def test_niculescu2003_sim(self):
"""Test niculescu2003 default simulation."""
sim = Simulation(self.net)
sim.run()
i = 0
for node in self.net.nodes():
i += 1
self.assertTrue(len(node.memory.get('pos', [None, None])) == 2\
or 'tp' in node.memory)
self.net.savefig(fname=get_path(DATETIME_DIR, "dv-%s" % self.n),
title="DV hop Default Simulation - %s nodes" % self.n,
x_label="X",
y_label="Y")
print i, self.net.__len__()
print self.net.nodes()[1].get_dic()
print self.net.nodes()[20].get_dic()
#print self.net.get_dic()
print self.net.nodes()[0].type, \
self.net.nodes()[0].get_dic()['Info']['position'],\
self.net.nodes()[0].memory['tp']
for node in self.net.nodes():
act = node.get_dic()['Info']['position']
est = node.memory['pos']
if node.type == 'C':
err = sqrt(sum(pow(act - est, 2)))
print node.type, act, est, err
def test_niculescu2003_sim(self):
"""Test niculescu2003 default simulation."""
sim = Simulation(self.net)
sim.run()
i=0;
for node in self.net.nodes():
i += 1
self.assertTrue(len(node.memory.get('pos', [None, None])) == 2\
or 'tp' in node.memory)
self.net.savefig(fname=get_path(DATETIME_DIR, "dv-%s" %self.n),
title="DV hop Default Simulation - %s nodes" % self.n,
x_label="X", y_label="Y")
print i, self.net.__len__()
print self.net.nodes()[1].get_dic()
print self.net.nodes()[20].get_dic()
#print self.net.get_dic()
print self.net.nodes()[0].type, \
self.net.nodes()[0].get_dic()['Info']['position'],\
self.net.nodes()[0].memory['tp']
for node in self.net.nodes():
act = node.get_dic()['Info']['position']
est = node.memory['pos']
if node.type=='C':
err = sqrt(sum(pow(act - est, 2)))
print node.type, act, est, err
n_range = np.arange(11, max_n, 5)
for n in n_range:
# network topology setup
Node.cid = 1
net_gen = Toplogy(degree=2, n_count=n)
net = net_gen.generate_star_ehwsnnetwork(x_radius=x_radius,
y_radius=y_radius,
sector=1,
clusters=clusters,
is_random=1)
net.name = "New scheme with EHWSN"
# saving topology as PNG image
net.savefig(fname=get_path(DATETIME_DIR, "star-%s" % n),
title="EHWSN nodes around coordinator - %s nodes" % n,
x_label="X",
y_label="Y")
# simulation start
net.algorithms = ((Beacon, {'informationKey': 'Data'}), )
coordinator_node = net.nodes()[0]
coordinator_node.memory['Data'] = 'Beacon 1'
sim = Simulation(net)
sim.run()
# simulation ends
# capturing results
consume = []
energy = []
def gethtmlScatter(x, y, fname, folder="", axis_range={}, labels=None,
xlabel="X", ylabel="Y", title=None, comment=".", **kwargs):
w = 100
h = 100
title = title or fname
report = kwargs.pop('report', comment)
plot_type = 'scatter'
if 'plot_type' in kwargs:
plot_type = kwargs.pop('plot_type')
plot_options = [""]
if 'plot_options' in kwargs:
plot_options = kwargs.pop('plot_options')
show_range = kwargs.pop('show_range', 0)
circle = ""
if show_range:
circle = "allowPointSelect:true, marker: { states: { select: { lineColor: 'red', radius: " + str(show_range) + "}}},"
series_data = ""
series_line = ""
k=0
if len(y)>10:
series_data += '''
{
name: "''' + (labels[k] if k < len(labels) else "") + '''",
''' + (plot_options[k] if k < len(plot_options) else "") + '''
data: ''' + str(y) + ''',
''' + circle + '''
},
'''
else:
k=0
for series in y:
series_data += '''
{
name: "''' + (labels[k] if k < len(labels) else "") + '''",
''' + (plot_options[k] if k < len(plot_options) else "") + '''
data: ''' + str(series) + ''',
''' + circle + '''
},
'''
k += 1
if k > 1:
j = 0
for yy in y[1]:
try:
#print yy['name'], y[1][j]['name']
if yy['name'] == y[2][j]['name']:
yyx, yyy = yy['x'], yy['y']
yyx2, yyy2 = y[2][j]['x'], y[2][j]['y']
yyx2 = w + 3 if yyx2 > w + 3 else yyx2
yyx2 = -3 if yyx2 < -3 else yyx2
yyy2 = h + 3 if yyy2 > h+3 else yyy2
yyy2 = -3 if yyy2 < -3 else yyy2
series_line += "chart.renderer.path(['M', chart.xAxis[0].toPixels(" + str(yyx) + \
"), chart.yAxis[0].toPixels(" + str(yyy) + \
"), 'L', chart.xAxis[0].toPixels(" + str(yyx2) + \
"), chart.yAxis[0].toPixels(" + str(yyy2) + \
")]).attr({'stroke-width': 1, stroke: 'gray'}).add(group);"
j += 1
except (ValueError, Exception) as exc:
print exc
break
contents = '''<!DOCTYPE html>
<html>
<head>
<meta content="text/html; charset=ISO-8859-1" http-equiv="content-type">
<title>'''+title+'''</title>
