Abstract:
Indoor positioning using Wi-Fi fingerprinting is common. It uses a similarity function to compare the operational fingerprint to a radio map of reference samples. In large deployments with a high fingerprint density and a prohibitively large radio map reference sample count, the matching algorithms scale poorly.
This paper compares existing methods to simplify and reduce the operational radio map. Our empirical results show that most methods reduce computational burden but degrade accuracy. Only k-means, affinity propagation, and the strongest access point rules balance accuracy and computational time.
In addition to comparative results, this paper introduces a new evaluation framework with multiple datasets to get more general results and improve future solution reproducibility.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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