Evidence Filtering
Processing Multi-Modality Sensor Data in Wireless Sensor Networks
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Wireless sensor networks attempt to revolutionize the way we interact with the physical world by networking multitudes of small sensors attached to wireless radios. This book presents a novel method named Evidence Filtering for processing multi-modality sensor data in such networks. Based on Dempster-Shafer evidence theory, it allows one to directly process temporally and spatially distributed multi-modality sensor data and infer on the `frequency' characteristics of events of interest. Second part of the book covers distributed methods to implement spatio-temporal filtering applications in gr...
Wireless sensor networks attempt to revolutionize
the way we interact with the physical world by
networking multitudes of small sensors attached to
wireless radios. This book presents a novel method
named Evidence Filtering for processing multi-
modality sensor data in such networks. Based on
Dempster-Shafer evidence theory, it allows one to
directly process temporally and spatially
distributed multi-modality sensor data and infer on
the `frequency' characteristics of events of
interest. Second part of the book covers distributed
methods to implement spatio-temporal filtering
applications in grid sensor networks. Based on the
Fornasini-Marchesini local state space model, these
distributed methods enable local actuation in
response to local events. The techniques discussed
here provide a highly effective toolset for one to
implement spatio-temporal filtering applications in
multi-modality grid sensor networks. This book also
covers past research in related areas and is
intended for students, engineers, scientists, and
researchers in the related disciplines.
the way we interact with the physical world by
networking multitudes of small sensors attached to
wireless radios. This book presents a novel method
named Evidence Filtering for processing multi-
modality sensor data in such networks. Based on
Dempster-Shafer evidence theory, it allows one to
directly process temporally and spatially
distributed multi-modality sensor data and infer on
the `frequency' characteristics of events of
interest. Second part of the book covers distributed
methods to implement spatio-temporal filtering
applications in grid sensor networks. Based on the
Fornasini-Marchesini local state space model, these
distributed methods enable local actuation in
response to local events. The techniques discussed
here provide a highly effective toolset for one to
implement spatio-temporal filtering applications in
multi-modality grid sensor networks. This book also
covers past research in related areas and is
intended for students, engineers, scientists, and
researchers in the related disciplines.
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