<script type='text/javascript' src='http://code.jquery.com/jquery-1.9.1.js'></script>
<style type='text/css'>
</style>
<script type='text/javascript'>//<![CDATA[
var chart;
var w;
$(function () {
var group;
vis = true;
w = $('#container').width()
$('#container').highcharts({
credits: {
enabled: false
},
exporting: {
filename: "''' + fname + '''"
},
legend: {
margin: -5, padding: 0, y: -5, //layout: 'vertical', align: 'right', verticalAlign: 'middle', shadow: true, backgroundColor: '#FFFFFF',
title: {
//text: 'Farrukh Shahzad<br/>PhD, KFUPM<br/>Nov. 2015</span>',
style: {fontStyle: 'italic', fontSize: '10px'}
}
},
chart: {
zoomType: 'xy', panning: true, panKey: 'shift',
type: "''' + plot_type + '''"
},
title: {
text: "''' + title + '''"
},
subtitle: {
text: "''' + comment + '''",
style: {color:'black', fontSize: '18px', fontFamily: 'Times'}
},
tooltip: {
borderRadius: 10,
crosshairs: [{width: 1, color: 'gray', dashStyle: 'shortdot'}, {width: 1, color: 'gray', dashStyle: 'shortdot'}],
pointFormat: 'X: {point.x:.1f}, Y: {point.y:.1f}',
headerFormat: '<b>Node: {point.key}</b>, {series.name}<br />',
shared: true
},
xAxis: [{ // Primary xAxis
//minRange : 5,
gridLineWidth: 1,
min: '''+ json.dumps(axis_range.get('xmin')) + ''', max: '''+ json.dumps(axis_range.get('xmax')) + ''',
title: { text: "'''+ xlabel + '''", y: -5, style: {color:'black'}}
}],
yAxis: [{ // Primary yAxis
lineWidth: 1,
minPadding: 0.0,
maxPadding: 0.0,
min: '''+ json.dumps(axis_range.get('ymin')) + ''', max: '''+ json.dumps(axis_range.get('ymax')) + ''',
title: {text: "'''+ ylabel + '''", x: 5, style: {color:'black'}}
}],
series: [''' + series_data + ''' ]
},
function (chart) { // on complete
group = chart.renderer.g().add();
'''+ series_line + '''
});''' + \
("$('#button').click(function () {if (vis) group.hide(); else group.show(); vis = !vis; });" if len(series_line)>1 else "") + \
'''
});
//]]>
</script>
</head>
<body>
<script src="http://code.highcharts.com/highcharts.js"></script>
<script src="http://code.highcharts.com/modules/data.js"></script>
<script src="http://code.highcharts.com/highcharts-more.js"></script>
<script src="http://code.highcharts.com/modules/exporting.js"></script>
<table border="0" align="center"><tr><td align="center">
<span id="container" style="height: 600px; margin: auto; min-width: 600px; max-width: 600px"></span>
''' + ("<button id='button'>Toggle Error Lines</button>" if len(series_line)>1 else "") + \
'''</td><td style="font-family: 'Lucida Console'; font-size: 12px; height: 600px; padding: 25px; max-width: 500px">
<span id="report" ><h3>''' + title + '''</h3>''' + \
report + '''<br><br>Click on the legend on the bottom to hide/show series</span>
</td></tr></table>
</body>
</html>
'''
path = get_path(folder,fname) + ".html"
if 'open' in kwargs:
browseLocal(contents, filename=path)
else:
strToFile(contents, filename=path)
'name': str(node.id), 'color': 'blue',
'marker': {'radius': '5'}})
nn = net.__len__()
n_anchors = (int)(nn * p_anchors/100.0)
avg_deg = round(net.avg_degree())
comm_range = node.commRange
# set the network name based on no. of Nodes, degree and Comm. Range
net.name = "%s*%s - $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $ND=$%s$/10^3.m^2$" \
% (net_gen.name, str(region), nn, n_anchors, round(avg_deg,1), round(comm_range,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$","")
folder = DATETIME_DIR+ "-" + net_gen.name
net.savefig(fname=get_path(folder, filename), title=net.name,
x_label="X-coordinate (m)", y_label="Y-coordinate (m)", show_labels=True, format="pdf")
# Now select the algorithm for simulation
net.algorithms = ((DVHop, {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'dataKey': 'I'
}),
(Trilaterate, {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'positionKey': 'pos'+str(region),
'dataKey': 'I'}),
)
start_time = time.time()
(w, h / 4)]])
#net2.save_json(get_path(TOPOLOGY_DIR, net_gen.name + ".json"), scale=(6, 5))
print Node.cid
#net, p_nk = net_gen.get_clusters(net, clusters=clusters, method="Random")
#print Node.cid
n_anchors = net_gen.anchors
node = net.nodes()[0]
# saving topology as PNG image
avg_deg = net.avg_degree()
na = net.__len__()
