%%%%%%%%
%
% n = 1000
%
% base case: straight connectivity.
%%%%%%%

rad = .010:.005:.100;

times = 1000;
n = 1000;



GComp = [3.566000 3.877000 4.744000  7.694000  13.370000  25.741000  63.934000  ... 
    235.006000  780.282000  970.566000  993.315000  998.093000   ...
    999.372000  999.775000  999.929000  999.973000  999.992000  ... 
    999.996000  999.998000  ];

connCtr = [   0.000000  0.000000  0.000000  ... 
    0.000000  0.000000  0.001000  0.075000  0.447000  0.745000  ... 
    0.885000  0.962000  0.981000  0.993000  0.998000  0.999000  ... 
    1.000000  1.000000 1.000000  0.998000  ];



rr = sqrt( log(1000) / (1000* pi))

figure(1)

plot(rad,connCtr, 'x-.')
hold on
plot([rr rr], [ 0 1])
grid on
xlabel('r: Communication Radius')
ylabel('P_c: Probability of Connectivity')

figure(2)

plot(rad,GComp/n, '^--')
hold on
plot([rr  rr], [ 0 1])
grid on
axis([0.01 .1 0.0 1.0])

xlabel('r: Communication Radius')
ylabel('Average fraction of the nodes in the giant component')


%%%%%%%%%%%%%%%%%
%
% effect of an attack on the network... how much more rad does it take
% to get to reach connectivity
%
% p = .8
%
%
%%%%%%%%%%%%%%%%%%%

times = 100;
n = 1000;p = 0.800000;

rad = [0.0100 0.0150 0.0200 0.0250 0.0300 0.0350 0.0400 0.0450 0.0500 0.0550 0.0600 0.0650 0.0700 0.0750 0.0800 0.0850 0.0900 0.0950 0.1000 ];
connCtr8 = [0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.010000  0.050000  0.430000  0.700000  0.870000  0.910000  0.980000  0.990000  0.990000  1.000000  1.000000  1.000000  ];
GComp8 = [4.030000  6.300000  10.140000  17.400000  33.790000  78.340000  261.310000  634.610000  771.990000  796.710000  802.540000  802.370000  802.500000  803.440000  803.190000  804.880000  805.780000  805.370000  804.600000  ];

rr8 = sqrt( log(1000) / (1000* .8*pi))

figure(3)

plot(rad,connCtr8, 'x-.')
hold on
plot([rr8 rr8], [ 0 1])
grid on
xlabel('r: Communication Radius')
ylabel('P_c: Probability of Connectivity')

figure(4)

plot(rad,GComp8/(.8*n), '^--')
hold on
plot([rr8  rr8], [ 0 1])
grid on
axis([0.01 .1 0.0 1.0])
xlabel('r: Communication Radius')
ylabel('Average fraction of the nodes in the giant component')

%%%%%%%%%%%%%%%
%
% effect of an attack on the network... how much more rad does it take
% to get to reach connectivity
%
% p_s = .8
% sensor failure.
%
%%%%%%%%%%%%%%%%%

connCtr8s = [  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.070000  0.370000  0.700000  0.890000  0.900000  0.970000  0.980000  1.000000  1.000000  1.000000  1.000000  ];


%% Connectivity with 20% Compromised
figure(5)

plot(rad,connCtr, 'x-.')
hold on
plot([rr rr], [ 0 1])


plot(rad,connCtr8, 'rx-.')
plot([rr8 rr8], [ 0 1], 'r')
grid on
xlabel('r: Communication Radius')
ylabel('P_c: Probability of Connectivity')


%% 20% comped, with 800nodes
figure(6)

plot(rad,connCtr8s, 'x-.')
hold on
plot(rad,connCtr8, 'rx-.')
grid on
xlabel('r: Communication Radius')
ylabel('P_c: Probability of Connectivity')