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Cluster-Based Pattern-Matching Localization Schemes for Large-Scale Wireless Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tseng, Yu-Chee |
| Copyright Year | 2007 |
| Abstract | In location-based services, the response time of location d etermination is critical, especially in real-time applications. This is especially true for patt ern-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 th e training phase, when the sensing field is large (such as a wireless city). In this work, we propose a c luster-based localization framework to speed up the positioning process for pattern-matchi ng localization schemes. Through grouping training locations with similar signal strength p atterns, we show how to reduce the associated comparison cost so as to accelerate the patternmatching process. To deal with signal fluctuations, several clustering strategies are propos ed. Extensive simulation studies are conducted. Experimental results show that more than 90% com putation cost can be reduced in average without degrading the positioning accuracy. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://ir.nctu.edu.tw/bitstream/11536/82170/1/650301.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
