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usernode.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Sep 27 22:28:39 2019
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from matplotlib.lines import Line2D
import networkx as nx
#import random
from random import random, sample, choice
import os
#import winsound
from time import gmtime, strftime
from shutil import copyfile
import csv
from scipy.signal import butter, lfilter, freqz, filtfilt
import pandas as pd
from numpy.core.arrayprint import set_string_function
from numpy.core.records import array
np.random.seed(0)
# Simulation Parameters
MONTE_CARLOS = 1
SIM_TIME = 180
STEP = 0.01
# Network Parameters
GRAPH = 'regular' # regular, complete or cycle
NU = 50
NUM_NODES = 50
USERNUMBER=100
NUM_NEIGHBOURS = 4
START_TIMES = 10*np.ones(NUM_NODES)
NODETX_SIZE= [int(10*(NUM_NODES+1)/((NodeID+1)**0.9)) for NodeID in range(NUM_NODES)]
PROBABILITY= False #DBNS
RANDOM= True #URNS
REPRANDOM= False #RBNS
MIMUDELAY= False
QOS = False #DBNS+
FILTER= True
AVERAGE= False
LimitedSize= False
RE_PEERING= False
# for NodeID in range(NUM_NODES):
# if NODETX_SIZE[NodeID]>=120:
# NODETX_SIZE[NodeID]=120
REPDIST = 'zipf'
if REPDIST=='zipf':
# IOTA data rep distribution - Zipf s=0.9
REP = [(NUM_NODES+1)/((NodeID+1)**0.9) for NodeID in range(NUM_NODES)]
elif REPDIST=='uniform':
# Permissioned System rep system?
REP = np.ones(NUM_NODES, dtype=int)
# Modes: 0 = inactive, 1 = content, 2 = best-effort, 3 = malicious
#MODE = [2-NodeID%3 for NodeID in range(NUM_NODES)] # honest env
MODE = [2, 2, 2, 2, 2, 2, 2, 2, 2 ,2]*5
#MODE = [1, 1, 1, 1, 1, 1, 1, 1, 1 ,1]*5
#MODE = [3-(NodeID+1)%4 for NodeID in range(NUM_NODES)] # malicoius env
#MODE[2]=4
#AVG_WORK = [2*rep/max(REP) for rep in REP
#AVG_WORK = np.ones(NUM_NODES)
#IOT = np.concatenate((np.zeros(int(NUM_NODES/2)), np.ones(int(NUM_NODES/2))))
#IOT = [NodeID%2 for NodeID in range(NUM_NODES)]
IOT = np.zeros(NUM_NODES)
IOTLOW = 0.5
IOTHIGH = 1
MAX_WORK = 10
MODE_COLOUR_MAP = ['grey', 'tab:blue', 'tab:red', 'tab:green', 'tab:green']
# Congestion Control Parameters
ALPHA = 0.075
BETA = 0.5
TAU = 2
MIN_TH = 1
MAX_TH = MIN_TH
QUANTUM = [MAX_WORK*rep/sum(REP) for rep in REP]
W_Q = 0.1
P_B = 0.5
DROP_TH = 10
SCHEDULE_ON_SOLID = True
SOLID_REQUESTS = True
BLACKLIST = True
SCHEDULING = 'drr_lds'
def main():
#plt.close('all')
dirstr = simulate()
#dirstr = 'data/2022-02-12_112013'
plot_results(dirstr)
#plot_results('data/2021-09-20_095913') # honest environment
#plot_results('data/2020-07-26_122609') # malicious environment
#plot_results('data/2020-07-25_154015') # IoT
#plot_scheduler_comp('Data/2020-07-23_233549', 'Data/2020-07-23_213333')
#plot_ratesetter_comp('data/2020-07-24_132945', 'data/2020-07-23_213333', 'data/2020-07-24_153139') # changing beta
#plot_ratesetter_comp('data/2020-07-23_212831', 'data/2020-07-23_213333', 'data/2020-07-24_124532') # changing W
#plot_ratesetter_comp('data/2020-07-23_213056', 'data/2020-07-23_213333', 'data/2020-07-24_154211') # changing alpha
#winsound.Beep(2500, 1000) # beep to say sim is finished
def simulate():
"""
Setup simulation inputs and instantiate output arrays
"""
# seed rng
np.random.seed(0)
TimeSteps = int(SIM_TIME/STEP)
'''
Create empty arrays to store results
'''
Lmds = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
OldestTxAges = np.zeros((TimeSteps, NUM_NODES))
OldestTxAge = []
InboxLens = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
InboxLensMA = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
SolidRequests = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
Deficits = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
