Name
Abstract | ||
|---|---|---|
We have proposed Monte Carlo Sensor Networks as a method to solve certain sensor queries in the presence of noise and partial information. In that work we used very coarse position estimates for enemy agents. Here we propose methods to (1) improve the posterior probability estimates by using a more precise analysis of the sensor range geometry, and (2) help select advantageous locations to place the sensor nodes. I. INTRODUCTION |
Year | Venue | Keywords |
|---|---|---|
2005 | CAINE | sensor network,posterior probability,monte carlo |
Field | DocType | Citations |
Monte Carlo method,Markov chain Monte Carlo,Computer science,Computational science,Statistics,Wireless sensor network,Distributed computing | Conference | 2 |
PageRank | References | Authors |
0.41 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas C. Henderson | 1 | 746 | 171.00 |
| Edward Grant | 2 | 10 | 4.58 |
| Kyle Luthy | 3 | 17 | 2.89 |
| Leonardo Mattos | 4 | 29 | 5.26 |
| Matt Craver | 5 | 3 | 0.79 |