// Vary node density and keep constant the fraction of n_source_nodes
// 1044s per iteration at ee-modalap
public static void averSize(int tst_nr, int n_averages, int plot)
{
Console.WriteLine("Executing {0}({1:d2},{2:d6},{3})", G.current(),
tst_nr, n_averages, plot);
double tx_rg = 2;
double x = 2 * tx_rg;
double[] y_v_n = new double[] {2, 3};// y vector normalized by tx_rg
int[] types = new int[] {0, 3, 9};
if (tst_nr == 1)
{
y_v_n = new double[] {2, 3, 4, 5, 6};
types = new int[] {0, 1, 3, 4, 5, 9};
}
double[] y_v = new double[y_v_n.Length];
for (int i = 0; i < y_v.Length; i++)
y_v[i] = y_v_n[i] * tx_rg;
double rho = 10;
int sched_lgth = 20;
int n_tx_frames = 5000;
double infid_thresh = 0.15;
// Parameters for the all-transmit-in-all approach: number of blocks
// and packets per block.
int blocks = 200;
int n_packets = 8;
// Number of tree reconfiguration cycles used to balance the energy
// consumption.
int n_tree_reconf = 5;
int buffer_size = 30;
G.VB = false;
G.rx_consum = 0.001;
double[,] consum_mean = new double[y_v.Length, types.Length];
double[,] consum_median = new double[y_v.Length, types.Length];
double[,] consum_max = new double[y_v.Length, types.Length];
double[,] rate_min_v = new double[y_v.Length, types.Length - 1];
for (int k = 0; k < n_averages; k++) {
Console.WriteLine("Repetition {0,4:D}. Total {1}", k,
G.elapsed());
for (int a = 0; a < y_v.Length; a++) {
int n = (int)(rho * x * y_v[a] / Math.PI / tx_rg / tx_rg);
int source_min = (int) (0.4 * (double) n);
Console.WriteLine("Simulating {0:d} nodes", n);
G.rgen = new Random(k);
AverTree at = new AverTree(n, x, y_v[a], tx_rg);
for (int d = 0; d < types.Length; d++) {
double[] tot_consum1 = new double[n];
G.rgen = new Random(k);
if (types[d] == 9) {
for (int h = 0; h < n_tree_reconf; h++) {
at.get_tree(tot_consum1);
LossTree e = new LossTree(at.fv, at.ps,
buffer_size);
e.simulate_it2(blocks, n_packets, h);
for (int i = 0; i < n; i++)
tot_consum1[i] += e.nodes[i].consum /
n_tree_reconf;
}
}
else {
for (int h = 0; h < n_tree_reconf; h++) {
double[] consum_old = new double[n];
double expon = 1.05;
for (double rate =0.1; ; rate *= expon) {
at.get_tree(tot_consum1);
LossTree t = new LossTree(at.fv, at.ps,
buffer_size);
t.find_schedule(sched_lgth, source_min);
// This is supposed to show the optimal rate.
// Console.WriteLine(((double)t.count.Count /
// sched_lgth));
int [] results = t.simulate_it(n_tx_frames,
rate, types[d], h);
// Fraction of reporting intervals with
// insufficient count
double infid_ratio = 0.0;
foreach (int m in results)
if (m < source_min)
infid_ratio +=
1.0/(double)results.Length;
// This rate yields sufficiently low
// infid_ratio. Record the consumption in
// case this is the last rate to yield
// sufficiently low infid_ratio.
if (infid_ratio < infid_thresh)
for (int q = 0; q < n; q++)
consum_old[q] = t.nodes[q].consum;
else {
// Record statistics in permanent
// variable This rate is the smallest
// rate that is too high. Record
// consumption of the previous
// iteration.
