Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Wafa Njima"'
Publikováno v:
IEEE Access, Vol 10, Pp 69896-69909 (2022)
Indoor localization techniques based on supervised learning deliver great performance accuracy while maintaining low online complexity. However, such systems require massive amounts of data for offline training, which necessitates costly measurements
Externí odkaz:
https://doaj.org/article/8ccf2fed0eaf4fc289bdf7baef9ad5f5
Publikováno v:
IEEE Access, Vol 9, Pp 98337-98347 (2021)
Several location-based services require accurate location information in indoor environments. Recently, it has been shown that deep neural network (DNN) based received signal strength indicator (RSSI) fingerprints achieve high localization performanc
Externí odkaz:
https://doaj.org/article/717c9e6833bb4b31b9a74ddc68b26ea5
Publikováno v:
IEEE Access, Vol 8, Pp 175741-175752 (2020)
In this paper, we study the problem of euclidean distance matrix (EDM) recovery aiming to tackle the problem of received signal strength indicator sparsity and fluctuations in indoor environments for localization purposes. This problem is addressed u
Externí odkaz:
https://doaj.org/article/78848225d66944b7a2759eaab1eb2ca3
Publikováno v:
Sensors, Vol 19, Iss 14, p 3127 (2019)
Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accur
Externí odkaz:
https://doaj.org/article/6b62cea4bdcf4c54abef7992f05ab24e
Publikováno v:
Applied Sciences, Vol 9, Iss 12, p 2414 (2019)
In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the propo
Externí odkaz:
https://doaj.org/article/9ac63a4f0fbe4e5c9f3c6f93dffe0d66
Publikováno v:
2022 IEEE 12th International Conference on Indoor Positioning and Indoor Navigation (IPIN).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06e290302b4d4a3a1c4b37e0fbd70e24
https://doi.org/10.1109/ipin54987.2022.9917510
https://doi.org/10.1109/ipin54987.2022.9917510
Publikováno v:
2022 9th International Conference on Future Internet of Things and Cloud (FiCloud).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e4a06273592bffbfde6951ae70eb75f
https://doi.org/10.1109/ficloud57274.2022.00046
https://doi.org/10.1109/ficloud57274.2022.00046
Publikováno v:
IEEE Access, Vol 9, Pp 98337-98347 (2021)
IEEE Access
IEEE Access, IEEE, In press, ⟨10.1109/access.2021.3095546⟩
IEEE Access
IEEE Access, IEEE, In press, ⟨10.1109/access.2021.3095546⟩
International audience; Several location-based services require accurate location information in indoor environments. Recently, it has been shown that deep neural network (DNN) based received signal strength indicator (RSSI) fingerprints achieve high
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83d7a5623865b82f54492c0d792e4d23
https://ieeexplore.ieee.org/document/9477623/
https://ieeexplore.ieee.org/document/9477623/
Publikováno v:
Fourth International Balkan Conference on Communications and Networking (BalkanCom 2021)
Fourth International Balkan Conference on Communications and Networking (BalkanCom 2021), Sep 2021, Novi Sad, Serbia
Fourth International Balkan Conference on Communications and Networking (BalkanCom 2021), Sep 2021, Novi Sad, Serbia
International audience; Machine learning techniques allow accurate indoor localization with low online complexity. However, a large amount of collected data samples is needed to properly train a deep neural network (DNN) model used for localization.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f595a9bdf1408d030bc961ed8452dbbb
https://hal.archives-ouvertes.fr/hal-03347456
https://hal.archives-ouvertes.fr/hal-03347456
Publikováno v:
The 2021 IEEE 93rd Vehicular Technology Conference: VTC2021-Spring
The 2021 IEEE 93rd Vehicular Technology Conference: VTC2021-Spring, Apr 2021, Helsinki (on line), Finland
VTC Spring
The 2021 IEEE 93rd Vehicular Technology Conference: VTC2021-Spring, Apr 2021, Helsinki (on line), Finland
VTC Spring
International audience; Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::764334e9eca2815f313436f8e5dce3d5
https://hal.science/hal-03170579
https://hal.science/hal-03170579