| Summary: | 碩士 === 國立交通大學 === 網路工程研究所 === 95 === In location-based services, the response time of location
determination is critical, especially in real-time applications. This is especially true for pattern-matching localization methods, which rely on comparing an object's current signal strength pattern against a pre-established location database of signal strength patterns collected at the training phase, when the sensing field is large (such as a wireless city). In this work, we propose a cluster-based localization framework to speed up the positioning process for pattern-matching localization schemes. Through grouping training locations with similar signal strength
patterns, we show how to reduce the associated comparison cost so as to accelerate the pattern-matching process. To deal with signal fluctuations, several clustering strategies are proposed. Extensive simulation studies are conducted. Experimental results show that more than 90% computation cost can be reduced in average without degrading the positioning accuracy.
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