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Efficient base station connectivity area discovery
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DOI：10.1177/0278364913488634.png)
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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.
Keyword:
communication maintenance
networked robotics
environment sampling
channel estimation
map decomposition
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