Throughput = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
WorkThroughput = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
Undissem = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
MeanDelay = [np.zeros(SIM_TIME) for mc in range(MONTE_CARLOS)]
MeanVisDelay = [np.zeros(SIM_TIME) for mc in range(MONTE_CARLOS)]
TXPool= [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
UserTX= [np.zeros((TimeSteps, USERNUMBER)) for mc in range(MONTE_CARLOS)]
EstTXPoolDelay = [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
ActualTXdelay= [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
FilteredRateRecord= [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
TXdelayError= [np.zeros((TimeSteps, NUM_NODES)) for mc in range(MONTE_CARLOS)]
ScheduledTX= np.zeros((TimeSteps, NUM_NODES))
PerBlacklisted= np.zeros(TimeSteps)
UserDelay = [np.zeros((TimeSteps, USERNUMBER)) for mc in range(MONTE_CARLOS)]
Costfee = [np.zeros((TimeSteps, USERNUMBER)) for mc in range(MONTE_CARLOS)]
TP = []
WTP = []
UserIssuedTX= []
NodeschedulTX= []
latencies = [[] for NodeID in range(NUM_NODES)]
inboxLatencies = [[] for NodeID in range(NUM_NODES)]
latTimes = [[] for NodeID in range(NUM_NODES)]
ServTimes = [[] for NodeID in range(NUM_NODES)]
ArrTimes = [[] for NodeID in range(NUM_NODES)]
interArrTimes = [[] for NodeID in range(NUM_NODES)]
DroppedTrans = [[] for NodeID in range(NUM_NODES)]
DropTimes = [[] for NodeID in range(NUM_NODES)]
"""
Monte Carlo Sims
"""
for mc in range(MONTE_CARLOS):
"""
Generate network topology:
Comment out one of the below lines for either random k-regular graph or a
graph from an adjlist txt file i.e. from the autopeering simulator
"""
if GRAPH=='regular':
G = nx.random_regular_graph(NUM_NEIGHBOURS, NUM_NODES) # random regular graph
elif GRAPH=='complete':
G = nx.complete_graph(NUM_NODES) # complete graph
elif GRAPH=='cycle':
G = nx.cycle_graph(NUM_NODES) # cycle graph
#G = nx.read_adjlist('input_adjlist.txt', delimiter=' ')
# Get adjacency matrix and weight by delay at each channel
ChannelDelays = 0.05*np.ones((NUM_NODES, NUM_NODES))+0.1*np.random.rand(NUM_NODES, NUM_NODES) # not used anymore
AdjMatrix = np.multiply(1*np.asarray(nx.to_numpy_matrix(G)), ChannelDelays)
Net = Network(AdjMatrix) # instantiate the network
for i in range(TimeSteps):
if 100*i/TimeSteps%10==0:
print("Simulation: "+str(mc) +"\t " + str(int(100*i/TimeSteps))+"% Complete")
# discrete time step size specified by global variable STEP
T = STEP*i
"""
The next line is the function which ultimately calls all others
and runs the simulation for a time step
"""
Net.simulate(T)
'''
save summary results in output arrays Users
'''
for NodeID in range(NUM_NODES):
Lmds[mc][i, NodeID] = Net.Nodes[NodeID].Lambda
if Net.Nodes[NodeID].Inbox.AllPackets and MODE[NodeID]<3: #don't include malicious nodes
HonestPackets = [p for p in Net.Nodes[NodeID].Inbox.AllPackets if MODE[p.Data.NodeID]<3]
if HonestPackets:
OldestPacket = min(HonestPackets, key=lambda x: x.Data.IssueTime)
OldestTxAges[i,NodeID] = T - OldestPacket.Data.IssueTime
if Net.Nodes[2].Neighbours!=[]:
InboxLens[mc][i, NodeID] = len(Net.Nodes[2].Neighbours[0].Inbox.Packets[NodeID])/REP[NodeID]
InboxLensMA[mc][i,NodeID] = len(Net.Nodes[2].Neighbours[0].Neighbours[-1].Inbox.Packets[NodeID])/REP[NodeID]
SolidRequests[mc][i,NodeID] = len(Net.Nodes[NodeID].Inbox.RequestedTrans)
Deficits[mc][i, NodeID] = Net.Nodes[6].Inbox.Deficit[NodeID]
Throughput[mc][i, NodeID] = Net.Throughput[NodeID]
WorkThroughput[mc][i,NodeID] = Net.WorkThroughput[NodeID]
Undissem[mc][i,NodeID] = Net.Nodes[NodeID].Undissem
TXPool[mc][i, NodeID] = len(Net.Nodes[NodeID].UserTX)