net.name = "%s, $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $N_D=$%s$/10^3.m^2$" \
% (net_gen.name, na, n_anchors, round(avg_deg,1), round(node.commRange,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$", "")
net.savefig(fname=get_path(TOPOLOGY_DIR, filename),
title=net.name,
xlabel="X-coordinate (m)",
ylabel="Y-coordinate (m)",
show_labels=True,
format="pdf")
print net.__len__(), avg_deg, node.commRange
#
#
# Node.cid = 1
# #net_gen = Toplogy(n_count=n, degree=degree, n_max=n, n_min=n, connected=False)
# net = net_gen.generate_grid_network(name="C-shaped Grid", randomness=0.2,
# cut_shape=[[(w/4,3*h/4), (w, h/4)]])
# f_anchors = (int)(100 / p_anchors)
}
})
avg_deg = round(net.avg_degree())
comm_range = node.commRange
# set the network name based on no. of Nodes, degree and Comm. Range
net.name = "%s(%s) - $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $ND=$%s$/10^3.m^2$" \
% (net_gen.name, vary, nn, n_anchors, round(avg_deg,1), round(comm_range,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$", "")
area = "A: %s x 1000 m^2, ND: %s /1000 m^2" % (round(
net_gen.area / 1000.0, 2), round(net_gen.net_density * 1000, 1))
net.savefig(fname=get_path(folder, filename),
title=net.name,
x_label="X-coordinate (m)",
y_label="Y-coordinate (m)",
show_labels=False,
format="pdf")
# Now select the algorithm for simulation
net.algorithms = (
(DVHop, {
'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'dataKey': 'I'
}),
(Trilaterate, {
'truePositionKey': 'tp',
n_power = []
n_range = np.arange(20, max_n + 1, 10)
start_time = time.time()
project_name = "IWSN"
folder = DATETIME_DIR + "-" + project_name
# network topology setup
Node.cid = 1
net_gen = Manual()
net = net_gen.generate_zigbee_network(name="Zigbee")
n = len(net)
net.name = "Zigbee Experiment" + "\nN=%s, R=%s m" \
%(n, global_settings.COMM_RANGE)
filename = net.name.replace("\n", "-")
# saving topology as PNG image
net.savefig(fname=get_path(folder, filename),
title=net.name,
xlabel="X-coordinate (m)",
ylabel="Y-coordinate (m)",
show_labels=True,
format="png",
label_color='yellow')
print net.__len__(), len(net)
net.reset()
Node.cid = 1
net_gen = Manual()
net = net_gen.generate_mesh_network(name="WirelessHart Mesh")
n = len(net)
net.name = "WirelessHart Experiment" + "\nN=%s, R=%s m" \
%(n, global_settings.COMM_RANGE)
positions.append({
'x': net.pos[node][0],
'y': net.pos[node][1],
'name': 'Node: ' + str(node.id)
})
nn = net.__len__()
n_anchors = (int)(nn * p_anchors / 100.0)
avg_deg = round(net.avg_degree())
comm_range = node.commRange
# set the network name based on no. of Nodes, degree and Comm. Range
net.name = "%s - $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $ND=$%s$/10^3.m^2$" \
% (net_gen.name, nn, n_anchors, round(avg_deg,1), round(comm_range,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$", "")
net.savefig(fname=get_path(DATETIME_DIR, filename),
title=net.name,
x_label="X-coordinate (m)",
y_label="Y-coordinate (m)",
show_labels=False,
format="pdf")
# Now select the algorithm for simulation
net.algorithms = (
(DVHop, {
'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'dataKey': 'I'
}),
(Trilaterate, {
'truePositionKey': 'tp',
cut_shape=[[(w/4,3*h/4), (w, h/4)]])
#net2.save_json(get_path(TOPOLOGY_DIR, net_gen.name + ".json"), scale=(6, 5))
print Node.cid
#net, p_nk = net_gen.get_clusters(net, clusters=clusters, method="Random")
#print Node.cid
n_anchors = net_gen.anchors
node=net.nodes()[0]
# saving topology as PNG image
avg_deg = net.avg_degree()
na = net.__len__()
net.name = "%s, $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $N_D=$%s$/10^3.m^2$" \
% (net_gen.name, na, n_anchors, round(avg_deg,1), round(node.commRange,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$","")
net.savefig(fname=get_path(TOPOLOGY_DIR, filename), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)", show_labels=True, format="pdf")
print net.__len__(), avg_deg, node.commRange
#
#
# Node.cid = 1
# #net_gen = Toplogy(n_count=n, degree=degree, n_max=n, n_min=n, connected=False)
# net = net_gen.generate_grid_network(name="C-shaped Grid", randomness=0.2,
# cut_shape=[[(w/4,3*h/4), (w, h/4)]])
# f_anchors = (int)(100 / p_anchors)
# for node in net.nodes():
# if node.id % f_anchors == 0: # anchor nodes
# node.compositeSensor = (TruePosSensor,)
# node.type = 'C' # Anchors
k += 1
