Leveraging online learning for CSS in frugal IoT network
We present a novel method for centralized collaborative spectrum sensing for
IoT network leveraging cognitive radio network. Based on an online learning
framework, we propose an algorithm to efficiently combine the individual
sensing results based on the past performance of each detector. Additionally,
we show how to utilize the learned normalized weights as a proxy metric of
detection accuracy and selectively enable the sensing at detectors. Our results
show improved performance in terms of inter-user collision and misdetection.
Further, by selectively enabling some of the devices in the network, we propose
a strategy to extend the field life of devices without compromising on
detection accuracy.
NurtureToken New!
Token crowdsale for this paper ends in
Authors
Are you an author of this paper? Check the Twitter handle we have for you is correct.
| Nancy Nayak | (add twitter) | |
| Vishnu Raj | (add twitter) | |
| Sheetal Kalyani | (add twitter) |
Ask The Authors
Ask the authors of this paper a question or leave a comment.
Read it. Rate it.
Github
User:
None
(add)
Repo:
None
(add)
Stargazers:
0
Forks:
0
Open Issues:
0
Network:
0
Subscribers:
0
Language:
None
Other
Sample Sizes (N=):
Inserted:
Words Total:
Words Unique:
Source:
Abstract:
Tweets
mathITbot:
Nancy Nayak, Vishnu Raj, Sheetal Kalyani : Leveraging online learning for CSS in frugal IoT network https://t.co/eL7IKO9PDe https://t.co/v5H1o5TOxl
Images
Related