EstTXPoolDelay[mc][i, NodeID] = Net.Nodes[NodeID].Estdelay
ActualTXdelay[mc][i, NodeID]= Net.Nodes[NodeID].ActualTXdelay
FilteredRateRecord[mc][i, NodeID]= Net.Nodes[NodeID].FilteredRateRecord
TXdelayError[mc][i, NodeID]= Net.Nodes[NodeID].TXdelayError
Costfee[mc][i, NodeID]= Net.Nodes[NodeID].Fee
ScheduledTX[i, NodeID]+= Net.Nodes[NodeID].Inbox.ScheduledTX
for NodeID in range(USERNUMBER):
UserTX[mc][i, NodeID] = Net.IssuedTX[NodeID]
UserDelay[mc][i, NodeID]= Net.Users[NodeID].User_lastdelay
Blacklist = [Net.Nodes[NodeID].Blacklist for NodeID in range(NUM_NODES)]
NumBlacklisted=[i for i,x in enumerate(Blacklist) if x==[2]]
PerBlacklisted[i]=len(NumBlacklisted)/NUM_NODES
print("Simulation: "+str(mc) +"\t 100% Complete")
OldestTxAge.append(np.mean(OldestTxAges, axis=1))
for NodeID in range(NUM_NODES):
for i in range(SIM_TIME):
delays = [Net.TranDelays[j] for j in range(len(Net.TranDelays)) if int(Net.DissemTimes[j])==i]
if delays:
MeanDelay[mc][i] = sum(delays)/len(delays)
visDelays = [Net.VisTranDelays[j] for j in range(len(Net.VisTranDelays)) if int(Net.DissemTimes[j])==i]
if visDelays:
MeanVisDelay[mc][i] = sum(visDelays)/len(visDelays)
ServTimes[NodeID] = sorted(Net.Nodes[NodeID].ServiceTimes)
ArrTimes[NodeID] = sorted(Net.Nodes[NodeID].ArrivalTimes)
ArrWorks = [x for _,x in sorted(zip(Net.Nodes[NodeID].ArrivalTimes,Net.Nodes[NodeID].ArrivalWorks))]
interArrTimes[NodeID].extend(np.diff(ArrTimes[NodeID])/ArrWorks[1:])
inboxLatencies[NodeID].extend(Net.Nodes[NodeID].InboxLatencies)
DroppedTrans[NodeID].extend(Net.Nodes[NodeID].Inbox.DroppedTrans)
DropTimes[NodeID].extend(Net.Nodes[NodeID].Inbox.DropTimes)
Blacklist = [Net.Nodes[NodeID].Blacklist for NodeID in range(NUM_NODES)]
latencies, latTimes = Net.tran_latency(latencies, latTimes)
window = 10
TP.append(np.concatenate((np.zeros((int(window/STEP), NUM_NODES)),(Throughput[mc][int(window/STEP):,:]-Throughput[mc][:-int(window/STEP),:])))/window)
WTP.append(np.concatenate((np.zeros((int(window/STEP), NUM_NODES)),(WorkThroughput[mc][int(window/STEP):,:]-WorkThroughput[mc][:-int(window/STEP),:])))/window)
UserIssuedTX.append(np.concatenate((np.zeros((int(window/STEP), USERNUMBER)),(UserTX[mc][int(window/STEP):,:]-UserTX[mc][:-int(window/STEP),:])))/window)
NodeschedulTX.append(np.concatenate((np.zeros((int(window/STEP), NUM_NODES)),(ScheduledTX[int(window/STEP):,:]-ScheduledTX[:-int(window/STEP),:])))/window)
#TP.append(np.convolve(np.zeros((Throughput[mc][500:,:]-Throughput[mc][:-500,:])))/5)
Neighbours = [[Neighb.NodeID for Neighb in Node.Neighbours] for Node in Net.Nodes]
del Net
"""
Get results
"""
avgLmds = sum(Lmds)/len(Lmds)
avgTP = sum(TP)/len(TP)
avgWTP = sum(WTP)/len(WTP)
avgInboxLen = sum(InboxLens)/len(InboxLens)
avgInboxLenMA = sum(InboxLensMA)/len(InboxLensMA)
avgSolReq = sum(SolidRequests)/len(SolidRequests)
avgDefs = sum(Deficits)/len(Deficits)
avgUndissem = sum(Undissem)/len(Undissem)
avgMeanDelay = sum(MeanDelay)/len(MeanDelay)
avgMeanVisDelay = sum(MeanVisDelay)/len(MeanVisDelay)
avgOTA = sum(OldestTxAge)/len(OldestTxAge)
avgTXPool= sum(TXPool)/len(TXPool)
avguserTX= sum(UserIssuedTX)/len(UserIssuedTX)
avgNodeschedulTX= sum(NodeschedulTX)/len(NodeschedulTX)
avgEstTXPoolDelay= sum(EstTXPoolDelay)/len(EstTXPoolDelay)
AvgActualTXdelay= sum(ActualTXdelay)/len(ActualTXdelay)
AvgFilteredRateRecord= sum(FilteredRateRecord)/len(FilteredRateRecord)
AvgTXdelayError= sum(TXdelayError)/len(TXdelayError)
AvgUserDelay = sum(UserDelay)/len(UserDelay)
AvgCostfee = sum(Costfee)/len(Costfee)
"""
Create a directory for these results and save them
"""
dirstr = 'data/'+ strftime("%Y-%m-%d_%H%M%S", gmtime())
