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| Overview | One of the
largest remaining obstacles to the widespread use of sensor
networks is the interpretation of the sensor data; users must be
able to query for high-level facts about the world, not just raw
ADC readings. We designed a framework called Semantic Streams
which allows users to pose declarative queries for semantic
values, such as "the speeds of vehicles near the entrance of the
parking garage." The system combines logical inference with AI
planning techniques to compose a sequence of inference
units, which are stream operators that perform a minimal
amount of sensor fusion or interpretation on incoming event
streams and incorporate newly inferred semantic values into
outgoing event streams. Both the sensor network and the
inference units are logically described in Prolog and the system
can reason about which sensors in space to use and whether the
inference units are being used in an appropriate world
context. Typically, multiple combinations of sensors and
inference units can answer the same query, so users can also
declare QoS constraints in CLP(R) notation to choose between
logically equivalent inference graphs, for example: "the
confidence of the speed estimates should be greater than 90%."
or ``I want to minimize the total number of radio messages.''
This work is patent pending. | |||
| Publications |
Kamin Whitehouse, Jie Liu, Feng Zhao."Semantic Streams: a Framework for Composable Inference over Sensor Data". The Third European Workshop on Wireless Sensor Networks (EWSN), Springer-Verlag Lecture Notes in Computer Science. Zurich, Switzerland. February 13-15, 2006. (ppt) "Automatic Programming with Semantic Streams", Kamin Whitehouse, Feng Zhao, Jie Liu. The 3rd ACM Conference on Embedded Networked Sensor Systems (SenSys '05), November 2-4 2005. |
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Kamin Whitehouse Computer Science Department The University of Virginia 217 Olsson Hall Charlottesville, Virginia 94720 |