| Title |
Path prediction in IoT systems through Markov Chain algorithm |
| ID_Doc |
40740 |
| Authors |
Piccialli, F; Cuomo, S; Giampaolo, F; Casolla, G; di Cola, VS |
| Title |
Path prediction in IoT systems through Markov Chain algorithm |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.future.2020.03.053 |
| Abstract |
In the Data Technology Era, inferring knowledge from data is an ubiquitous and pervasive research topic. Digital Ecosystems based on the Internet of Things (IoT) are generally designed for generating and collecting complex, real-time and (un)structured data. As one of the main component of the Smart City framework, the huge amount of IoT data has to be opportunely processed, also through Machine Learning algorithms in order to discover new knowledge and to improve the quality-of-life of the citizens. In our research work we propose some learning methodologies to analyse and forecast visitors' paths within a cultural and complex space. Starting from data collected in a museum equipped with a non-invasive monitoring IoT system, we show how it is possible to discover and predict useful information on visitors' movements and, finally, we present and discuss some useful insights on their behaviours within a real case-of-study. (C) 2020 Elsevier B.V. All rights reserved. |
| Author Keywords |
Path prediction; Smart City; Machine Learning; Markov Chain |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000536950900018 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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