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M = n * nn | ARRAY xI[M]

Q Check the box that says: Change the default database to: and select epizza, click Next Q Click Finish

Q Click on OK to finish setting up the ODBC Connection (Optional: Click on Test Data Source to test it) And finally, to set up the main administrator user: Q Using the SQL Server Enterprise Manager again, edit the table Users for the epizza database and manually change the value for the administrator user on the column UserLevel to 0

for the variable xImpact After each vectorized run, the resulting n sample values xImpact[1], xImpact[2], , xImpact[n] are fed to the combinedsample vector xI with a small one-line loop

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by the rotation of the antennas by some angle Figure 719(c) and (d) show how the ROI can be reduced by reduction of the transmission range of the locators Once information has been collected in the ranging phase, it then has to be leveraged during the computing phase in order to estimate the location of the node In [164] the authors also consider two different ways of computing the ROI using the information from the beacons received The rst method, called HiRLoc-I, involves collecting the beacons over several transmission rounds and computing the intersection of the corresponding sectors The second method, called HiRLoc-II, involves estimating the ROI after each round of transmission The ROI in the successor round is obtained as the intersection of the ROI of the previous round with the sectors corresponding to the beacons received in the successor round Both these methods, although resulting in the same estimate of ROI once all transmission rounds have been completed, exhibit different properties HiRLoc-I makes use of a smaller number of sectors compared with HiRLoc-II This is because use of the communication range variation results in several beacons being discarded Note that only the beacon corresponding to the smallest range that the node can receive is included HiRLoc-II has an extra computational step on account of the need to determine the intersection of the ROI with the previous estimate at each transmission round At the same time HiRLoc-II provides the node with an estimate of its location at any time instead of having to wait for several transmission rounds, as in the case of HiRLoc-I As in the case of SeRLoc, each node uses a majority vote-based scheme in order to reduce the computational complexity associated with calculating the ROI The defenses against attacks, such as wormhole, Sybil, and impersonation, are all based on similar mechanisms as in the case of SeRLoc, and hence we do not explain those here.

The source code for the application in 6 is under the directory c : \wlsbook\src\c06 This directory also contains the file GNUmakef ile, which is ready to be used to compile and deploy the code The application is deployed under the directory c: \wlsbook\srv\c06-srv, and the file weblogic properties is set up to start the WebLogic server with the E-Pizza application As usual, you can open a command shell, run the setEnv and the startwls scripts to begin using the application in this chapter

C:\> cd \wlsbook C:\wlsbook> setenv 6 Setting environment for chapter 6 Current settings: Java Home = c: \ j dkl3 WebLogic Home = c:\weblogic Project Home = c:\wlsbook CLASSPATH = c:\weblogic\lib\weblogic510sp5bootjar;c:\weblogic\classes\boot;c:\weblogic\lib\we blogic510sp5jar;c:\weblogic\1icense;c:\weblogic\classes;c:\weblogic\lib\weblogica uxjar;c:\wlsbook\srv\c06-srv\serverclasses C:\wlsbook> startwls

The M-dimensional combined-sample array xI will then produce Monte Carlo statistics such as averages and probability-density estimates exactly as in Sections 4-8 to 4-10

Open the browser and point it to the usual URL (http://localhost:7001), and the site should look just like the ASP site

Summary

REFERENCES 1 http://wwwcooperedu/engineering/chemechem/MMC/tutorhtml presents an excellent review of large-scale Monte Carlo simulation 2 G A Korn, Interactive Dynamic System Simulation with Microsoft Windows, Taylor and Francis, London, 1998 3 M Galassi et al, Reference Manual for the GNU Scientific Library (gsl) (current edition), ftp://ftpgnuorg/gnu/gsl/ (printed copies can be purchased from Network Theory Ltd at http://wwwnetwork-theorycouk/gsl/manual/) 4 J L Melsa and A P Sage, An Introduction to Probability and Random Processes, Prentice-Hall, Englewood Cliffs, NJ, 1971 5 G A Korn and T M Korn, Mathematical Handbook for Scientists and Engineers, revised edition, Dover, New York, 2000 6 G A Korn and J V Wait, Digital Continuous-System Simulation, PrenticeHall, Englewood Cliffs, NJ, 1978 7 G A Korn, Random-Process Simulation and Measurements, McGraw-Hill, New York, 1966 8 K Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1972 9 G A Korn, Measurement of Probability Density, Entropy, and Information in Interactive Monte Carlo Simulation, in Proceedings of the SCS MULTICON, San Diego, CA, 1999

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