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This book presents systematic methods for estimating environmental
fields using multiple mobile sensors. Monitoring environmental
fields is a complex task and is of great use in many areas, such as
for building models of natural phenomenon, e.g. agriculture
monitoring, such as monitoring soil temperature to manage frost,
wind, water, disease, and pests. Ocean, river and lake monitoring
of environmental phenomena, such as salinity in lakes, tracking
water temperature, particulate densities and pollutants responsible
for sustaining marine colonies, or coral cover of oceanic reefs.
Meteorology monitoring, such as tracking of storms, gas plumes, and
air quality; forest monitoring for tracking humidity in forests,
and prediction and decision making during forest fire fighting,
etc. Sampling is a broad methodology for gathering statistical
information about a phenomenon. The capabilities and distributed
nature of wireless sensor networks provide an attractive sampling
approach for estimation of spatiotemporally distributed
environmental fields. This is adaptive sampling, where the strategy
for 'where to sample next' evolves temporally with past
measurements. Thus the sensor network physically adapts with past
measurements to enable sampling at locations that give maximal
information about the field being estimated. This book presents
adaptive sampling strategies with multiple, heterogeneous and
mobile sensors. Sensors of this kind present several complexities,
some of which like deadlocks and localisation issues are also
addressed here.
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