Efficient base station connectivity area discovery. (October 2013)
- Record Type:
- Journal Article
- Title:
- Efficient base station connectivity area discovery. (October 2013)
- Main Title:
- Efficient base station connectivity area discovery
- Authors:
- Twigg, Jeffrey N.
Fink, Jonathan R.
Yu, Paul L.
Sadler, Brian M. - Abstract:
- Many applications of autonomy are significantly complicated by the need for wireless networking, with challenges including scalability and robustness. Radio accomplishes this in a complex environment, but suffers from rapid signal strength variation and attenuation typically much worse than free space loss. In this paper, we propose and test algorithms to autonomously discover the connectivity area for a base station in an unknown environment using an average of received signal strength (RSS) values and a RSS threshold to delineate the goodness of the channel. We combine region decomposition and RSS sampling to cast the problem as an efficient graph search. The nominal RSS in a sampling region is obtained by averaging local RSS samples to reduce the small-scale fading variation. The RSS gradient is exploited during exploration to develop an efficient approach for discovery of the base station connectivity boundary in an unknown environment. Indoor and outdoor experiments demonstrate the proposed techniques. The results can be used for sensing and collaborative autonomy, building base station coverage maps in unknown environments, and facilitating multi-hop relaying to a base station.
- Is Part Of:
- International journal of robotics research. Volume 32:Number 12(2013)
- Journal:
- International journal of robotics research
- Issue:
- Volume 32:Number 12(2013)
- Issue Display:
- Volume 32, Issue 12 (2013)
- Year:
- 2013
- Volume:
- 32
- Issue:
- 12
- Issue Sort Value:
- 2013-0032-0012-0000
- Page Start:
- 1398
- Page End:
- 1410
- Publication Date:
- 2013-10
- Subjects:
- communication maintenance -- networked robotics -- environment sampling -- channel estimation -- map decomposition
Robots -- Periodicals
Robots, Industrial -- Periodicals
629.89205 - Journal URLs:
- http://ijr.sagepub.com/ ↗
http://www.uk.sagepub.com/home.nav ↗ - DOI:
- 10.1177/0278364913488634 ↗
- Languages:
- English
- ISSNs:
- 0278-3649
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 24785.xml