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| Content Provider | ACM Digital Library |
|---|---|
| Author | Olariu, Stephan Ismail, Abdul Samad Kamat, Maznah |
| Abstract | The capability of sensor nodes to collect and aggregate ambient data enables a mobile user to receive assistance from the network in charting a safe path through a potentially dangerous area. However, the information would be meaningless without localization. Applying an existing coarse-grain location-aware cluster (virtual infrastructure i.e. VI) for object detection provides only approximate location of dangerous objects as the objects are further away from the center. This distance-dependent location-based cluster creates various sizes of localized clusters, which cause inconsistency in size and thus may lead to inefficient path planning. The main contribution of this paper is to propose a fine-granularity clustering technique, that we call eVI that enhances the performance of previously-proposed VI by dividing clusters into smaller sub-clusters. Utilizing the eVI for autonomous navigation enables mobile sink to detect location of dangerous object effectively and thus result shorter safe travelled distance compared to other sector-based clusters as demonstrated in the simulation result. |
| Starting Page | 259 |
| Ending Page | 265 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450304405 |
| DOI | 10.1145/1971519.1971563 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2010-11-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Location-based cluster object detection Enhanced virtual infrastructure Robot navigation Location-based service |
| Content Type | Text |
| Resource Type | Article |
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