{"id":25488,"date":"2022-10-25T10:23:47","date_gmt":"2022-10-25T08:23:47","guid":{"rendered":"https:\/\/networks.imdea.org\/?p=25488"},"modified":"2022-10-25T16:46:08","modified_gmt":"2022-10-25T14:46:08","slug":"imdea-networks-researchers-create-an-algorithm-that-maximizes-iot-sensor-inference-accuracy-using-edge-computing","status":"publish","type":"post","link":"https:\/\/networks.imdea.org\/es\/investigadores-de-imdea-networks-crean-un-algoritmo-que-maximiza-la-precision-de-la-inferencia-de-sensores-de-iot-usando-la-computacion-de-borde\/","title":{"rendered":"Investigadores de IMDEA Networks crean un algoritmo que maximiza la precisi\u00f3n de la inferencia de sensores de IoT usando la computaci\u00f3n de borde"},"content":{"rendered":"<p>Estamos en una era fascinante en la que incluso dispositivos con pocos recursos, como los sensores del Internet de las Cosas (IoT), pueden utilizar algoritmos de aprendizaje profundo para abordar problemas complejos como la clasificaci\u00f3n de im\u00e1genes o el procesamiento del lenguaje natural (rama de la inteligencia artificial que se ocupa de\u00a0dotar a los ordenadores de la capacidad de entender lenguaje hablado y escrito del mismo modo que los seres humanos). Sin embargo, <strong>el aprendizaje profundo en los sensores de IoT puede no ser capaz de garantizar los requisitos de calidad de los servicios<\/strong>, como la precisi\u00f3n de la inferencia y la latencia. Con el crecimiento exponencial de los datos recogidos por miles de millones de dispositivos IoT se ha planteado la necesidad de cambiar a un modelo distribuido en el que parte de la computaci\u00f3n se produce en el borde de la red (\u201c<strong>Edge computing<\/strong>\u201d), m\u00e1s cerca de donde se crean los datos, en lugar de enviarlos a la nube para su procesamiento y almacenamiento.<\/p>\n<p>Los investigadores de IMDEA Networks <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/andrea-fresa\/\" target=\"_blank\" rel=\"noopener\">Andrea Fresa<\/a> (PhD Student) y <a href=\"https:\/\/networks.imdea.org\/es\/equipo\/equipo-imdea-networks\/personas\/jaya-prakash-varma-champati\/\" target=\"_blank\" rel=\"noopener\">Jaya Prakash Champati<\/a> (Research Assistant Professor) han realizado un estudio en el que <strong>han presentado el algoritmo AMR\u00b2<\/strong>, que hace uso de la infraestructura de computaci\u00f3n de borde (procesamiento, an\u00e1lisis y almacenamiento de los datos m\u00e1s cerca de donde se generan para permitir an\u00e1lisis y respuestas m\u00e1s r\u00e1pidos, casi en tiempo real) para aumentar la precisi\u00f3n de la inferencia de los sensores de IoT mientras se observan las limitaciones de latencia y han demostrado que el problema queda resuelto. El paper \u201c<a href=\"https:\/\/dspace.networks.imdea.org\/handle\/20.500.12761\/1613\" target=\"_blank\" rel=\"noopener\">An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System<\/a>\u201d se ha publicado esta semana en la conferencia MSWiM.<\/p>\n<p>Para entender qu\u00e9 es la inferencia hay que explicar primero que el <em>machine learning<\/em> (ML) o aprendizaje autom\u00e1tico trabaja en dos fases principalmente. La primera hace referencia al entrenamiento, cuando el desarrollador alimenta su modelo con un conjunto de datos curados para que pueda \u201caprender\u201d todo lo que necesita sobre el tipo de datos que va a analizar. La siguiente fase es la de <strong>inferencia<\/strong>: el modelo puede hacer predicciones basadas en datos reales para producir resultados procesables.<\/p>\n<p>En su publicaci\u00f3n, los investigadores han llegado a la conclusi\u00f3n de que la precisi\u00f3n de la inferencia aument\u00f3 hasta un 40% si se compara el algoritmo AMR\u00b2 con las t\u00e9cnicas de programaci\u00f3n b\u00e1sicas. Asimismo, han descubierto que para soportar adecuadamente los algoritmos de aprendizaje autom\u00e1tico en el borde la red es esencial un algoritmo de programaci\u00f3n eficiente.<\/p>\n<p>\u201c<strong>Los resultados de nuestro estudio podr\u00edan ser muy \u00fatiles para las aplicaciones de aprendizaje autom\u00e1tico<\/strong> que necesitan una inferencia r\u00e1pida y precisa en los dispositivos finales. Pensemos en un servicio como Google Photos, por ejemplo, que categoriza los elementos de las im\u00e1genes. Podemos garantizar el retardo de ejecuci\u00f3n utilizando el algoritmo AMR\u00b2, lo que puede ser muy provechoso para un desarrollador que puede utilizarlo en el dise\u00f1o para garantizar que los retrasos no sean visibles para el usuario\u201d, explica Andrea Fresa.<\/p>\n<p>El principal obst\u00e1culo que han encontrado al realizar este estudio es demostrar el rendimiento te\u00f3rico del algoritmo AMR\u00b2 y validarlo utilizando un <strong>banco de pruebas experimental que consiste en una Raspberry Pi y un servidor conectados a trav\u00e9s de una LAN<\/strong>. \u201cPara demostrar los l\u00edmites de rendimiento de AMR\u00b2, empleamos ideas fundamentales de la programaci\u00f3n lineal e instrumentos de la investigaci\u00f3n operativa\u201d, subraya Fresa.<\/p>\n<p>No obstante, con este trabajo los investigadores de IMDEA Networks han sentado las bases para futuras investigaciones que ayudar\u00e1n a hacer posible la ejecuci\u00f3n de aplicaciones de aprendizaje autom\u00e1tico en el borde de la red de forma r\u00e1pida y precisa.<\/p>\n<div style=\"min-height: 30px;\"><a target=\"_blank\" rel=\"noindex,nofollow\" href=\"https:\/\/networks.imdea.org\/es\/investigadores-de-imdea-networks-crean-un-algoritmo-que-maximiza-la-precision-de-la-inferencia-de-sensores-de-iot-usando-la-computacion-de-borde\/?format=pdf\" title=\"Download PDF\"><img decoding=\"async\" style=\"float: left;max-width: 50px;\" alt=\"Download PDF\" src=\"https:\/\/networks.imdea.org\/wp-content\/plugins\/wp-advanced-pdf\/asset\/images\/pdf.png\"><\/a><\/div>","protected":false},"excerpt":{"rendered":"Estamos en una era fascinante en la que incluso dispositivos con pocos recursos, como los sensores del Internet de las Cosas (IoT), pueden utilizar algoritmos de aprendizaje profundo para abordar problemas complejos como la clasificaci\u00f3n de im\u00e1genes o el procesamiento del lenguaje natural (rama de la inteligencia artificial que se ocupa de\u00a0dotar a los ordenadores&#8230;","protected":false},"author":145,"featured_media":25491,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1,1187],"tags":[492,497,491,464,465,470,494,466],"class_list":["post-25488","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categoria","category-press-release","tag-artificial-intelligence-ai","tag-cloud-computing","tag-fog-and-edge-computing","tag-imdea-networks","tag-internet","tag-internet-of-things-iot","tag-machine-learning","tag-network-protocols-and-algorithms"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - 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