Imputation method based on recurrent neural networks for the internet of things

El Internet de las Cosas (IoT) es un nuevo paradigma tecnológico, en el cual sensores y objetos comunes, como electrodomésticos, se conectan e interactúan a través de la Internet -- Este nuevo paradigma, de la mano con técnicas de Inteligencia Artificial (AI) y técnicas modernas para el análisis de datos, hace posible el desarrollo de productos y servicios inteligentes; lo que promete revolucionar la industria y la forma de vida de los humanos -- Sin embargo, existen muchos problemas que deben ser solucionados para poder contar con productos y servicios confiables basados en el IoT -- Dentro de estos problemas, el problema de los datos faltantes impide la correcta aplicación de modernas técnicas the AI y análisis de datos en aplicaciones basadas en el IoT -- Este escrito presenta un análisis del problema de los datos faltantes en el contexto del IoT, así como métodos de imputación actuales propuestos a solucionar este problema -- Del análisis se concluye que las soluciones actuales tienen grandes limitaciones si se considera lo amplio del contexto de las applicaciones basadas en IoT -- El análisis también expone que no hay un marco experimental en común que pueda ser usado por los diferentes autores, y que los experimentos encontrados carecen de reproducibilidad y no consideran adecuadamente como el problema de los datos faltantes se presenta en el contexto en particular del IoT -- De acuerdo con lo anterior, este escrito presenta dos propuestas principales: i) un marco experimental que permite evaluar adecuadamente los métodos de imputación que se pretendan evaluar en este contexto; y ii) un método de imputación que es lo suficientemente general como para ser aplicado en los diferentes escenarios del IoT -- El método de imputación se basa en el uso de Redes Neuronales Recurrentes, una familia de métodos de aprendizaje supervisado que ha mostrado un buen desempeño explotando patrones de datos sequenciales y relaciones intrínsecas entre variablesThe Internet of Things (IoT) refers to the new technological paradigm in which sensors and common objects, like household appliances, connect to and interact through the Internet -- This new paradigm, and the use of Artificial Intelligence (AI) and modern data analysis techniques, powers the development of smart products and services; which promise to revolutionize the industry and humans way of living -- Nonetheless, there are plenty of issues that need to be solved in order to have reliable products and services based on the IoT -- Among others, the problem of missing data posses great threats to the applicability of AI and data analysis to IoT applications -- This manuscript shows an analysis of the missing data problem in the context of the IoT, as well as the current imputation methods proposed to solve the problem -- This analysis leads to the conclusion that current solutions are very limited when considering how broad the context of IoT applications may be -- Additionally, this manuscript exposes that there is not a common experimental set up in which the authors have tested their proposed imputation methods; moreover, the experiments found in the literature, lack reproducibility and do not carefully consider how the missing data problem may present in the IoT -- Consequently, the reader will find two proposals in this manuscript: i) an experimental set up to properly test imputation methods in the context of the IoT; and ii) an imputation method that is general enough as to be applied to several IoT scenarios -- The latter is based on Recurrent Neural Networks, a family of supervised learning methods which have excel at exploiting patterns in sequential data and intrinsic association between the variables of dat

Efficient spatial data recovery scheme for cyber-physical system

Feedback data loss can severely degrade the overall system performance and as well as it can affect the control and computation of the Cyber-physical System (CPS). Therefore, incomplete feedback makes a great challenge in any uncertain condition to maintain the real-time control of the CPS. In this paper, we propose a data recovery scheme, called Efficient Spatial Data Recovery (ESDR) scheme for CPS to minimize the error estimation and maximize the accuracy of the scheme. In this scheme, we also present an algorithm with Pearson Correlation Coefficient (PCC) to efficiently solve the long consecutive missing data. Numerical results reveal that the proposed ESDR scheme outperforms both WP and STI algorithms regardless of the increment percentage of missing data in terms of the root mean square error, mean absolute error and integral of absolute error