Abstract
In order to improve the efficiency of an urban infrastructure, the concept of smart cities can be used, which is an equal balance between communication technologies and physical devices that connect to them. The recent changes in technology and economy have garnered a significant level of attention in the field of smart cities. The evolution of a city, in terms of both the city infrastructure and the community, can be monitored directly through the help of this technology. One such domain under the smart city infrastructure is the medical domain. On a global scale, hospitals need to take care of factors like pressure with respect to cost and reimbursement, as this can happen when they serve a population with illness. Hence, they need to find out ways to improve their efficiency. By combining the concept of smart city with hospitals, we get smart hospitals. Smart hospitals are being developed with the intention of providing a better value-added service for the common people. It also helps in redesigning and radically thinking about new processes. All of these are enabled by an interconnection of a complete network infrastructure. Further analysis of the smartness in hospitals would lead us to localization. Localization has to heavily depend on sensing devices, such as beacons, RFID tags, etc., despite the use of GPS services. Taking a typical hospital environment into account here, a navigation path should not only provide the shortest path but also keep track of the overall paths the patient has traversed across the hospital. This will help in analyzing the paths of the specific patient and give the respective recommendation measures that need to be taken in order to complete the course of action (in this case, diagnosis and treatment). The motive is a smart indoor navigation system, which learns the user’s behavior through previous sensing data.
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- CBR:
-
Case-based reasoning
- RFID:
-
Radiofrequency identification
- GPS:
-
Global positioning system
- IT:
-
Information technology
- ICT:
-
Integrated information and communication technologies
- IoT:
-
Internet of Things
- OWL:
-
Web ontology language
- AdOp:
-
Advertisement output
- QOp:
-
Query output
- ms:
-
Millisecond
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Appendix
Appendix
Context of the work
Case-based reasoning
Conceptual architecture
OWL representation of cases
Registration agent
Sample testing
Case-based retrieval agent
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Karpagam, G.R., Eshwar, K., Karthikeyan, K., Syed Hameed, M. (2020). Case-Based Reasoning (CBR)-Based Smart Indoor Navigation. In: Kumar, L., Jayashree, L., Manimegalai, R. (eds) Proceedings of International Conference on Artificial Intelligence, Smart Grid and Smart City Applications. AISGSC 2019 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-24051-6_47
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DOI: https://doi.org/10.1007/978-3-030-24051-6_47
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