Information Technology Reference
In-Depth Information
Finally, geosensor networks and decentralized spatial computing lead to the
vision of
ambient spatial intelligence
(
P7
. Laube et al.
2009
; Duckham and Bennett
2009
). Ambient spatial intelligence emerges from the idea of ubiquitous computing
(Greenfield
2006
) and ambient intelligence (Augusto and Shapiro
2007
). Ambient
spatial intelligence is concerned with embedding the intelligence to monitor geo-
graphical phenomena and respond to spatio-temporal queries directly into our built
and natural environments (Duckham
2012
).
4.2 Movement in Decentralized Spatial Information Systems
Movement is found in various forms in geosensor networks. Geosensor networks
can monitor the movement of entities or mobility and dynamic processes in fields.
Alternatively, the sensing nodes themselves can move and monitor a geographic phe-
nomenon whilst moving. Mobile nodes could be passively suspended in a dynamic
medium (i.e., nodes floating in a water current), carried by a mobile agent (i.e., a
smart phone), or even have their own locomotion capabilities (i.e., autonomous agents
in robotics). Finally, information tokens often move through the network, for instance
when routing information from a source node to a sink. Hence, mobility can be the
primary study subject, a property of the decentralized system, or a property of infor-
mation that is handed around through the system amongst communicating and collab-
orating nodes. On all these levels, mobility presents at the same time challenges and
opportunities. This section investigates what these challenges and opportunities are.
1
Section
2.2
introduced three dimensions discriminating conceptual movement
spaces and the therein embedded movement. Very similar consideration are relevant
when investigating mobility in geosensor networks, except that with the
mobility
mode
an additional fourth dimension should be considered here. The following list
results from integrating the list in Sect.
2.2
with characteristics of mobile objects
listed in Duckham (
2012
, Sect. 6.1.2, p. 173).
•
Constraints to movement
. Do the moving objects roam freely in Euclidean space
or are they constrained by a transportation network?
•
Continuous versus discrete movement space
. How is the movement tracked, is
it a quasi-continuous stream of position fixes captured with coordinates (GPS
positions) or sequences of visited partitions of space (mobile phone antenna cells,
network edges)?
1
This topic is solely about the analysis of movement data. Even though the distinction between data
capture and data analysis gets occasionally a bit blurred in this chapter, there are important aspects
of wireless sensor networks involving movement that are not covered in this chapter. For example,
target tracking, that is in essence the capturing of raw positional data of moving objects, is not
covered. Also information routing, another wireless sensor network classic, contributes to setting
up and maintaining the network infrastructure, but is not considered analysis. Readers interested in
such issues are referred to the introductory text on wireless sensor networks in Zhao and Guibas
(
2004
).
