A Hidden Environment Model for Constructing Indoor Radio Maps

Zhe Xiang*  Hangjin Zhang^  Jian Huang*  Song Song*  Kevin C. Almeroth^

*IBM China Research Lab            ^Department of Computer Science
Beijing, China                      University of California--Santa Barbara
{xiangzhe,jianh,soong}@cn.ibm.com   {hangjin,almeroth}@cs.ucsb.edu

Constructing indoor radio maps plays an important role in many
services and applications such as wireless base station planning.
In this paper, we propose a hybrid approach to construct indoor
radio maps by developing a novel indoor propagation model, called
the Hidden Environment Model, and combining it with a smaller
number of on-site measurements. As part of this model, we
introduce an Environment Factor Matrix (EFM). The EFM represents a
model of the environmental features that affect radio attenuation.
We also develop a Lazy Sampling Algorithm to help generate the
EFM. The goal of the Lazy Sampling Algorithm is to balance the
number of measurements that need to be taken with our model's
accuracy, i.e., to minimize the measurement workload while
maintaining satisfactory accuracy. We evaluate our model by
comparing the radio maps calculated from the model to a radio map
obtained by exhaustive measurements. The results show that our
Hidden Environment Model achieves good accuracy.