  For data sets retrieved from wireless sensors to be insightful, it is often
of paramount importance that the data be accurate and also location stamped.
This paper describes a maximum-likelihood based multihop localization algorithm
called kHopLoc for use in wireless sensor networks that is strong in both
isotropic and anisotropic network deployment regions. During an initial
training phase, a Monte Carlo simulation is utilized to produce multihop
connection density functions. Then, sensor node locations are estimated by
maximizing local likelihood functions of the hop counts to anchor nodes.
Compared to other multihop localization algorithms, the proposed kHopLoc
algorithm achieves higher accuracy in varying network configurations and
connection link-models.