Node.cid = 1
net_gen = Topology(n_count=n, connected=False,
commRange=global_settings.COMM_RANGE, comm_range=global_settings.COMM_RANGE)
net = net_gen.generate_grid_network(name="Grid", randomness=0, p_anchors=None)
net, p = net_gen.get_clusters(net, clusters=clusters, method="EM")
node = net.nodes()[int(n/2)]
net.remove_node(node)
n = len(net)
net.name = "Grid Topology" + "\nN=%s, R=%s m" \
%(n, global_settings.COMM_RANGE)
filename = net.name.replace("\n", "-")
# saving topology as PNG image
net.savefig(fname=get_path(folder, filename), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
show_labels=True, format="png", label_color='yellow')
net.name = filename
net.save_json(get_path(folder, filename+".json"), scale=(1, 1))
print net.__len__(), len(net)
coordinator_node = net.nodes()[n-1]
coordinator_node.type = 'B'
coordinator_node.compositeSensor = (NeighborsSensor,) # (TruePosSensor,)
# simulation start
net.algorithms = ((Beacon, {'informationKey': 'Data', 'detination': coordinator_node}),)
coordinator_node.memory['Data'] = 'Beacon 1'
# saving topology as PNG image
#n_anchors = (int)(na * p_anchors/100.0)
net.name = "%s - $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $N_D=$%s$/10^3.m^2$, %s=%s" \
% (net_gen.name, na, clusters, round(avg_deg,1), round(node.commRange,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1),
global_settings.CHANNEL_TYPE, global_settings.DOI)
filename = (net.name.split("\n")[0]).replace("$","")
#net.savefig(fname=get_path(TOPOLOGY_DIR, filename), title=net.name,
# xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
# show_labels=True, format="png")
net2, p = net_gen.get_clusters(net, clusters=clusters, method="EM")
node = net2.nodes()[int(n/2)]
net2.remove_node(node)
net2.savefig(fname=get_path(TOPOLOGY_DIR, filename+"-anchors"), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
show_labels=True, format="png")
n_anchors = net_gen.anchors
avg_deg = net2.avg_degree()
na = net2.__len__()
print na, avg_deg, node.commRange, net_gen.area, net_gen.net_density
from networkx import *
import networkx.algorithms.approximation
print networkx.info(net)
#print networkx.edges(net)
#print networkx.algorithms.approximation.k_components(net)
print networkx.algorithms.approximation.average_clustering(net)
def gethtmlmultiplots(x, y, fname, folder="", axis_range={}, labels=None,
xlabel="X", ylabel="Y", title=None, comment=".", **kwargs):
w = 100
h = 100
title = title or fname
report = kwargs.pop('report', comment)
plot_type = 'scatter'
if 'plot_type' in kwargs:
plot_type = kwargs.pop('plot_type')
plot_options = [""]
if 'plot_options' in kwargs:
plot_options = kwargs.pop('plot_options')
series_data = ""
k=0
if len(y)>10:
series_data += '''
{
name: "''' + (labels[k] if k < len(labels) else "") + '''",
''' + (plot_options[k] if k < len(plot_options) else "") + '''
data: ''' + str(y) + ''',
},
'''
else:
for series in y:
series_data += '''
{
name: "''' + (labels[k] if k < len(labels) else "") + '''",
''' + (plot_options[k] if k < len(plot_options) else "") + '''
data: ''' + str(series) + ''',
},
'''
k += 1
contents = '''<!DOCTYPE html>
<html>
<head>
<meta content="text/html; charset=ISO-8859-1" http-equiv="content-type">
<title>'''+title+'''</title>
<script type='text/javascript' src='http://code.jquery.com/jquery-1.9.1.js'></script>
<style type='text/css'>
</style>
<script type='text/javascript'>//<![CDATA[
var chart;
var w;
$(function () {
var group;
vis = true;
w = $('#container').width()
$('#container').highcharts({
credits: {
enabled: false
},
exporting: {
filename: "''' + fname + '''"
},
legend: {
margin: -5, padding: 0, y: -5, //layout: 'vertical', align: 'right', verticalAlign: 'middle', shadow: true, backgroundColor: '#FFFFFF',
title: {
//text: 'Farrukh Shahzad<br/>PhD, KFUPM<br/>Nov. 2015</span>',
style: {fontStyle: 'italic', fontSize: '10px'}
}
},
chart: {
zoomType: 'xy', panning: true, panKey: 'shift',
type: "''' + plot_type + '''"
},
title: {
text: "''' + title + '''"
},
subtitle: {
text: "''' + comment + '''",
style: {color:'black', fontSize: '18px', fontFamily: 'Times'}
