Semantic localization


Improvements in sensor and wireless network enable accurate, automated, instant determination and dissemination of a user’s or
objects position. The new enabler of location-based services (LBSs) apart from the current ubiquitous networking infrastructure is
the enrichment of the different systems with semantics information, such as time, location, individual capability, preference and
more. Such semantically enriched system-modeling aims at developing applications with enhanced functionality and advanced
reasoning capabilities. These systems are able to deliver more personalized services to users by domain knowledge with advanced
reasoning mechanisms, and provide solutions to problems that were otherwise infeasible. This approach also takes user’s preference
and place property into consideration that can be utilized to achieve a comprehensive range of personalized services, such as
advertising, recommendations, or polling. This paper provides an overview of indoor localization technologies, popular models for
extracting semantics from location data, approaches for associating semantic information and location data, and applications that
may be enabled with location semantics. To make the presentation easy to understand, we will use a museum scenario to explain
pros and cons of different technologies and models. More specifically, we will first explore users’ needs in a museum scenario.
Based on these needs, we will then discuss advantages and disadvantages of using different localization technologies to meet these
needs. From these discussions, we can highlight gaps between real application requirements and existing technologies, and point
out promising localization research directions. By identifying gaps between various models and real application requirements,
we can draw a road map for future location semantics research.