta por medio de su aplicación en un caso concreto en el campo de la logística, en particular, en la supervisión de artículos perecederos. Para ello, ha sido necesario diseñar y definir previamente los módulos de sofware necesarios para la implementación del sistema

    Wi-Fi Sensing Algorithms Utilizing Zigbee RF Receiver for Use in Emergency Communications Mesh

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    This thesis examines a low-power Wi-Fi sensing wake-up controller for an emergency communications mesh network; the goal of the research study is to progressively develop a prototype system that could be used in a live environment. Wireless network protocols are reviewed, in addition to a limited view of cluster analysis, in order to introduce relevant receiver concepts crucial to understanding this study. Algorithms for system implementation are developed, and pseudocode, designed to be configurable and platform-independent, is given for each. The system’s design goals are identified, followed by a discussion on approaches and optimizations in order to maximize the system’s usefulness. An example hardware configuration is given, in conjunction with an analysis of benefits and a discussion of drawbacks for several design options. Finally, the prototype is tested according to design goals in order to establish its feasibility. The results demonstrate that the prototype meets the proposed design goals. The implications of these findings include low power optimization for wireless technologies and machine learning techniques for wireless detection

    Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

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    Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure

    Efficient and Secure Key Distribution Protocol for Wireless Sensor Networks

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    Modern wireless sensor networks have adopted the IEEE 802.15.4 standard. This standard defines the first two layers, the physical and medium access control layers; determines the radio wave used for communication; and defines the 128-bit advanced encryption standard (AES-128) for encrypting and validating the transmitted data. However, the standard does not specify how to manage, store, or distribute the encryption keys. Many solutions have been proposed to address this problem, but the majority are impractical in resource-constrained devices such as wireless sensor nodes or cause degradation of other metrics. Therefore, we propose an efficient and secure key distribution protocol that is simple, practical, and feasible to implement on resource-constrained wireless sensor nodes. We conduct simulations and hardware implementations to analyze our work and compare it to existing solutions based on different metrics such as energy consumption, storage overhead, key connectivity, replay attack, man-in-the-middle attack, and resiliency to node capture attack. Our findings show that the proposed protocol is secure and more efficient than other solutions.http://dx.doi.org/10.3390/s1810356

    Location in Ad Hoc Networks

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    Fog computing for sustainable smart cities: a survey

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    The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities
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