os.makedirs(dirstr, exist_ok=True)
pos=nx.spring_layout(G)
nx.draw_networkx_nodes(G,pos,
nodelist=range(NUM_NODES),
node_color=[MODE_COLOUR_MAP[MODE[NodeID]] for NodeID in range(NUM_NODES)],
node_size=200,
alpha=0.8)
nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)
mng = plt.get_current_fig_manager()
#mng.window.showMaximized()
plt.axis('off')
plt.savefig(dirstr+'/Graph.png', bbox_inches='tight')
np.savetxt(dirstr+'/aaconfig.txt', ['MCs = ' + str(MONTE_CARLOS) +
'\nsimtime = ' + str(SIM_TIME) +
'\nstep = ' + str(STEP) +
'\n\n# Network Parameters' +
'\nnu = ' + str(NU) +
'\ngraph type = ' + GRAPH +
'\nnumber of nodes = ' + str(NUM_NODES) +
'\nnumber of neighbours = ' + str(NUM_NEIGHBOURS) +
'\nrepdist = ' + str(REPDIST) +
'\nmodes = ' + str(MODE) +
'\niot = ' + str(IOT) +
'\niotlow = ' + str(IOTLOW) +
'\niothigh = ' + str(IOTHIGH) +
'\ndcmax = ' + str(MAX_WORK) +
'\n\n# Congestion Control Parameters' +
'\nalpha = ' + str(ALPHA) +
'\nbeta = ' + str(BETA) +
'\ntau = ' + str(TAU) +
'\nminth = ' + str(MIN_TH) +
'\nmaxth = ' + str(MAX_TH) +
'\nquantum = ' + str(QUANTUM) +
'\nw_q = ' + str(W_Q) +
'\np_b = ' + str(P_B) +
'\nSchedule on solid = ' + str(SCHEDULE_ON_SOLID) +
'\nsolidification requests = ' + str(SOLID_REQUESTS) +
'\nblacklist = ' + str(BLACKLIST) +
'\nPROBABILITY = ' + str(PROBABILITY) +
'\nRANDOM = ' + str(RANDOM) +
'\nREPRANDOM = ' + str(REPRANDOM) +
'\nMIMUDELAY = ' + str(MIMUDELAY) +
'\nQOS = ' + str(QOS) +
'\nsched=' + SCHEDULING], delimiter = " ", fmt='%s')
np.savetxt(dirstr+'/avgLmds.csv', avgLmds, delimiter=',')
np.savetxt(dirstr+'/avgTP.csv', avgTP, delimiter=',')
np.savetxt(dirstr+'/avgWTP.csv', avgWTP, delimiter=',')
np.savetxt(dirstr+'/avgInboxLen.csv', avgInboxLen, delimiter=',')
np.savetxt(dirstr+'/avgInboxLenMA.csv', avgInboxLenMA, delimiter=',')
np.savetxt(dirstr+'/avgSolReq.csv', avgSolReq, delimiter=',')
np.savetxt(dirstr+'/avgDefs.csv', avgDefs, delimiter=',')
np.savetxt(dirstr+'/avgUndissem.csv', avgUndissem, delimiter=',')
np.savetxt(dirstr+'/avgMeanDelay.csv', avgMeanDelay, delimiter=',')
np.savetxt(dirstr+'/avgMeanVisDelay.csv', avgMeanVisDelay, delimiter=',')
np.savetxt(dirstr+'/avgOldestTxAge.csv', avgOTA, delimiter=',')
np.savetxt(dirstr+'/avgTXPool.csv', avgTXPool, delimiter=',')
np.savetxt(dirstr+'/avguserTX.csv', avguserTX, delimiter=',')
np.savetxt(dirstr+'/avgNodeschedulTX.csv', avgNodeschedulTX, delimiter=',')
np.savetxt(dirstr+'/avgEstTXPoolDelay.csv', avgEstTXPoolDelay, delimiter=',')
np.savetxt(dirstr+'/AvgActualTXdelay.csv', AvgActualTXdelay, delimiter=',')
np.savetxt(dirstr+'/AvgFilteredRateRecord.csv', AvgFilteredRateRecord, delimiter=',')
np.savetxt(dirstr+'/AvgTXdelayError.csv', AvgTXdelayError, delimiter=',')
np.savetxt(dirstr+'/PerBlacklisted.csv', PerBlacklisted, delimiter=',')
np.savetxt(dirstr+'/AvgUserDelay.csv', AvgUserDelay, delimiter=',')
np.savetxt(dirstr+'/AvgCostfee.csv', AvgCostfee, delimiter=',')
#np.savetxt(dirstr+'/adjlist.csv', np.asarray(Neighbours), delimiter=',')
f = open(dirstr+'/blacklist.csv','w')
with f:
writer = csv.writer(f)
for row in Blacklist:
if row:
writer.writerow(row)
else:
writer.writerow('None')
for NodeID in range(NUM_NODES):
np.savetxt(dirstr+'/latencies'+str(NodeID)+'.csv',
np.asarray(latencies[NodeID]), delimiter=',')
'''
np.savetxt(dirstr+'/inboxLatencies'+str(NodeID)+'.csv',
np.asarray(inboxLatencies[NodeID]), delimiter=',')
np.savetxt(dirstr+'/ServTimes'+str(NodeID)+'.csv',
np.asarray(ServTimes[NodeID]), delimiter=',')
np.savetxt(dirstr+'/ArrTimes'+str(NodeID)+'.csv',
np.asarray(ArrTimes[NodeID]), delimiter=',')
'''
if DroppedTrans[NodeID]:
