TY - JOUR
T1 - Accurate 3-D Localization of Selected Smart Objects in Optical Internet of Underwater Things
AU - Saeed, Nasir
AU - Alouini, Mohamed Slim
AU - Al-Naffouri, Tareq Y.
N1 - Funding Information:
Manuscript received September 15, 2019; accepted October 5, 2019. Date of publication October 8, 2019; date of current version February 11, 2020. This work was supported by the Office of Sponsored Research at King Abdullah University of Science and Technology. (Corresponding author: Nasir Saeed.) The authors are with the Department of Electrical Engineering, Computer Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia (e-mail: [email protected]). Digital Object Identifier 10.1109/JIOT.2019.2946270
Publisher Copyright:
© 2014 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor's location have been proposed. However, the optimization of the anchors' location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.
AB - Localization is a fundamental task for the optical Internet of Underwater Things (O-IoUT) to enable various applications, such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for O-IoUT greatly relies on the location of the anchors. Therefore, recently, the localization techniques for O-IoUT which optimize the anchor's location have been proposed. However, the optimization of the anchors' location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this article, we propose a 3-D accurate localization technique by optimizing the anchor's location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable sensors. The numerical results show that the proposed technique of optimizing anchor's location for a set of selected sensors provides a better location accuracy.
KW - Anchor's location
KW - data tagging
KW - localization
KW - optical Internet of Underwater Things (O-IoUT)
KW - routing
UR - http://www.scopus.com/inward/record.url?scp=85079793294&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2019.2946270
DO - 10.1109/JIOT.2019.2946270
M3 - Article
AN - SCOPUS:85079793294
SN - 2327-4662
VL - 7
SP - 937
EP - 947
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
M1 - 8862956
ER -