Encoding Semantic Awareness in Resource-Constrained Devices
IEEE Intelligent Systems
IEEE Internet Computing
μOR --- A Micro OWL DL Reasoner for Ambient Intelligent Devices
GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
A Functional Ontology of Observation and Measurement
GeoS '09 Proceedings of the 3rd International Conference on GeoSpatial Semantics
Constructing Bodies and their Qualities from Observations
Proceedings of the 2010 conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010)
A Process-Centric Ontological Approach for Integrating Geo-Sensor Data
Proceedings of the 2010 conference on Formal Ontology in Information Systems: Proceedings of the Sixth International Conference (FOIS 2010)
Semantic rules for context-aware geographical information retrieval
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
Ontology-driven complex event processing in heterogeneous sensor networks
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
A semantically enabled service architecture for mashups over streaming and stored data
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Rule-based OWL reasoning for specific embedded devices
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part II
Delta-reasoner: a semantic web reasoner for an intelligent mobile platform
Proceedings of the 21st international conference companion on World Wide Web
Semantic Perception: Converting Sensory Observations to Abstractions
IEEE Internet Computing
Ontology paper: The SSN ontology of the W3C semantic sensor network incubator group
Web Semantics: Science, Services and Agents on the World Wide Web
Physical cyber social computing for human experience
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception --- explanation and discrimination --- and then provide efficient algorithms for these tasks, using bit-vector encodings and operations. The applicability of our approach to machine perception is evaluated on a smart-phone mobile device, demonstrating dramatic improvements in both efficiency and scale.