Drops = np.zeros((len(DroppedTrans[NodeID]), 3))
for i in range(len(DroppedTrans[NodeID])):
Drops[i, 0] = DroppedTrans[NodeID][i].NodeID
Drops[i, 1] = DroppedTrans[NodeID][i].Index
Drops[i, 2] = DropTimes[NodeID][i]
np.savetxt(dirstr+'/Drops'+str(NodeID)+'.csv', Drops, delimiter=',')
return dirstr
def plot_ratesetter_comp(dir1, dir2, dir3):
fig, ax = plt.subplots(figsize=(8,4))
ax.grid(linestyle='--')
ax.set_xlabel('Time (sec)')
axt = ax.twinx()
ax.tick_params(axis='y', labelcolor='black')
axt.tick_params(axis='y', labelcolor='tab:gray')
ax.set_ylabel('Dissemination rate (Work/sec)', color='black')
axt.set_ylabel('Mean Latency (sec)', color='tab:gray')
avgTP1 = np.loadtxt(dir1+'/avgTP.csv', delimiter=',')
avgMeanDelay1 = np.loadtxt(dir1+'/avgMeanDelay.csv', delimiter=',')
avgTP2 = np.loadtxt(dir2+'/avgTP.csv', delimiter=',')
avgMeanDelay2 = np.loadtxt(dir2+'/avgMeanDelay.csv', delimiter=',')
avgTP3 = np.loadtxt(dir3+'/avgTP.csv', delimiter=',')
avgMeanDelay3 = np.loadtxt(dir3+'/avgMeanDelay.csv', delimiter=',')
markerevery = 200
ax.plot(np.arange(10, SIM_TIME, STEP), np.sum(avgTP1[1000:,:], axis=1), color = 'black', marker = 'o', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay1, color='tab:gray', marker = 'o', markevery=int(markerevery*STEP))
ax.plot(np.arange(10, SIM_TIME, STEP), np.sum(avgTP2[1000:,:], axis=1), color = 'black', marker = 'x', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay2, color='tab:gray', marker = 'x', markevery=int(markerevery*STEP))
ax.plot(np.arange(10, SIM_TIME, STEP), np.sum(avgTP3[1000:,:], axis=1), color = 'black', marker = '^', markevery=markerevery)
axt.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay3, color='tab:gray', marker = '^', markevery=int(markerevery*STEP))
ModeLines = [Line2D([0],[0],color='black', linestyle=None, marker='o'), Line2D([0],[0],color='black', linestyle=None, marker='x'), Line2D([0],[0],color='black', linestyle=None, marker='^')]
#ax.legend(ModeLines, [r'$A=0.05$', r'$A=0.075$', r'$A=0.1$'], loc='lower right')
#ax.set_title(r'$\beta=0.7, \quad W=2$')
ax.legend(ModeLines, [r'$\beta=0.5$', r'$\beta=0.7$', r'$\beta=0.9$'], loc='lower right')
ax.set_title(r'$A=0.075, \quad W=2$')
#ax.legend(ModeLines, [r'$W=1$', r'$W=2$', r'$W=3$'], loc='lower right')
#ax.set_title(r'$A=0.075, \quad \beta=0.7$')
dirstr = 'data/'+ strftime("%Y-%m-%d_%H%M%S", gmtime())
os.makedirs(dirstr, exist_ok=True)
copyfile(dir1+'/aaconfig.txt', dirstr+'/config1.txt')
copyfile(dir2+'/aaconfig.txt', dirstr+'/config2.txt')
copyfile(dir3+'/aaconfig.txt', dirstr+'/config3.txt')
fig.tight_layout()
plt.savefig(dirstr+'/Throughput.png', bbox_inches='tight')
def plot_scheduler_comp(dir1, dir2):
latencies1 = []
for NodeID in range(NUM_NODES):
if os.stat(dir1+'/latencies'+str(NodeID)+'.csv').st_size != 0:
lat = [np.loadtxt(dir1+'/latencies'+str(NodeID)+'.csv', delimiter=',')]
else:
lat = [0]
latencies1.append(lat)
latencies2 = []
for NodeID in range(NUM_NODES):
if os.stat(dir2+'/latencies'+str(NodeID)+'.csv').st_size != 0:
lat = [np.loadtxt(dir2+'/latencies'+str(NodeID)+'.csv', delimiter=',')]
else:
lat = [0]
latencies2.append(lat)
fig, ax = plt.subplots(2,1, sharex=True, figsize=(8,4))
ax[0].grid(linestyle='--')
ax[1].grid(linestyle='--')
ax[1].set_xlabel('Latency (sec)')
ax[0].set_title('DRR')
ax[1].set_title('DRR-')
xlim = plot_cdf(latencies1, ax[0])
plot_cdf(latencies2, ax[1], xlim)
dirstr = 'data/'+ strftime("%Y-%m-%d_%H%M%S", gmtime())
os.makedirs(dirstr, exist_ok=True)
copyfile(dir1+'/aaconfig.txt', dirstr+'/config1.txt')
copyfile(dir2+'/aaconfig.txt', dirstr+'/config2.txt')
plt.savefig(dirstr+'/LatencyComp.png', bbox_inches='tight')
