{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:41:32Z","timestamp":1779201692954,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,25]],"date-time":"2018-05-25T00:00:00Z","timestamp":1527206400000},"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>IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns\/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor\u2019s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.<\/jats:p>","DOI":"10.3390\/s18061711","type":"journal-article","created":{"date-parts":[[2018,5,28]],"date-time":"2018-05-28T03:54:21Z","timestamp":1527479661000},"page":"1711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Real-Time Analysis of a Sensor\u2019s Data for Automated Decision Making in an IoT-Based Smart Home"],"prefix":"10.3390","volume":"18","author":[{"given":"Nida Saddaf","family":"Khan","sequence":"first","affiliation":[{"name":"Telecommunication Research Lab, Department of Computer Science, Institute of Business Administration, Garden\/Kayani Shaheed Road, Karachi 74400, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2938-2789","authenticated-orcid":false,"given":"Sayeed","family":"Ghani","sequence":"additional","affiliation":[{"name":"Telecommunication Research Lab, Department of Computer Science, Institute of Business Administration, Garden\/Kayani Shaheed Road, Karachi 74400, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sajjad","family":"Haider","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Lab, Department of Computer Science, Institute of Business Administration, Garden\/Kayani Shaheed Road, Karachi 74400, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MCE.2016.2640718","article-title":"Create Your Own Internet of Things: A survey of IoT platforms","volume":"6","author":"Singh","year":"2017","journal-title":"IEEE Consum. 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