Studying usability of AI in the IoT systems/paradigm through embedding NN techniques into mobile smart service system
The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at...
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| Vydané v: | Computing Ročník 101; číslo 11; s. 1661 - 1685 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article Publikácia |
| Jazyk: | English |
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Vienna
Springer Vienna
01.11.2019
Springer Nature B.V |
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| ISSN: | 0010-485X, 1436-5057 |
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| Abstract | The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system. |
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| AbstractList | The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system.
Peer Reviewed The Internet of Things (IoT) has emerged as a disruptive technology for the current and future of computing and communication. IoT is characterized by a variety of heterogeneous technologies and devices able to be connected to the Internet. Current and future research and development efforts aim at adding artificial intelligence to IoT systems, enabling devices to become smart and thus make autonomous decisions individually or collectively. Additionally, such smart devices have the ability to interact not only with other smart devices but also with humans. Thus, the aim of this paper is to investigate the usability of the artificial intelligence in the IoT paradigm. To achieve the approach, a system called smart-IoT is built based on artificial neural networks, namely, neural networks have been learned by back-propagation algorithm. The system is tested using mobile devices under Android as smart objects. Experiments with neural networks were carried on certain services (such as auto set alarms for a specific event, or estimating the time to return home). These experiments showed the feasibility of embedding neural networks techniques into the IoT system. The approach allows also for easy adding of new services, which in turn means that smart IoT is a modular and full-fledged system. |
| Author | Kryvinska, Natalia Poniszewska-Maranda, Aneta Kaczmarek, Daniel Xhafa, Fatos |
| Author_xml | – sequence: 1 givenname: Aneta surname: Poniszewska-Maranda fullname: Poniszewska-Maranda, Aneta organization: Lodz University of Technology – sequence: 2 givenname: Daniel surname: Kaczmarek fullname: Kaczmarek, Daniel organization: Lodz University of Technology – sequence: 3 givenname: Natalia surname: Kryvinska fullname: Kryvinska, Natalia organization: Comenius University in Bratislava – sequence: 4 givenname: Fatos orcidid: 0000-0001-6569-5497 surname: Xhafa fullname: Xhafa, Fatos email: fatos@cs.upc.edu organization: Universitat Politècnica de Catalunya |
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| CitedBy_id | crossref_primary_10_1016_j_procs_2019_08_009 crossref_primary_10_1016_j_iot_2022_100615 crossref_primary_10_3390_electronics12091993 crossref_primary_10_1155_2022_9445776 crossref_primary_10_1016_j_patrec_2020_05_016 crossref_primary_10_1155_2021_9601078 crossref_primary_10_1109_ACCESS_2020_3045115 crossref_primary_10_3389_frobt_2021_749274 crossref_primary_10_3390_app14051751 crossref_primary_10_3390_s21113800 crossref_primary_10_1016_j_procs_2021_07_010 crossref_primary_10_1108_IJIUS_02_2022_0019 crossref_primary_10_3390_app10249122 crossref_primary_10_3390_app13042299 crossref_primary_10_1016_j_iot_2022_100565 crossref_primary_10_1016_j_iot_2025_101705 crossref_primary_10_1016_j_procs_2021_12_205 crossref_primary_10_1155_2022_8469945 crossref_primary_10_1177_1460458220982640 crossref_primary_10_1016_j_procs_2019_08_018 crossref_primary_10_3390_app13158869 crossref_primary_10_1016_j_procs_2019_11_075 |
| Cites_doi | 10.1007/978-3-642-19157-2 10.1504/IJSSC.2015.070949 10.1504/IJSSC.2015.070945 10.1109/TII.2014.2300753 10.2200/S00429ED1V01Y201207AIM018 10.1109/TII.2013.2277938 10.1007/978-3-642-34952-2 10.1007/BF01934666 10.4249/scholarpedia.1977 10.1007/978-1-4302-5741-7 10.1109/JSEN.2013.2263379 10.1504/IJSSC.2014.060688 10.4236/jcc.2015.35021 10.1504/IJSSC.2012.048898 10.1109/MIS.2013.39 10.1155/2015/431047 10.1016/j.future.2013.01.010 10.1109/ICACTE.2010.5579493 10.1109/FIT.2012.53 10.1109/CCNC.2010.5421661 10.1109/WARTIA.2014.6976227 10.1109/IMIS.2012.109 10.15439/2015F161 10.1109/CSCWD.2014.6846894 10.1109/COMGEO.2013.8 |
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| Contributor | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
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| Keywords | Internet of Things (IoT) Mobile service system Artificial intelligence (AI) Neural networks Mobile device 92B20 Neural networks, artificial life and related topics System design 94C30 Applications of design theory 68Txx Artificial intelligence 68Uxx Computing methodologies and applications 82C32 Neural nets |
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