def plot_results(dirstr):
"""
Initialise plots
"""
plt.close('all')
"""
Load results from the data directory
"""
avgLmds = np.loadtxt(dirstr+'/avgLmds.csv', delimiter=',')
#avgTP = np.loadtxt(dirstr+'/avgTP.csv', delimiter=',')
avgTP = np.loadtxt(dirstr+'/avgWTP.csv', delimiter=',')
avgInboxLen = np.loadtxt(dirstr+'/avgInboxLen.csv', delimiter=',')
avgInboxLenMA = np.loadtxt(dirstr+'/avgInboxLenMA.csv', delimiter=',')
avgSolReq = np.loadtxt(dirstr+'/avgSolReq.csv', delimiter=',')
avgUndissem = np.loadtxt(dirstr+'/avgUndissem.csv', delimiter=',')
avgMeanDelay = np.loadtxt(dirstr+'/avgMeanDelay.csv', delimiter=',')
avgOTA = np.loadtxt(dirstr+'/avgOldestTxAge.csv', delimiter=',')
avgDefs = np.loadtxt(dirstr+'/avgDefs.csv', delimiter=',')
avgTXPool = np.loadtxt(dirstr+'/avgTXPool.csv', delimiter=',')
avguserTX = np.loadtxt(dirstr+'/avguserTX.csv', delimiter=',')
avgNodeschedulTX = np.loadtxt(dirstr+'/avgNodeschedulTX.csv', delimiter=',')
avgEstTXPoolDelay = np.loadtxt(dirstr+'/avgEstTXPoolDelay.csv', delimiter=',')
AvgActualTXdelay = np.loadtxt(dirstr+'/AvgActualTXdelay.csv', delimiter=',')
AvgFilteredRateRecord= np.loadtxt(dirstr+'/AvgFilteredRateRecord.csv', delimiter=',')
AvgTXdelayError= np.loadtxt(dirstr+'/AvgTXdelayError.csv', delimiter=',')
PerBlacklisted= np.loadtxt(dirstr+'/PerBlacklisted.csv', delimiter=',')
AvgUserDelay = np.loadtxt(dirstr+'/AvgUserDelay.csv', delimiter=',')
AvgCostfee = np.loadtxt(dirstr+'/AvgCostfee.csv', delimiter=',')
latencies = []
ServTimes = []
ArrTimes = []
for NodeID in range(NUM_NODES):
if os.stat(dirstr+'/latencies'+str(NodeID)+'.csv').st_size != 0:
lat = [np.loadtxt(dirstr+'/latencies'+str(NodeID)+'.csv', delimiter=',')]
else:
lat = [0]
latencies.append(lat)
'''
if os.stat(dirstr+'/InboxLatencies'+str(NodeID)+'.csv').st_size != 0:
inbLat = [np.loadtxt(dirstr+'/inboxLatencies'+str(NodeID)+'.csv', delimiter=',')]
else:
inbLat = [0]
inboxLatencies.append(inbLat)
'''
#ServTimes.append([np.loadtxt(dirstr+'/ServTimes'+str(NodeID)+'.csv', delimiter=',')])
#ArrTimes.append([np.loadtxt(dirstr+'/ArrTimes'+str(NodeID)+'.csv', delimiter=',')])
"""
Plot results
"""
window = 10
fig1, ax1 = plt.subplots(2,1, sharex=True, figsize=(8,8))
ax1[0].title.set_text('Dissemination Rate')
ax1[1].title.set_text('Scaled Dissemination Rate')
ax1[0].grid(linestyle='--')
ax1[1].grid(linestyle='--')
ax1[1].set_xlabel('Time (sec)')
#ax1[0].set_ylabel(r'${\lambda_i} / {\~{\lambda}_i}$')
ax1[0].set_ylabel(r'$DR_i$')
ax1[1].set_ylabel(r'$DR_i / {\~{\lambda}_i}$')
mal = False
iot = False
for NodeID in range(NUM_NODES):
if IOT[NodeID]:
iot = True
marker = 'x'
else:
marker = None
if MODE[NodeID]==1:
ax1[0].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:blue', marker=marker, markevery=0.1)
ax1[1].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID]*sum(REP)/(NU*REP[NodeID]), linewidth=5*REP[NodeID]/REP[0], color='tab:blue', marker=marker, markevery=0.1)
if MODE[NodeID]==2:
ax1[0].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:red', marker=marker, markevery=0.1)
ax1[1].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID]*sum(REP)/(NU*REP[NodeID]), linewidth=5*REP[NodeID]/REP[0], color='tab:red', marker=marker, markevery=0.1)
if MODE[NodeID]>2:
ax1[0].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:green', marker=marker, markevery=0.1)
ax1[1].plot(np.arange(window, SIM_TIME, STEP), avgTP[int(window/STEP):,NodeID]*sum(REP)/(NU*REP[NodeID]), linewidth=5*REP[NodeID]/REP[0], color='tab:green', marker=marker, markevery=0.1)
mal = True
if mal:
ModeLines = [Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:red', lw=4), Line2D([0],[0],color='tab:green', lw=4)]
