{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:19:54Z","timestamp":1783059594618,"version":"3.54.6"},"reference-count":24,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2015,4,21]],"date-time":"2015-04-21T00:00:00Z","timestamp":1429574400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.<\/jats:p>","DOI":"10.3390\/s150409277","type":"journal-article","created":{"date-parts":[[2015,4,22]],"date-time":"2015-04-22T04:41:53Z","timestamp":1429677713000},"page":"9277-9304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes"],"prefix":"10.3390","volume":"15","author":[{"given":"Juan","family":"Pardo","sequence":"first","affiliation":[{"name":"ESAI\u2014Embedded Systems and Artificial Intelligence Group, Escuela Superior de Ense\u00f1anzas T\u00e9cnicas, Universidad CEU Cardenal Herrera, C\/San Bartolom\u00e9, 46115 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Zamora-Mart\u00ednez","sequence":"additional","affiliation":[{"name":"ESAI\u2014Embedded Systems and Artificial Intelligence Group, Escuela Superior de Ense\u00f1anzas T\u00e9cnicas, Universidad CEU Cardenal Herrera, C\/San Bartolom\u00e9, 46115 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paloma","family":"Botella-Rocamora","sequence":"additional","affiliation":[{"name":"ESAI\u2014Embedded Systems and Artificial Intelligence Group, Escuela Superior de Ense\u00f1anzas T\u00e9cnicas, Universidad CEU Cardenal Herrera, C\/San Bartolom\u00e9, 46115 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zheng, J., and Jamalipour, A. 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