Semantic Localization for IoT
Matthew Weber and Edward A. Lee
Abstract Euclidean geometry and Newtonian time with floating point numbers are
common computational models of the physical world. However, to achieve the kind
of cyber-physical collaboration that arises in the IoT, such a literal representation of
space and time may not be the best choice. In this chapter we survey location models
from robotics, the internet, cyber-physical systems, and philosophy. The diversity in
these models is justified by differing application demands and conceptualizations of
space (spatial ontologies). To facilitate interoperability of spatial knowledge across
representations, we propose a logical framework wherein a spatial ontology is defined
as a model-theoretic structure. The logic language induced from a collection of such
structures may be used to formally describe location in the IoT via semantic localization. Space-aware IoT services gain advantages for privacy and interoperability
when they are designed for the most abstract spatial-ontologies as possible. We finish
the chapter with definitions for open ontologies and logical inference.
1 Location as IoT Context
Today, we have mature theories of computation, developed over the last 80 years
or so, and mature theories of physical structure and dynamics, developed over the
last 300 years or so. But we have only the barest beginnings of theories that conjoin
the two. One of the key points of friction is that the notion of location in space and
time are central to a physical reality, but absent in a cyber reality. When the focus is
mutual imitation, as in simulation, it is natural to construct cyber representations of
space and time by approximating positions in a Euclidean geometry and Newtonian
time with floating point numbers. But when the goal is the kind of cyber-physical
M. Weber (B) · E. A. Lee
UC Berkeley, Berkeley, United States
e-mail: matt.weber@eecs.berkeley.edu
E. A. Lee
e-mail: eal@eecs.berkeley.edu
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_16
365
Matthew Weber and Edward A. Lee
Abstract Euclidean geometry and Newtonian time with floating point numbers are
common computational models of the physical world. However, to achieve the kind
of cyber-physical collaboration that arises in the IoT, such a literal representation of
space and time may not be the best choice. In this chapter we survey location models
from robotics, the internet, cyber-physical systems, and philosophy. The diversity in
these models is justified by differing application demands and conceptualizations of
space (spatial ontologies). To facilitate interoperability of spatial knowledge across
representations, we propose a logical framework wherein a spatial ontology is defined
as a model-theoretic structure. The logic language induced from a collection of such
structures may be used to formally describe location in the IoT via semantic localization. Space-aware IoT services gain advantages for privacy and interoperability
when they are designed for the most abstract spatial-ontologies as possible. We finish
the chapter with definitions for open ontologies and logical inference.
1 Location as IoT Context
Today, we have mature theories of computation, developed over the last 80 years
or so, and mature theories of physical structure and dynamics, developed over the
last 300 years or so. But we have only the barest beginnings of theories that conjoin
the two. One of the key points of friction is that the notion of location in space and
time are central to a physical reality, but absent in a cyber reality. When the focus is
mutual imitation, as in simulation, it is natural to construct cyber representations of
space and time by approximating positions in a Euclidean geometry and Newtonian
time with floating point numbers. But when the goal is the kind of cyber-physical
M. Weber (B) · E. A. Lee
UC Berkeley, Berkeley, United States
e-mail: matt.weber@eecs.berkeley.edu
E. A. Lee
e-mail: eal@eecs.berkeley.edu
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_16
365