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M. Weber and E. A. Lee
collaboration that arises in the Internet of Things (IoT), such a literal representation
of space and time may not be the best choice.
When considering mobile devices and the IoT, applications often care more about
logical spatial and temporal relationships than quantitative ones. To preserve security
and privacy, for example, one device may be granted access to data held by another
device only when the two devices are in the same room at the same time. The notion
of “same room at the same time” is an example of what we call semantic localization.
It is not so much about geometric location, but rather asserts a “semantic” spatial
relationship.
In this chapter we survey IoT-relevant location models from robotics, the internet,
cyber-physical systems (CPS)s, and philosophy. The diversity in these models is
justified by differing application demands and conceptualizations of space (i.e. 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.
For all its importance to understanding IoT systems, localization, the challenge of
determining the location of physical objects, remains an open problem. GPS, which
has been a resounding success for outdoor localization, relies on direct line-of-sight
signals from satellites, and is consequently ineffective for indoor environments or
outdoor environments where obstructions, such as buildings, interfere with measurements. Researchers have been trying to address the indoor localization problem since
the early 1990s with systems like Active Badges [1] and Cricket [2], and yet even
to this day, a general purpose, accurate, cost effective, deployable system with the
potential to reach the ubiquity of outdoor GPS remains elusive. A big part of what
makes the problem difficult is the potential for interference in indoor environments
where walls, furniture, and people, obstruct and reflect signals. Even something as
simple as turning on a microwave oven causes interference to RF signals and might
disrupt signal strength measurements for an indoor localization system operating
in the 802.11 bands. Nevertheless, we are optimistic that in the near future, IoT
applications will routinely have available a variety of types of location information
with a range of quality. This chapter addresses how to organize and use that location
information.
The most commonly articulated purpose for indoor positioning is indoor navigation. There is no doubt a market for apps that can help you find your way in whatever
building you happen to be inside, but in our view this is probably a small market that
dramatically understates the potential of contextual awareness in the IoT. The future
of indoor and outdoor space-aware IoT systems involves scenarios where position in
space is less important than spatial interrelationships. Consider a fleet of self-driving
cars, where proximity in driving time, energy, and ride sharing opportunities are more
useful criteria for control than geo-coordinates. Indoors, having awareness of which
devices are in the same room may be more useful than measurements of their position
M. Weber and E. A. Lee
collaboration that arises in the Internet of Things (IoT), such a literal representation
of space and time may not be the best choice.
When considering mobile devices and the IoT, applications often care more about
logical spatial and temporal relationships than quantitative ones. To preserve security
and privacy, for example, one device may be granted access to data held by another
device only when the two devices are in the same room at the same time. The notion
of “same room at the same time” is an example of what we call semantic localization.
It is not so much about geometric location, but rather asserts a “semantic” spatial
relationship.
In this chapter we survey IoT-relevant location models from robotics, the internet,
cyber-physical systems (CPS)s, and philosophy. The diversity in these models is
justified by differing application demands and conceptualizations of space (i.e. 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.
For all its importance to understanding IoT systems, localization, the challenge of
determining the location of physical objects, remains an open problem. GPS, which
has been a resounding success for outdoor localization, relies on direct line-of-sight
signals from satellites, and is consequently ineffective for indoor environments or
outdoor environments where obstructions, such as buildings, interfere with measurements. Researchers have been trying to address the indoor localization problem since
the early 1990s with systems like Active Badges [1] and Cricket [2], and yet even
to this day, a general purpose, accurate, cost effective, deployable system with the
potential to reach the ubiquity of outdoor GPS remains elusive. A big part of what
makes the problem difficult is the potential for interference in indoor environments
where walls, furniture, and people, obstruct and reflect signals. Even something as
simple as turning on a microwave oven causes interference to RF signals and might
disrupt signal strength measurements for an indoor localization system operating
in the 802.11 bands. Nevertheless, we are optimistic that in the near future, IoT
applications will routinely have available a variety of types of location information
with a range of quality. This chapter addresses how to organize and use that location
information.
The most commonly articulated purpose for indoor positioning is indoor navigation. There is no doubt a market for apps that can help you find your way in whatever
building you happen to be inside, but in our view this is probably a small market that
dramatically understates the potential of contextual awareness in the IoT. The future
of indoor and outdoor space-aware IoT systems involves scenarios where position in
space is less important than spatial interrelationships. Consider a fleet of self-driving
cars, where proximity in driving time, energy, and ride sharing opportunities are more
useful criteria for control than geo-coordinates. Indoors, having awareness of which
devices are in the same room may be more useful than measurements of their position