MATLAB to automatically create a wireless network GUI graphical user interface

This is a sample of creating a node manuly using GUI
BeconX=500;
BeconY=500;
axes(handles.axes1);
gca;
hold on;
hbecon=plot(BeconX,BeconY,'s')
set(hbecon,'color','red','LineWidth',8);

Basic part of my code is to randomly place the sensor nodes in the given space then connecting each two nodes if the distance between them less than or equal to the communication radius. Please check Matlab code below.

clear;
noOfNodes = 50;
rand('state', 0);
figure(1);
clf;
hold on;
L = 1000;
R = 200; % maximum range;
netXloc = rand(1,noOfNodes)*L;
netYloc = rand(1,noOfNodes)*L;
for i = 1:noOfNodes
plot(netXloc(i), netYloc(i), '.');
text(netXloc(i), netYloc(i), num2str(i));
for j = 1:noOfNodes
distance = sqrt((netXloc(i) - netXloc(j))^2 + (netYloc(i) - netYloc(j))^2);
if distance <= R
matrix(i, j) = 1; % there is a link;
line([netXloc(i) netXloc(j)], [netYloc(i) netYloc(j)], 'LineStyle', ':');
else
matrix(i, j) = inf;
end;
end;
end;

how to simulate wireless sensor network using matlab

Wireless Sensor Network Localization Measurement Repository

  
This page provides electronic access to data collected in the measurement campaign reported in [1].  Please see the reference for a detailed description of the measurement experiments.  This page describes only the files which contain the data.

Files

The data is contained within a Matlab file (binary file) that can be opened in Matlab with the `load' command. 

• Download Matlab measurements data file

Description

There are three channel measurement experiments presented in [1], the first in Section IV, and the next two in Section V.  This data corresponds to the measurement campaign in Section IV.  This campaign measured both TOA and RSS between sensors in a 44-sensor network in an office environment.   The variables contained in the Matlab file are as follows:

• P_dB_RSS:  Measured received power between sensors in dBm (a 44 by 44 matrix).  The (i,j) element is the dB average of 10 measurements, 5 with the transmitter at i and receiver at j, and 5 with the transmitter at j and receiver at i.  (The diagonal elements are not used, and the matrix is symmetric.)
• n_p_RSS:  The estimated path loss exponent
• P_0_RSS:  The received power at the reference distance of 1m, in dBm.
• tilde_d_RSS:  The ML distance estimate from P_dB_RSS, n_p_RSS, and P_0_RSS, as given in Equation (7) in reference [1].
• deviceLocs:  Actual coordinates of the 44 devices, in units of meters as shown in the plot on this page.
• T_TOA:  Time delay between sensors, in seconds (a 44 by 44 matrix).  The (i,j) element is the T_i,j reported in Section IV of [1], i.e., the average of 10 measured time delays, 5 with the transmitter at i and receiver at j, and 5 with the transmitter at j and receiver at i. (The diagonal elements are not used, and the matrix is symmetric.)
  
• mu_T_TOA:  mean time delay error.  This, as described in Section IV of [1], has already been subtracted out of T_TOA.  If you want the original TOA measurements you should add it back in to each element.
  
• v_p_TOA:  Speed of propagation (speed of light) in m/s.
  

Further Information

Please contact Neal Patwari by email:  npatwari _at_ umich.edu, or visit the Sensing and Processing Across Networks (SPAN) web site.

References

[1]  Neal Patwari, Alfred O. Hero III, Matt Perkins, Neiyer Correal, and Robert J. O'Dea,"Relative Location Estimation in Wireless Sensor Networks", IEEE Transactions on Signal Processing, vol. 51, no. 8, Aug. 2003, pages 2137-2148.  Available: [pdf].

New!  Matlab Localization Code

We now make a set of Matlab localization code freely available.  This code encompasses two main components:

• simMLE.m:  Simulation script and accompanying functions which run simulation trials for sensor localization when measurements are RSS and TOA.  The simulation generates a sensor network geometry, selects reference devices, generates measurements based on the statistical models presented in [1], and then estimates sensor coordinates by maximizing the likelihood function.  Many trials are run, and afterwards, the 1-standard deviation uncertainty ellipse of the simulations is ouput.  The Cramér-Rao bound (CRB) is also calculated and displayed.
• calcLocalizationCRB.m:  Function for calculation of the CRB on unbiased location estimators.  This code calculates the bound when measurements are either RSS, Quantized RSS, Proximity (Connectivity), TOA, or AOA.  This function is based on the work presented in several papers (see publications page).   Arbitrary sensor geometries, channel measurement parameters, and sets of measurements can be set.

Download Matlab localization simulation and Cramer-Rao Bound calculation code.