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forts and sharing sites [106-108]. For example, the California De-
partment of Transportation has made the data it collects from
highway loop counters publicly available (for free) [107]. Utilizing
these data stores and developing data-driven techniques to tackle
core problems such as map-matching [101], identifying locations of
interest [102] or trac analysis [33] continues to be a promising
area of research.
Communication E ciency: Communication between mobile
objects, as well as between mobile objects and a central database,
remains an expensive operation in terms of power and bandwidth
consumption. Making use of low quality sensors which use less
power, or scheduling the transfer of data in a more effective manner
can both help to reduce these costs [79, 83].
Information Movement: In this chapter, we have focused on
managing the mobility data of physical objects traveling through
space, however, studying the flow of information offers a similar
set of challenges. With the growth of the internet, there has been
an astronomical increase in the availability and sharing of infor-
mation. Only recently have researchers started to ask questions
about how information gets disseminated over time [1, 23, 45, 93].
Additionally, combining the challenges invovled in tracking infor-
mation and moving objects, which are used to transfer informa-
tion, results in yet another set of interesting problems known as
data ferrying [103].
Acknowledgements
This work was partially supported by the National Science Foundation
under grant IIS-0917149.
References
[1] E. Adar and L. Adamic. Tracking information epidemics in
blogspace. In Web Intelligence , pages 207-214. Ieee, 2005.
[2] C. S. Agate and K. J. Sullivan. Road-Constrained Target Tracking
and Identification Using a Particle Filter. In SPIE , number 805,
2003.
[3] M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tuto-
rial on particle filters for online nonlinear/non-Gaussian Bayesian
tracking. IEEE Transactions on Signal Processing , 50(2):174-188,
2002.
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