rate_min_v[a,d] +=rate/expon/
n_tree_reconf / n_averages;
for (int r=0; r < n; r++)
tot_consum1[r] += consum_old[r] /
n_tree_reconf;
break;
}
}
}
}
consum_mean[a,d] += G.Mean(tot_consum1) / n_averages;
consum_median[a,d] += G.Median(tot_consum1) / n_averages;
consum_max[a,d] += G.Max(tot_consum1) / n_averages;
}
}
}
string[] legv = new string[types.Length];
for (int i = 0; i < types.Length; i++)
legv[i] = types[i].ToString();
Pgf g = new Pgf();
string xaxis = "normalized y-v";
g.add(xaxis, "consum-mean");
g.mplot(y_v_n, consum_mean, legv);
g.add(xaxis, "consum-median");
g.mplot(y_v_n, consum_median, legv);
g.add(xaxis, "consum-max");
g.mplot(y_v_n, consum_max, legv);
double[,] gain_mean = new double [y_v_n.Length, types.Length - 1];
double[,] gain_median = new double [y_v_n.Length, types.Length -1];
double[,] gain_max = new double [y_v_n.Length, types.Length - 1];
int refz = types.Length - 1; // Column used as a benchmark
for (int q = 0; q < y_v_n.Length; q++)
for (int s = 0; s < types.Length - 1; s++)
{
gain_mean[q, s] = 100 * (consum_mean[q, refz] -
consum_mean[q, s]) / consum_mean[q, refz];
gain_median[q, s] = 100 * (consum_median[q, refz] -
consum_median[q, s]) / consum_median[q, refz];
gain_max[q, s] = 100 * (consum_max[q, refz] -
consum_max[q, s]) / consum_max[q, refz];
}
string[] legv2 = new string[types.Length - 1];
for (int i = 0; i < types.Length - 1; i++)
legv2[i] = types[i].ToString();
g.add(xaxis, "normalized load");
g.mplot(y_v_n, rate_min_v, legv2);
g.add(xaxis, "gain-mean");
g.mplot(y_v_n, gain_mean, legv2);
g.add(xaxis, "gain-median");
g.mplot(y_v_n, gain_median, legv2);
g.add(xaxis, "gain-max");
g.mplot(y_v_n, gain_max, legv2);
g.add(xaxis, "max-rate");
g.mplot(y_v_n, rate_min_v, legv2);
string filename = String.Format("{0}_{1:d2}_{2:d6}", G.current(),
tst_nr, n_averages);
g.save(filename, plot);
//Console.WriteLine("consum_mean = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_mean[0], consum_mean[1], consum_mean[2]);
//Console.WriteLine("consum_median = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_median[0], consum_median[1], consum_median[2]);
//Console.WriteLine("consum_max = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_max[0], consum_max[1], consum_max[2]);
}
// 438 seconds per iteration in ee-moda
public static void averSource(int tst_nr, int n_averages, int plot)
{
Console.WriteLine("Executing {0}({1:d2},{2:d6},{3})", G.current(),
tst_nr, n_averages, plot);
double tx_rg = 2;
double x = 3 * tx_rg;
double y = 3 * tx_rg;
double rho = 9;
int n = (int)(rho * x * y / Math.PI / tx_rg / tx_rg);
int sched_lgth = 20;
int n_tx_frames = 5000;
double infid_thresh = 0.15;
// Parameters for the all-transmit-in-all approach: number of blocks
// and packets per block.
int blocks = 200;
int n_packets = 8;
int[] source_min_v = new int[] {3, 5, 8, 11, 14, 17, 20};
Console.WriteLine("Simulating {0:d} nodes", n);
// Number of tree reconfiguration cycles used to balance the energy
// consumption.
int n_tree_reconf = 5;
int buffer_size = 30;
int[] types = new int[] {0, 3, 9};
G.VB = false;
G.rx_consum = 0.001;
double[,] consum_mean = new double[source_min_v.Length, 3];
double[,] consum_median = new double[source_min_v.Length, 3];
double[,] consum_max = new double[source_min_v.Length, 3];
for (int k = 0; k < n_averages; k++)
{
Console.WriteLine("Repetition {0,4:D}. Total {1}", k,
G.elapsed());
Console.WriteLine(" Current time is {0}",
DateTime.Now.ToString("u"));
G.rgen = new Random(k);
AverTree at = new AverTree(n, x, y, tx_rg);
for (int a = 0; a < source_min_v.Length; a++)
{
for (int d = 0; d < types.Length; d++)
{
double[] tot_consum1 = new double[n];
G.rgen = new Random(k);
if (types[d] == 9)
{
for (int h = 0; h < n_tree_reconf; h++)
{
at.get_tree(tot_consum1);
LossTree e = new LossTree(at.fv, at.ps,
buffer_size);
e.simulate_it2(blocks, n_packets, h);
for (int i = 0; i < n; i++)
tot_consum1[i] += e.nodes[i].consum /
n_tree_reconf;
}
}
else
{
for (int h = 0; h < n_tree_reconf; h++)
{
double[] consum_old = new double[n];
for (double rate =0.1; ; rate *= 1.05)
{
at.get_tree(tot_consum1);
LossTree t = new LossTree(at.fv, at.ps,
buffer_size);
t.find_schedule(sched_lgth, source_min_v[a]);
// This is supposed to show the optimal rate.