fig1.legend(ModeLines, ['Content','Best-effort', 'Malicious'], loc='right')
elif iot:
ModeLines = [Line2D([0],[0],color='tab:blue'), Line2D([0],[0],color='tab:red'), Line2D([0],[0],color='tab:blue', marker='x'), Line2D([0],[0],color='tab:red', marker='x')]
fig1.legend(ModeLines, ['Content value node','Best-effort value node', 'Content IoT node', 'Best-effort IoT node'], loc='right')
else:
ModeLines = [Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:red', lw=4)]
fig1.legend(ModeLines, ['Content','Best-effort'], loc='right')
plt.savefig(dirstr+'/Rates.png', bbox_inches='tight')
fig2, ax2 = plt.subplots(figsize=(8,4))
ax2.grid(linestyle='--')
ax2.set_xlabel('Time (sec)')
ax2.plot(np.arange(window, SIM_TIME, STEP), np.sum(avgTP[int(window/STEP):,:], axis=1), color = 'black')
ax2.set_ylim((0,1.1*NU))
ax22 = ax2.twinx()
ax22.plot(np.arange(0, SIM_TIME, 1), avgMeanDelay, color='tab:gray')
ax2.tick_params(axis='y', labelcolor='black')
ax22.tick_params(axis='y', labelcolor='tab:gray')
ax2.set_ylabel('Dissemination rate (Work/sec)', color='black')
ax22.set_ylabel('Mean Latency (sec)', color='tab:gray')
fig2.tight_layout()
plt.savefig(dirstr+'/Throughput.png', bbox_inches='tight')
fig3, ax3 = plt.subplots(figsize=(8,4))
ax3.grid(linestyle='--')
ax3.set_xlabel('Latency (sec)')
plot_cdf(latencies, ax3)
plt.savefig(dirstr+'/Latency.png', bbox_inches='tight')
'''
fig3a, ax3a = plt.subplots(figsize=(8,4))
ax3a.grid(linestyle='--')
ax3a.set_xlabel('Inbox Latency (sec)')
plot_cdf(inboxLatencies, ax3a)
plt.savefig(dirstr+'/InboxLatency.png', bbox_inches='tight')
'''
fig4, ax4 = plt.subplots(figsize=(8,4))
ax4.grid(linestyle='--')
ax4.set_xlabel('Time (sec)')
ax4.set_ylabel(r'$\lambda_i$')
#ax4.plot(np.arange(0, SIM_TIME, STEP), np.sum(avgLmds, axis=1), color='tab:blue')
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax4.plot(np.arange(0, SIM_TIME, STEP), avgLmds[:,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:blue')
if MODE[NodeID]==2:
ax4.plot(np.arange(0, SIM_TIME, STEP), avgLmds[:,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:red')
if MODE[NodeID]==3 or MODE[NodeID]==4:
ax4.plot(np.arange(0, SIM_TIME, STEP), avgLmds[:,NodeID], linewidth=5*REP[NodeID]/REP[0], color='tab:green')
plt.savefig(dirstr+'/IssueRates.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('Reputation-scaled inbox length (neighbour)')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/AvgInboxLen.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('Reputation-scaled inbox length (neighbour of neighbour)')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLenMA[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLenMA[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLenMA[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/AvgInboxLenMA.png', bbox_inches='tight')
"""
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('Deficits at Node 6')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgSolReq[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgSolReq[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgSolReq[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/AvgSolReq.png', bbox_inches='tight')
"""
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('Deficits at Node 6')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange(0, SIM_TIME, STEP), avgDefs[:,NodeID], color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange(0, SIM_TIME, STEP), avgDefs[:,NodeID], color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange(0, SIM_TIME, STEP), avgDefs[:,NodeID], color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/avgDefs.png', bbox_inches='tight')
