| Titre : | Big Data and Internet of Things |
| Auteurs : | BOUKHALFA Siham, Auteur ; AMINE Abdelmalek, Directeur de thèse |
| Type de document : | texte imprimé |
| Editeur : | [S.l.] : Université Dr Moulay Tahar de Saida Faculté de technologie Département d’informatique, 2021/2022 |
| Format : | 167 p |
| Accompagnement : | CD |
| Langues: | Anglais |
| Langues originales: | Anglais |
| Mots-clés: | Big Data ; Internet of Things ; Data mining ; metaheuristic ; RFID ; Classification ; Human activity recognition ; Batteryless wearable sensor ; cockroach ; older people ; Border Surveillance ; Security ; Border Patrol ; Video Surveillance ; Security Alarm ; Remote Surveillance System. |
| Résumé : |
Abstract
Nowadays, in a world covered by networks, there are more smart devices than peoples, since a person owns different smart devices in different forms. These devices, which interconnect and exchange a very large flow of data, perform several functions including monitoring, data collection, and data evaluation. In this thesis, we will focus on this new trend of interconnected objects used to improve the daily life of individuals. For this, the exploitation of the Internet of Things in the field of monitoring and control is a recent research axis that helps human beings to ensure this task based on the data captured by the intelligent devices that will be subsequently analyzed and processed by different methods. It is in this context that we orient our research on the concept of linking objects to the Internet, known today as the Internet of Things. Our work is articulated around two issues, physical activity and fall prevention in the elderly and the security of international borders. In our first work, we proposed an approach based on metaheuristics for real-time security and boundary protection. This technique is inspired by the behavior of natural cockroaches and the phenomenon of seeking the most attractive and secure place to hide. In our second work, we used classification algorithms to combat the risk of falls in the elderly and enable these individuals to continue their lives in the best possible condition. We examine the applicability of three data mining algorithms for real-world IoT datasets. These include K-nn, Naive Bayes, and Decision Tree. The main contribution of this work is the analysis of the efficiency of three data mining algorithms. All the experiments carried out and the results obtained have shown the benefits derived from the use of our system. |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| BUC-D 000509 | BUC-D 000509 | CD | Bibliothèque PMB Services | Doctorat | Consultation sur place Exclu du prêt |
Documents numériques (1)
Big Data and Internet of Things Adobe Acrobat PDF |