},
tooltip: {
borderRadius: 10,
crosshairs: [{width: 1, color: 'gray', dashStyle: 'shortdot'}, {width: 1, color: 'gray', dashStyle: 'shortdot'}],
pointFormat: '{point.x:.1f}, {point.y:.1f}',
headerFormat: '<b>{series.name}</b><br />',
shared: true
},
xAxis: [{ // Primary xAxis
gridLineWidth: 1,
min: '''+ json.dumps(axis_range.get('xmin')) + ''', max: '''+ json.dumps(axis_range.get('xmax')) + ''',
title: { text: "'''+ xlabel + '''", y: -5, style: {color:'black'}}
}],
yAxis: [{ // Primary yAxis
lineWidth: 1,
minPadding: 0.0,
maxPadding: 0.0,
min: '''+ json.dumps(axis_range.get('ymin')) + ''', max: '''+ json.dumps(axis_range.get('ymax')) + ''',
title: {text: "'''+ ylabel + '''", x: 5, style: {color:'black'}}
}],
series: [''' + series_data + ''' ]
})
});
//]]>
</script>
</head>
<body>
<script src="http://code.highcharts.com/highcharts.js"></script>
<script src="http://code.highcharts.com/modules/data.js"></script>
<script src="http://code.highcharts.com/highcharts-more.js"></script>
<script src="http://code.highcharts.com/modules/exporting.js"></script>
<table border="0" align="center"><tr><td align="center">
<span id="container" style="height: 600px; margin: auto; min-width: 600px; max-width: 600px"></span>
</td><td style="font-family: 'Lucida Console'; font-size: 12px; height: 600px; padding: 25px; max-width: 500px">
<span id="report" ><h3>''' + title + '''</h3>''' + \
report + '''<br><br>Click on the legend on the bottom to hide/show series</span>
</td></tr></table>
</body>
</html>
'''
path = get_path(folder,fname) + ".html"
if 'open' in kwargs:
browseLocal(contents, filename=path)
else:
strToFile(contents, filename=path)
else:
positions.append({
'x': net.pos[node][0],
'y': net.pos[node][1],
'name': 'Node: ' + str(node.id)
})
newpos.append({'x': newx, 'y': newy, 'name': 'Node: ' + str(node.id)})
xestpositions.append(newx)
deltapos.append(net.pos[node][0] - newx)
avg_deg = round(net.avg_degree())
net.name = "%s - Nodes=%s, Avg degree=%s, Range=%s" \
% (net_gen.name, net.__len__(), int(avg_deg), int(node.commRange))
net.savefig(fname=get_path(TOPOLOGY_DIR, net.name),
title=net.name,
format='pdf',
x_label="X",
y_label="Y",
show_labels=False)
print net.__len__(), avg_deg, node.commRange, n_anchors
end_time = time.time() - start_time
ntx = 345
comments = "Anchors: " + str(n_anchors) +"="+str(p_anchors) +"%"+ \
", Runtime(sec): "+ str(round(end_time,2)) + \
", No. of Tx: " + str(ntx)
plotter.gethtmlScatter(xpositions, [anchpositions, positions, newpos],
fname="Topology-" + net.name,
folder=TOPOLOGY_DIR,
# x_radius=x_radius, y_radius=y_radius, sector=0.7,
# cluster_divide=4, randomness=4.8)
net = net_gen.generate_gaussian_network(name='', randomness=1)
#net = net_gen.generate_grid_network(name="Randomized Grid", randomness=0.7, p_anchors=None)
for m in range(2):
method = methods[m]
net, p_nk = net_gen.get_clusters(net, clusters=clusters, method=method)
net.name = "Energy Efficient Clustering-%s\nN=%s, K=%s" \
%(methods2[m], n, clusters)
filename = net.name.replace("\n", "-")
# saving topology as PNG image
net.savefig(fname=get_path(folder, filename), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
show_labels=True, format="pdf")
print net.__len__(), len(net)
# tn = net.neighbors(net.nodes()[1])
# print len(tn), tn
# bfs = list(nx.bfs_tree(net, net.nodes()[1]))
# print bfs, len(bfs)
# print net.nodes()[34].commRange
p=nx.shortest_path_length(net)
print len(p), p
total=1e-3
nn = len(net)-clusters
print len(p_nk), p_nk[0][0]
for i in range(nn):
for k in range(clusters):
Node.cid = 1
#net_gen = NetworkGenerator(n_count=n)
#net = net_gen.generate_ring_network(x_radius=x_radius*clusters, y_radius=y_radius*clusters)
net_gen = Star(n_count=n)
net = net_gen.generate_ring_network(x_radius=x_radius, y_radius=y_radius, sector=0.75)
n_anchors = (int)(p_anchors * net.__len__()/100)
for node in net.nodes():
if (node.id % n_anchors==0): # anchor nodes
node.compositeSensor = (TruePosSensor,)
node.type = 'C' # Anchors
net.name = "DV on ring"
# saving topology as PNG image
net.savefig(fname=get_path(TOPOLOGY_DIR, "dv-%s-ring-sect" %n),
title="C shaped - %s nodes" % n,
x_label="X", y_label="Y")
