  The ability of a sensor node to determine its physical location within a
network (Localization) is of fundamental importance in sensor networks.
Interpretating data from sensors will not be possible unless the context of the
data is known; this is most often accomplished by tracking its physical
location. Existing research has focused on localization in static sensor
networks where localization is a one-time (or low frequency) activity. In
contrast, this paper considers localization for mobile sensors: when sensors
are mobile, localization must be invoked periodically to enable the sensors to
track their location. The higher the frequency of localization, the lower the
error introduced because of mobility. However, localization is a costly
operation since it involves both communication and computation. In this paper,
we propose and investigate adaptive and predictive protocols that control the
frequency of localization based on sensor mobility behavior to reduce the
energy requirements for localization while bounding the localization error. We
show that such protocols can significantly reduce the localization energy
without sacrificing accuracy (in fact, improving accuracy for most situations).
Using simulation and analysis we explore the tradeoff between energy efficiency
and localization error due to mobility for several protocols.
