  Distance estimation is vital for localization and many other applications in
wireless sensor networks (WSNs). Particularly, it is desirable to implement
distance estimation as well as localization without using specific hardware in
low-cost WSNs. As such, both the received signal strength (RSS) based approach
and the connectivity based approach have gained much attention. The RSS based
approach is suitable for estimating short distances, whereas the connectivity
based approach obtains relatively good performance for estimating long
distances. Considering the complementary features of these two approaches, we
propose a fusion method based on the maximum-likelihood estimator (MLE) to
estimate the distance between any pair of neighboring nodes in a WSN through
efficiently fusing the information from the RSS and local connectivity.
Additionally, the method is reported under the practical log-normal shadowing
model, and the associated Cramer-Rao lower bound (CRLB) is also derived for
performance analysis. Both simulations and experiments based on practical
measurements are carried out, and demonstrate that the proposed method
outperforms any single approach and approaches to the CRLB as well.
