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M. Weber and E. A. Lee
and doorways to localize their robot, instead of the more commonly used techniques that use edges and corners in the field of view without consideration for their
semantics.
5 Conclusion
In this chapter we observe that spatial models used in IoT applications frequently have
good reason to be domain specific. We propose semantic localization as a unifying
interface between spatial modeling and spatial programming. This abstract approach
is motivated by the need to reconcile diverse spatial representations for cross-domain
interaction. By treating spatial models as mathematical structures from model theory,
the language of mathematical logic becomes an effective tool for describing the
qualitative spatial relationships important for developing contextually aware IoT
services. When space aware services are designed for abstract spatial ontologies,
they gain advantages in privacy and interoperability.
Semantic localization focuses our discussion of physical and relational ontologies
in which information may be expressed through mathematical coordinates, spatial
relationships, and non-Euclidian maps of an environment. We formalize the notion of
an open ontology with partially unknown information, and give examples of logical
inference on open ontologies. Open relational ontologies are promising for developing contextually aware IoT services, and have a conceptual match with semantic
web technologies such as RDF, SPARQL, and semantic repositories. Semantic localization gives a principled foundation for location modeling and the design of spatially
aware IoT systems.
Acknowledgements The work in this chapter was supported in part by the National Science Foundation (NSF), award #CNS-1836601 (Reconciling Safety with the Internet) and the iCyPhy Research
Center (Industrial Cyber-Physical Systems), supported by Camozzi Industries, Denso, Siemens, and
Toyota.
References
1. Want, R., et al.: The active badge location system. ACM Trans. Inf. Syst.(TOIS) 10(1), 91–102
(1992)
2. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In:
Proceedings of the 6th Annual International Conference on Mobile Computing and Networking,
pp. 32–43 (2000)
3. Lazik, P., et al.: ALPS: a bluetooth and ultrasound platform for mapping and localization. In:
ACM Press, pp. 73–84 (2015)
4. Michael, N., Fink, J., Kumar, V.: Experimental testbed for large multirobot teams. Robot.
Autom. Mag. IEEE 15(1), 53–61 (2008)
5. Latronico, E., et al.: A vision of swarmlets. Internet Comput IEEE 19(2), 20–28 (2015)
M. Weber and E. A. Lee
and doorways to localize their robot, instead of the more commonly used techniques that use edges and corners in the field of view without consideration for their
semantics.
5 Conclusion
In this chapter we observe that spatial models used in IoT applications frequently have
good reason to be domain specific. We propose semantic localization as a unifying
interface between spatial modeling and spatial programming. This abstract approach
is motivated by the need to reconcile diverse spatial representations for cross-domain
interaction. By treating spatial models as mathematical structures from model theory,
the language of mathematical logic becomes an effective tool for describing the
qualitative spatial relationships important for developing contextually aware IoT
services. When space aware services are designed for abstract spatial ontologies,
they gain advantages in privacy and interoperability.
Semantic localization focuses our discussion of physical and relational ontologies
in which information may be expressed through mathematical coordinates, spatial
relationships, and non-Euclidian maps of an environment. We formalize the notion of
an open ontology with partially unknown information, and give examples of logical
inference on open ontologies. Open relational ontologies are promising for developing contextually aware IoT services, and have a conceptual match with semantic
web technologies such as RDF, SPARQL, and semantic repositories. Semantic localization gives a principled foundation for location modeling and the design of spatially
aware IoT systems.
Acknowledgements The work in this chapter was supported in part by the National Science Foundation (NSF), award #CNS-1836601 (Reconciling Safety with the Internet) and the iCyPhy Research
Center (Industrial Cyber-Physical Systems), supported by Camozzi Industries, Denso, Siemens, and
Toyota.
References
1. Want, R., et al.: The active badge location system. ACM Trans. Inf. Syst.(TOIS) 10(1), 91–102
(1992)
2. Priyantha, N.B., Chakraborty, A., Balakrishnan, H.: The cricket location-support system. In:
Proceedings of the 6th Annual International Conference on Mobile Computing and Networking,
pp. 32–43 (2000)
3. Lazik, P., et al.: ALPS: a bluetooth and ultrasound platform for mapping and localization. In:
ACM Press, pp. 73–84 (2015)
4. Michael, N., Fink, J., Kumar, V.: Experimental testbed for large multirobot teams. Robot.
Autom. Mag. IEEE 15(1), 53–61 (2008)
5. Latronico, E., et al.: A vision of swarmlets. Internet Comput IEEE 19(2), 20–28 (2015)