print net.__len__(), net.avg_degree(), node.commRange
net.reset()
Node.cid = 1
net_gen = Star(n_count=n)
net = net_gen.generate_two_ring_network(x_radius=x_radius/2, y_radius=y_radius/2, sector=0.75)
n_anchors = (int)(p_anchors * net.__len__()/100)
for node in net.nodes():
if (node.id % n_anchors==0): # anchor nodes
node.compositeSensor = (TruePosSensor,)
node.type = 'C' # Anchors
net.name = "DV on ring"
for n in n_range:
# network topology setup
Node.cid = 1
net_gen = Toplogy(degree=2, n_count=n)
net = net_gen.generate_star_ehwsnnetwork(
x_radius=x_radius,
y_radius=y_radius,
sector=1,
clusters=clusters,
is_random=1)
net.name = "New scheme with EHWSN"
# saving topology as PNG image
net.savefig(fname=get_path(DATETIME_DIR, "star-%s" %n),
title="EHWSN nodes around coordinator - %s nodes" %n,
x_label="X", y_label="Y")
# simulation start
net.algorithms = ((Beacon, {'informationKey': 'Data'}), )
coordinator_node = net.nodes()[0]
coordinator_node.memory['Data'] = 'Beacon 1'
sim = Simulation(net)
sim.run()
# simulation ends
# capturing results
consume = []
energy = []
distances = []
node.type = 'C' # Anchors
anchpositions.append({'x': net.pos[node][0], 'y': net.pos[node][1],
'name': str(node.id), 'color': 'red',
'marker': {'symbol': 'circle', 'radius': '8'}})
else:
node.type = 'N' # Normal
node.compositeSensor = ('NeighborsSensor',)
positions.append({'x': net.pos[node][0], 'y': net.pos[node][1],
'name': 'Node: ' + str(node.id), 'color': 'blue',
'marker': {'radius': '5'}})
avg_deg = round(net.avg_degree())
comm_range = node.commRange
# set the network name based on no. of Nodes, degree and Comm. Range
area = "A: %s x 1000 m^2, ND: %s /1000 m^2, DOI: %s" \
%(round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1),
round(doi, 1))
net.name = "%s(%s) - $N=$%s(%s), $D=$%s, $R=$%s m\n" \
"$A=$%s$\\times 10^3m^2$, $ND=$%s$/10^3.m^2$, $DOI$=%s" \
% (net_gen.name, vary, nn, n_anchors, round(avg_deg,1),
round(comm_range,1), round(net_gen.area/1000.0, 2),
round(net_gen.net_density*1000, 1), round(doi, 1))
filename = (net.name.split("\n")[0]).replace("$","")
net.savefig(fname=get_path(folder, filename), title=net.name,
x_label="X-coordinate (m)", y_label="Y-coordinate (m)",
show_labels=True, format="pdf")
net.reset()
'marker': {'symbol': 'circle', 'radius': '8'}})
else:
positions.append({'x': net.pos[node][0], 'y': net.pos[node][1],
'name': 'Node: ' + str(node.id)})
nn = net.__len__()
n_anchors = (int)(nn * p_anchors/100.0)
avg_deg = round(net.avg_degree())
comm_range = node.commRange
# set the network name based on no. of Nodes, degree and Comm. Range
net.name = "%s - $N=$%s(%s), $D=$%s, $R=$%s m\n$A=$%s$\\times 10^3m^2$, $ND=$%s$/10^3.m^2$" \
% (net_gen.name, nn, n_anchors, round(avg_deg,1), round(comm_range,1),
round(net_gen.area/1000.0, 2), round(net_gen.net_density*1000, 1))
filename = (net.name.split("\n")[0]).replace("$","")
net.savefig(fname=get_path(DATETIME_DIR, filename), title=net.name,
x_label="X-coordinate (m)", y_label="Y-coordinate (m)", show_labels=False, format="pdf")
# Now select the algorithm for simulation
net.algorithms = ((DVHop, {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'dataKey': 'I'
}),
(Trilaterate, {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'positionKey': 'pos',
'dataKey': 'I'}),
)
start_time = time.time()
for n in n_range:
# network topology setup
Node.cid = 1
net_gen = LOS(degree=2, n_count=n)
net = net_gen.generate_network(x_radius=x_radius,
y_radius=y_radius,
sector=0.5,
clusters=clusters,
is_random=0)
net.name = "Yokogawa Industrial Sensor Experiment"
# saving topology as PNG image
net.savefig(
fname=get_path(DATETIME_DIR, "topology-%s" % n),
title="Industrial sensor nodes around Base station - %s nodes" % n,
x_label="X",
y_label="Y")
# simulation start
net.algorithms = ((Beacon, {'informationKey': 'Data'}), )
coordinator_node = net.nodes()[0]
coordinator_node.memory['Data'] = 'Beacon 1'
sim = Simulation(net)
sim.run()
# simulation ends
# capturing results
consume = []
energy = []
Node.cid = 1
#net_gen = NetworkGenerator(n_count=n)
#net = net_gen.generate_ring_network(x_radius=x_radius*clusters, y_radius=y_radius*clusters)
net_gen = Star(n_count=n)
net = net_gen.generate_ring_network(x_radius=x_radius,
