Name
Abstract | ||
|---|---|---|
Many indoor positioning applications focus on determining the location of a device in a particular area, like a room of a building, rather than its metric coordinates. It is common that most buildings are partitioned in more or less a regular way and discovering the relative positions of the nodes can give us an accurate estimate of the space, e.g. a room, they are located in. In this study, we have developed a probabilistic methodology to detect the sequence of wireless sensor nodes placed in an indoor environment. We assume no preliminary training, preconfiguration or infrastructure other than the identity of one reference node at the beginning of the sequence. We trigger the nodes under consideration to transmit a series of packets, and use received signal strength information as observed by the neighbors to infer about the sequence of the nodes. In this paper, a centralized processing of the information is used. The proposed methodology is verified both by simulation and by experiments in a building with regular rooms, high shadowing and multipath distortions. Rarely observed classification errors in the experiments have motivated some extension to the basic methodology. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IPIN.2014.7275526 | 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN) |
Keywords | Field | DocType |
node sequence determination,linear configuration,indoor positioning applications,device location,building room,probabilistic methodology,wireless sensor node sequence detection,indoor environment,received signal strength information,multipath distortions,high shadowing,regular rooms | Multipath propagation,Wireless,Rayleigh channels,Network packet,Electronic engineering,Interference (wave propagation),Signal strength,Engineering,Probabilistic logic | Conference |
ISSN | Citations | PageRank |
2162-7347 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Onur Ergin | 1 | 4 | 1.12 |
| vlado handziski | 2 | 570 | 50.64 |
| Arash Behboodi | 3 | 65 | 13.77 |
| Adam Wolisz | 4 | 2693 | 407.71 |