Zobrazeno 1 - 10
of 168
pro vyhledávání: '"Dipak Ghosal"'
Publikováno v:
Applied Network Science, Vol 7, Iss 1, Pp 1-16 (2022)
Abstract A road network can be represented as a weighted directed graph with the nodes being the traffic intersections, the edges being the road segments, and the weights being some attribute of a road segment. Such a representation enables researche
Externí odkaz:
https://doaj.org/article/21dc0aeb417a490fbd8179e9c403215a
Publikováno v:
Sensors, Vol 9, Iss 10, Pp 8083-8108 (2009)
Congestion in a Wireless Sensor Network (WSN) can lead to buffer overflow, resource waste and delay or loss of critical information from the sensors. In this paper, we propose the Priority-based Coverage-aware Congestion Control (PCC) algorithm which
Externí odkaz:
https://doaj.org/article/27e6c5a4c8c14b70ab15cbe506d0ff6d
Autor:
Scott G. Burman, Jingya Gao, Gregory B. Pasternack, Nann A. Fangue, Paul Cadrett, Elizabeth Campbell, Dipak Ghosal
Publikováno v:
ACM Transactions on Sensor Networks. 19:1-28
For a Chinook salmon restoration project in the lower Yuba River in California, we designed and deployed a wireless sensor network to monitor river temperatures at micro-habitat scales. The study required that temperatures be measured along a 3 km st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08aca11be6ed27f24fb0856fcebb3152
https://doi.org/10.1145/3542697
https://doi.org/10.1145/3542697
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:6406-6417
There is an increasing trend in the use of wireless communication along with new traffic signal control (TSC) algorithms to leverage and accommodate connected and autonomous vehicles. However, this development has increased the potential for cyber-at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::373866e7c319ded0a16677a3c64b4c4f
https://doi.org/10.1109/tits.2021.3056658
https://doi.org/10.1109/tits.2021.3056658
Autor:
Taran Lynn, Dipak Ghosal
Publikováno v:
2022 IEEE International Conference on Networking, Architecture and Storage (NAS).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f846c75015db5750d107e841e088075f
https://doi.org/10.1109/nas55553.2022.9925440
https://doi.org/10.1109/nas55553.2022.9925440
Publikováno v:
2021 IEEE 10th International Conference on Cloud Networking (CloudNet).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::304078bad084ccf3ee3c8ff27889b6e3
https://doi.org/10.1109/cloudnet53349.2021.9657128
https://doi.org/10.1109/cloudnet53349.2021.9657128
Publikováno v:
ANT/EDI40
Vehicular Ad-hoc Network (VANET) simulators have been gaining widespread attention in recent years and are crucial for conducting vehicular research. The fact that VANET simulators can simulate near real life scenarios to evaluate driver-safety appli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9a0ad33fd8a653a9d4f59c3f96df43b
https://doi.org/10.1016/j.procs.2019.04.012
https://doi.org/10.1016/j.procs.2019.04.012
Autor:
Brian Tierney, Ben Mack-Crane, Dipak Ghosal, Fatma Alali, Mariam Kiran, Rajkumar Kettimuthu, Nathan Hanford, Yatish Kumar, Eric Pouyoul
Publikováno v:
Future Generation Computer Systems. 89:736-745
Many scientific workflows require large data transfers between distributed instrument facilities, storage and computing resources. To ensure that these resources are maximally utilized, R&E networks connecting these resources must ensure that an inhe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d763c4495f6993128c37acafa4d31cbe
https://doi.org/10.1016/j.future.2018.07.030
https://doi.org/10.1016/j.future.2018.07.030
Publikováno v:
VTC Spring
One major source of air pollution is automobile emissions in urban areas. Although hybrid and fully electric vehicles are started to gain popularity, the majority of vehicles are still fuel-based. With the rapid advancement of artificial intelligence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5d31731c37f1efff8dfac03746576d6
https://doi.org/10.1109/vtc2021-spring51267.2021.9448639
https://doi.org/10.1109/vtc2021-spring51267.2021.9448639
Publikováno v:
ITSC
Reinforcement Learning (RL) is being rapidly adopted in many complex environments due to its ability to leverage neural networks to learn good strategies. In traffic signal control (TSC), existing work has focused on off-policy learning (Q-learning)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fe4d633e7cbe823023ab6e3dee8d9b4
https://doi.org/10.1109/itsc45102.2020.9294471
https://doi.org/10.1109/itsc45102.2020.9294471