y_radius=y_radius,
sector=0.75)
n_anchors = (int)(p_anchors * net.__len__() / 100)
for node in net.nodes():
if (node.id % n_anchors == 0): # anchor nodes
node.compositeSensor = (TruePosSensor, )
node.type = 'C' # Anchors
net.name = "DV on ring"
# saving topology as PNG image
net.savefig(fname=get_path(TOPOLOGY_DIR, "dv-%s-ring-sect" % n),
title="C shaped - %s nodes" % n,
x_label="X",
y_label="Y")
print net.__len__(), net.avg_degree(), node.commRange
net.reset()
Node.cid = 1
net_gen = Star(n_count=n)
net = net_gen.generate_two_ring_network(x_radius=x_radius / 2,
y_radius=y_radius / 2,
sector=0.75)
n_anchors = (int)(p_anchors * net.__len__() / 100)
for node in net.nodes():
if (node.id % n_anchors == 0): # anchor nodes
n_power = []
n_range = np.arange(20, max_n+1, 10)
start_time = time.time()
project_name = "IWSN - ISA100.11a (obstacle)"
folder = DATETIME_DIR+ "-" + project_name
# network topology setup
Node.cid = 1
net_gen = Manual()
net = net_gen.generate_mesh_network(name="ISA100 Mesh", type=2)
n = len(net)
net.name = "ISA100 Rough Simulation" + "\nN=%s, R=%s m" \
%(n, global_settings.COMM_RANGE)
filename = net.name.replace("\n", "-")
# saving topology as PNG image
net.savefig(fname=get_path(folder, filename), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
show_labels=True, format="png", label_color='yellow')
print net.__len__(), len(net)
# simulation start
net.algorithms = ((Beacon, {'informationKey': 'Data'}),)
coordinator_node = net.nodes()[0]
coordinator_node.memory['Data'] = 'Beacon 1'
sim = Simulation(net)
sim.run()
# simulation ends
# capturing results
consume = []
energy = []
anchpositions.append({'x': net.pos[node][0], 'y': net.pos[node][1],
'name': str(node.id)})
else:
positions.append({'x': net.pos[node][0], 'y': net.pos[node][1],
'name': 'Node: ' + str(node.id)})
newpos.append({'x': newx, 'y': newy,
'name': 'Node: ' + str(node.id)})
xestpositions.append(newx)
deltapos.append(net.pos[node][0] - newx)
avg_deg = round(net.avg_degree())
net.name = "%s - Nodes=%s, Avg degree=%s, Range=%s" \
% (net_gen.name, net.__len__(), int(avg_deg), int(node.commRange))
net.savefig(fname=get_path(TOPOLOGY_DIR, net.name),
title=net.name, format='pdf',
x_label="X", y_label="Y", show_labels=False)
print net.__len__(),avg_deg, node.commRange, n_anchors
end_time = time.time() - start_time
ntx = 345
comments = "Anchors: " + str(n_anchors) +"="+str(p_anchors) +"%"+ \
", Runtime(sec): "+ str(round(end_time,2)) + \
", No. of Tx: " + str(ntx)
plotter.gethtmlScatter(xpositions, [anchpositions, positions, newpos],
fname="Topology-"+net.name, folder=TOPOLOGY_DIR,
xlabel="X", ylabel="Y", labels=['Anchor','Regular','Localized'],
title="Topology-"+net.name, open=1, range={'xmin':0, 'ymin':0, 'xmax': w, 'ymax': h},
comment=comments, show_range=node.commRange,
},
'pymote.simulation':
{
'level': 'INFO',
'handlers': ['simFileHandler'],
'propagate': 1
}
},
'handlers':
{
'fileHandler':
{
'class': 'logging.FileHandler',
'level': 'DEBUG',
'formatter': 'fileFormatter',
'filename': get_path(LOG_DIR, 'pymote.log')
},
'consoleHandler':
{
'class': 'logging.StreamHandler',
'formatter': 'consoleFormatter',
'stream': 'ext://sys.stdout'
},
'simFileHandler':
{
'class': 'logging.FileHandler',
'level': 'DEBUG',
'formatter': 'fileFormatter',
'filename': get_path(LOG_DIR, 'simulation.log')
}
},
# set the network name based on no. of Nodes, degree and Comm. Range
net.name = "%s(%s) - $N=$%s(%s), $D=$%s, $R=$%s m\n" \
"$A=$%s$\\times 10^3m^2$, $N_D=$%s$/10^3.m^2$, $DOI$=%s" \
% (net_gen.name, vary, nn, n_anchors, round(avg_deg, 1),
round(comm_range, 1), round(net_gen.area / 1000.0, 2),
round(net_gen.net_density * 1000, 1), round(doi, 1))
filename = (net.name.split("\n")[0]).replace("$", "")
area = "A: %s x 1000 m^2, ND: %s /1000 m^2, DOI: %s" \
% (round(net_gen.area / 1000.0, 2), round(net_gen.net_density * 1000, 1),
round(doi, 1))
try:
net.savefig(fname=get_path(folder, filename), title=net.name,
xlabel="X-coordinate (m)", ylabel="Y-coordinate (m)",
show_labels=True, format="pdf")
except:
pass
# Now select the algorithm for simulation
net.algorithms = ((algorithm[method][0], {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'dataKey': 'I',
'maxHop': vary