// Console.WriteLine(((double)t.count.Count /
// sched_lgth));
int [] results = t.simulate_it(n_tx_frames,
rate, types[d], h);
// Fraction of reporting intervals with
// insufficient count
double infid_ratio = 0.0;
foreach (int m in results)
{
if (m < source_min_v[a])
infid_ratio
+=1.0/(double)results.Length;
}
if (infid_ratio < infid_thresh)
{
// This rate yields sufficiently low
// infid_ratio. Record the consumption in
// case this is the last rate to yield
// sufficiently low infid_ratio.
for (int q = 0; q < n; q++)
consum_old[q] = t.nodes[q].consum;
}
else
{
// Record statistics in permanent
// variable This rate is the smallest
// rate that is too high. Record
// consumption of the previous
// iteration.
for (int r=0; r < n; r++)
tot_consum1[r] += consum_old[r] /
n_tree_reconf;
break;
}
}
}
}
consum_mean[a,d] += G.Mean(tot_consum1) / n_averages;
consum_median[a,d] += G.Median(tot_consum1) / n_averages;
consum_max[a,d] += G.Max(tot_consum1) / n_averages;
}
}
}
string[] legv = new string[] {"0", "3", "9"};
Pgf g = new Pgf();
g.extra_body.Add(String.Format("\n\nThe number of nodes is {0:d}",
n));
for (int i = 0; i < 2; i++)
{
string xaxis = "source.min";
double[] xvec = new double[source_min_v.Length];
if (i == 0)
for (int q = 0; q < source_min_v.Length; q++)
xvec[q] = (double) source_min_v[q];
else if (i == 1)
{
for (int q = 0; q < source_min_v.Length; q++)
xvec[q] = 100 * source_min_v[q] / (double) (n-1);
xaxis = "percent source.min";
}
g.add(xaxis, "consum-mean");
g.mplot(xvec, consum_mean, legv);
g.add(xaxis, "consum-median");
g.mplot(xvec, consum_median, legv);
g.add(xaxis, "consum-max");
g.mplot(xvec, consum_max, legv);
double[,] gain_mean = new double [source_min_v.Length, 2];
double[,] gain_median = new double [source_min_v.Length, 2];
double[,] gain_max = new double [source_min_v.Length, 2];
for (int q = 0; q < source_min_v.Length; q++)
for (int s = 0; s < 2; s++)
{
gain_mean[q, s] = 100 * (consum_mean[q, 2] -
consum_mean[q, s]) / consum_mean[q, 2];
gain_median[q, s] = 100 * (consum_median[q, 2] -
consum_median[q, s]) / consum_median[q, 2];
gain_max[q, s] = 100 * (consum_max[q, 2] -
consum_max[q, s]) / consum_max[q, 2];
}
string[] legv2 = new string[] {"0", "3"};
g.add(xaxis, "gain-mean");
g.mplot(xvec, gain_mean, legv2);
g.add(xaxis, "gain-median");
g.mplot(xvec, gain_median, legv2);
g.add(xaxis, "gain-max");
g.mplot(xvec, gain_max, legv2);
}
string filename = String.Format("{0}_{1:d2}_{2:d6}", G.current(),
tst_nr, n_averages);
g.save(filename, plot);
//Console.WriteLine("consum_mean = {0,8:F3} {1,8:F3}
//{2,8:F3}",
// consum_mean[0], consum_mean[1], consum_mean[2]);
//Console.WriteLine("consum_median = {0,8:F3} {1,8:F3}
//{2,8:F3}",
// consum_median[0], consum_median[1], consum_median[2]);
//Console.WriteLine("consum_max = {0,8:F3} {1,8:F3}
//{2,8:F3}",
// consum_max[0], consum_max[1], consum_max[2]);
}
public static void averRxConsum(int tst_nr, int n_averages, int plot)
{
Console.WriteLine("Executing {0}({1:d2},{2:d6},{3})", G.current(),
tst_nr, n_averages, plot);
double tx_rg = 2;
double x = 3 * tx_rg;
double y = 3 * tx_rg;
double rho = 9;
int n = (int)(rho * x * y / Math.PI / tx_rg / tx_rg);
int sched_lgth = 20;
int n_tx_frames = 5000;
double infid_thresh = 0.15;
// Parameters for the all-transmit-in-all approach: number of blocks
// and packets per block.
int blocks = 200;
int n_packets = 8;
int source_min = 1;
if (tst_nr == 0)
source_min = 3;//(int) (n * 0.2);
else if (tst_nr == 1)
source_min = 5;
else if (tst_nr == 2)
source_min = 8;
else
throw new Exception("Invalid tst_nr");
Console.WriteLine("source_min = {0:d}", source_min);
Console.WriteLine("Simulating {0:d} nodes", n);
// Number of tree reconfiguration cycles used to balance the energy
// consumption.
