Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Golshan Famitafreshi"'
Publikováno v:
IEEE Access, Vol 11, Pp 126705-126723 (2023)
The crisis of energy supplies has led to the need for sustainability in technology, especially in the Internet of Things (IoT) paradigm. One solution is the integration of passive technologies like Energy Harvesting (EH) into IoT systems, which reduc
Externí odkaz:
https://doaj.org/article/e7881e1d3c1f46bea72fd43397db70be
Publikováno v:
Sensors, Vol 22, Iss 10, p 3831 (2022)
The adverse impacts of using conventional batteries in the Internet of Things (IoT) devices, such as cost-effective maintenance, numerous battery replacements, and environmental hazards, have led to an interest in integrating energy harvesting techno
Externí odkaz:
https://doaj.org/article/906bcba3b66c43d88949f016c0014b09
Publikováno v:
Sensors, Vol 21, Iss 9, p 3097 (2021)
The Internet of Things (IoT) is revolutionizing technology in a wide variety of areas, from smart healthcare to smart transportation. Due to the increasing trend in the number of IoT devices and their different levels of energy requirements, one of t
Externí odkaz:
https://doaj.org/article/0b8c0f5e390f4e04b77dcc8852c4267b
Autor:
Cristina Cano, Golshan Famitafreshi
Publikováno v:
Machine Learning for Networking ISBN: 9783030457778
MLN
O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)
Lecture Notes in Computer Science
2nd International Conference on Machine Learning for Networking (MLN)
2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.85-98, ⟨10.1007/978-3-030-45778-5_7⟩
MLN
O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)
Lecture Notes in Computer Science
2nd International Conference on Machine Learning for Networking (MLN)
2nd International Conference on Machine Learning for Networking (MLN), Dec 2019, Paris, France. pp.85-98, ⟨10.1007/978-3-030-45778-5_7⟩
In this paper, we revisit proportional fair channel allocation in IEEE 802.11 networks. Traditional approaches are either based on the explicit solution of the optimization problem or use iterative solvers to converge to the optimum. Instead, we prop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08ce338043a7fc2727fee6de42cb52cb
https://doi.org/10.1007/978-3-030-45778-5_7
https://doi.org/10.1007/978-3-030-45778-5_7