}),
(algorithm[method][1], {'truePositionKey': 'tp',
'hopsizeKey': 'hs',
'positionKey': 'pos',
'dataKey': 'I'}),
connected=False,
commRange=global_settings.COMM_RANGE,
comm_range=global_settings.COMM_RANGE)
net = net_gen.generate_grid_network(name="Grid",
randomness=0,
p_anchors=None)
net, p = net_gen.get_clusters(net, clusters=clusters, method="EM")
node = net.nodes()[int(n / 2)]
net.remove_node(node)
n = len(net)
net.name = "Grid Topology" + "\nN=%s, R=%s m" \
%(n, global_settings.COMM_RANGE)
filename = net.name.replace("\n", "-")
# saving topology as PNG image
net.savefig(fname=get_path(folder, filename),
title=net.name,
xlabel="X-coordinate (m)",
ylabel="Y-coordinate (m)",
show_labels=True,
format="png",
label_color='yellow')
net.name = filename
net.save_json(get_path(folder, filename + ".json"), scale=(1, 1))
print net.__len__(), len(net)
coordinator_node = net.nodes()[n - 1]
coordinator_node.type = 'B'
coordinator_node.compositeSensor = (NeighborsSensor, ) # (TruePosSensor,)
def gethtmlLine(x, y, fname, folder="", axis_range={}, labels=None,
xlabel="X", ylabel="Y", title=None, color='r', comment=".", **kwargs):
title = title or fname
plot_type = 'spline'
if 'plot_type' in kwargs:
plot_type = kwargs.pop('plot_type', 'line')
plot_options = [""]
if 'plot_options' in kwargs:
plot_options = kwargs.pop('plot_options')
show_range = kwargs.pop('show_range', None)
circle = ""
if show_range:
circle = "allowPointSelect:true, marker: {states: {select: {lineColor: 'red', radius: " + str(show_range) + "}}},"
series_data = ''
k = 0
for series in y:
series_data += '''
{
name: "''' + (labels[k] if k < len(labels) else "") + '''",
''' + (plot_options[k] if k < len(plot_options) else "") + '''
data: ''' + str(series) + ''',
''' + circle + '''
},
'''
k += 1
contents = '''<!DOCTYPE html>
<html>
<head>
<meta content="text/html; charset=ISO-8859-1" http-equiv="content-type">
<title>'''+title+'''</title>
<script type='text/javascript' src='http://code.jquery.com/jquery-1.9.1.js'></script>
<style type='text/css'>
</style>
<script type='text/javascript'>//<![CDATA[
var chart;
var w;
$(function () {
w = $('#container').width()
$('#container').highcharts({
credits: {
enabled: false
},
exporting: {
filename: "''' + fname + '''"
},
legend: {
margin: -10, padding: 0, y: -5, //layout: 'vertical', align: 'right', verticalAlign: 'middle', shadow: true, backgroundColor: '#FFFFFF',
title: {
//text: 'Farrukh Shahzad<br/>PhD, KFUPM<br/>Nov. 2015</span>',
style: {fontStyle: 'italic', fontSize: '10px'}
}
},
chart: {
zoomType: 'xy', panning: true, panKey: 'shift',
type: "''' + plot_type + '''"
},
title: {
text: "''' + title + '''"
},
subtitle: {
text: "''' + comment + '''",
style: {color:'black', fontSize: '18px', fontFamily: 'Times'}
},
tooltip: {
borderRadius: 10,
crosshairs: [{width: 1, color: 'gray', dashStyle: 'shortdot'}, {width: 1, color: 'gray', dashStyle: 'shortdot'}],
pointFormat: '<span style="font-weight: bold; color: {series.color}">{series.name}</span>: <b>{point.y:.1f}</b><br />',
headerFormat: '<span style="font-size: 12px">''' + xlabel + ''' {point.key}</span><br />',
shared: true
},
xAxis: { // Primary xAxis
minRange : 5,
min: '''+ json.dumps(axis_range.get('xmin')) + ''', max: '''+ json.dumps(axis_range.get('xmax')) + ''',
title: {text: "'''+ xlabel + '''", y: -10, style: {color:'black'}}
},
yAxis: { // Primary yAxis
lineWidth: 1,
minPadding: 0.0,
maxPadding: 0.0,
min: '''+ json.dumps(axis_range.get('ymin')) + ''', max: '''+ json.dumps(axis_range.get('ymax')) + ''',
title: {text: "'''+ ylabel + '''", x: 10, style: {color:'black'}}
},
series: [''' + series_data + ''']
});
});
//]]>
</script>
</head>
<body>
<script src="http://code.highcharts.com/highcharts.js"></script>
<script src="http://code.highcharts.com/modules/data.js"></script>
<script src="http://code.highcharts.com/highcharts-more.js"></script>
<script src="http://code.highcharts.com/modules/exporting.js"></script>
<div id="container" style="height: 600px; margin: auto; min-width: 600px; max-width: 600px"></div>
</body>
</html>
'''
path = get_path(folder,fname) + ".html"
if 'open' in kwargs:
browseLocal(contents, filename=path)
else:
strToFile(contents, filename=path)