int n_tree_reconf = 5;
double [] tx_factor_v = new double[] {0, 4, 8, 12, 16};
double [] rx_consum_v = new double[tx_factor_v.Length];
for (int i = 0; i < tx_factor_v.Length; i++)
{
rx_consum_v[i] = Math.Pow(10, - tx_factor_v[i] / 10);
Console.WriteLine(rx_consum_v[i]);
}
int buffer_size = 30;
int[] types = new int[] {0, 3, 9};
G.VB = false;
double[,] consum_mean = new double[rx_consum_v.Length, 3];
double[,] consum_median = new double[rx_consum_v.Length, 3];
double[,] consum_max = new double[rx_consum_v.Length, 3];
for (int k = 0; k < n_averages; k++)
{
G.rgen = new Random(k);
AverTree at = new AverTree(n, x, y, tx_rg);
for (int a = 0; a < rx_consum_v.Length; a++)
{
G.rx_consum = rx_consum_v[a];
for (int d = 0; d < types.Length; d++)
{
double[] tot_consum1 = new double[n];
G.rgen = new Random(k);
if (types[d] == 9)
{
for (int h = 0; h < n_tree_reconf; h++)
{
at.get_tree(tot_consum1);
LossTree e = new LossTree(at.fv, at.ps,
buffer_size);
e.simulate_it2(blocks, n_packets, h);
for (int i = 0; i < n; i++)
tot_consum1[i] += e.nodes[i].consum /
n_tree_reconf;
}
}
else
{
for (int h = 0; h < n_tree_reconf; h++)
{
double[] consum_old = new double[n];
for (double rate =0.1; ; rate *= 1.05)
{
at.get_tree(tot_consum1);
LossTree t = new LossTree(at.fv, at.ps,
buffer_size);
t.find_schedule(sched_lgth, source_min, false);
// This is supposed to show the optimal rate.
// Console.WriteLine(((double)t.count.Count /
// sched_lgth));
int [] results = t.simulate_it(n_tx_frames,
rate, types[d], h);
// Fraction of reporting intervals with
// insufficient count
double infid_ratio = 0.0;
foreach (int m in results)
{
if (m < source_min)
infid_ratio
+=1.0/(double)results.Length;
}
if (infid_ratio < infid_thresh)
{
// This rate yields sufficiently low
// infid_ratio. Record the consumption in
// case this is the last rate to yield
// sufficiently low infid_ratio.
for (int q = 0; q < n; q++)
consum_old[q] = t.nodes[q].consum;
}
else
{
// Record statistics in permanent
// variable This rate is the smallest
// rate that is too high. Record
// consumption of the previous
// iteration.
for (int r=0; r < n; r++)
tot_consum1[r] += consum_old[r] /
n_tree_reconf;
break;
}
}
}
}
consum_mean[a,d] += G.Mean(tot_consum1) / n_averages;
consum_median[a,d] += G.Median(tot_consum1) / n_averages;
consum_max[a,d] += G.Max(tot_consum1) / n_averages;
}
}
}
string[] legv = new string[] {"0", "3", "9"};
Pgf g = new Pgf();
string xaxis = "$10\\,\\mathrm{log}_{10}(P_{tx}/P_{rx})$";
g.add(xaxis, "consum-mean");
g.mplot(tx_factor_v, consum_mean, legv);
g.add(xaxis, "consum-median");
g.mplot(tx_factor_v, consum_median, legv);
g.add(xaxis, "consum-max");
g.mplot(tx_factor_v, consum_max, legv);
double[,] gain_mean = new double [rx_consum_v.Length, 2];
double[,] gain_median = new double [rx_consum_v.Length, 2];
double[,] gain_max = new double [rx_consum_v.Length, 2];
for (int q = 0; q < rx_consum_v.Length; q++)
for (int s = 0; s < 2; s++)
{
gain_mean[q, s] = 100 * (consum_mean[q, 2] -
consum_mean[q, s]) / consum_mean[q, 2];
gain_median[q, s] = 100 * (consum_median[q, 2] -
consum_median[q, s]) / consum_median[q, 2];
gain_max[q, s] = 100 * (consum_max[q, 2] -
consum_max[q, s]) / consum_max[q, 2];
}
string[] legv2 = new string[] {"0", "3"};
g.add(xaxis, "gain-mean");
g.mplot(tx_factor_v, gain_mean, legv2);
g.add(xaxis, "gain-median");
g.mplot(tx_factor_v, gain_median, legv2);
g.add(xaxis, "gain-max");
g.mplot(tx_factor_v, gain_max, legv2);
string filename = String.Format("{0}_{1:d2}_{2:d6}", G.current(),
tst_nr, n_averages);
g.save(filename, plot);
//Console.WriteLine("consum_mean = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_mean[0], consum_mean[1], consum_mean[2]);
//Console.WriteLine("consum_median = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_median[0], consum_median[1], consum_median[2]);
//Console.WriteLine("consum_max = {0,8:F3} {1,8:F3} {2,8:F3}",
// consum_max[0], consum_max[1], consum_max[2]);
}