'''
fig5a, ax5a = plt.subplots(figsize=(8,4))
ax5a.grid(linestyle='--')
ax5a.set_xlabel('Time (sec)')
ax5a.set_ylabel('Inbox Len and Arrivals')
NodeID = 2
step = 1
bins = np.arange(0, SIM_TIME, step)
i = 0
j = 0
nArr = np.zeros(len(bins))
inboxLen = np.zeros(len(bins))
for b, t in enumerate(bins):
if b>0:
inboxLen[b] = inboxLen[b-1]
while ArrTimes[NodeID][0][i] < t+step:
nArr[b] += 1
inboxLen[b] +=1
i += 1
if i>=len(ArrTimes[NodeID][0]):
break
while ServTimes[NodeID][0][j] < t+step:
inboxLen[b] -= 1
j += 1
if j>=len(ServTimes[NodeID][0]):
break
ax5a.plot(bins, nArr/(step*NU), color = 'black')
ax5b = ax5a.twinx()
ax5b.plot(bins, inboxLen, color='blue')
plt.savefig(dirstr+'/InboxLenMA.png', bbox_inches='tight')
'''
fig6, ax6 = plt.subplots(figsize=(8,4))
ax6.grid(linestyle='--')
ax6.set_xlabel('Node ID')
ax6.title.set_text('Reputation Distribution')
ax6.set_ylabel('Reputation')
for NodeID in range(NUM_NODES):
if MODE[NodeID]==0:
ax6.bar(NodeID, REP[NodeID], color='gray')
if MODE[NodeID]==1:
ax6.bar(NodeID, REP[NodeID], color='tab:blue')
if MODE[NodeID]==2:
ax6.bar(NodeID, REP[NodeID], color='tab:red')
if MODE[NodeID]>2:
ax6.bar(NodeID, REP[NodeID], color='tab:green')
ModeLines = [Line2D([0],[0],color='tab:red', lw=4), Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='gray', lw=4), Line2D([0],[0],color='tab:green', lw=4)]
ax6.legend(ModeLines, ['Best-effort', 'Content', 'Inactive', 'Malicious'], loc='upper right')
plt.savefig(dirstr+'/RepDist.png', bbox_inches='tight')
'''
fig7, ax7 = plt.subplots(figsize=(8,4))
plot_cdf(ISTimes, ax7)
ax7.grid(linestyle='--')
ax7.set_xlabel('Inter-service time (sec)')
plt.savefig(dirstr+'/InterServiceTimes.png', bbox_inches='tight')
'''
fig9, ax9 = plt.subplots(figsize=(8,4))
ax9.grid(linestyle='--')
ax9.plot(np.arange(0, SIM_TIME, STEP), avgOTA, color='black')
ax9.set_ylabel('Max time in transit (sec)')
ax9.set_xlabel('Time (sec)')
plt.savefig(dirstr+'/MaxAge.png', bbox_inches='tight')
plt.savefig(dirstr+'/AvgInboxLen.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('avgTXPool')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgTXPool[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgTXPool[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgTXPool[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/avgTXPool.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('AvgCostfee')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgCostfee[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgCostfee[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgCostfee[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax5.set_xlim(0, SIM_TIME)
plt.savefig(dirstr+'/AvgCostfee.png', bbox_inches='tight')
# fig10, ax10 = plt.subplots(figsize=(8,4))
# ax10.grid(linestyle='--')
# ax10.set_xlabel('Time (sec)')
# ax10.plot(np.arange(window, SIM_TIME, STEP), np.sum(avguserTX[int(window/STEP):,:], axis=1), color = 'black')
# #ax10.set_ylim((0,1.1*NU))
# ax10.plot(np.arange(0, SIM_TIME, STEP), [50 for i in range(int(SIM_TIME/STEP))], color='tab:gray')
# ax10.tick_params(axis='y', labelcolor='black')
# ax10.set_ylabel('user issuing rate/ Scheduling rate', color='black')
# fig10.tight_layout()
# plt.savefig(dirstr+'User_demand.png', bbox_inches='tight')
fig10, ax10 = plt.subplots(figsize=(8,4))
ax10.grid(linestyle='--')
ax10.set_xlabel('Time (sec)')
l1= ax10.plot(np.arange(window, SIM_TIME, STEP), np.sum(avguserTX[int(window/STEP):,:], axis=1), color = 'black')
#ax10.set_ylim((0,1.1*NU))
l2= ax10.plot(np.arange(window, SIM_TIME, STEP), avgNodeschedulTX[int(window/STEP):,:], color='tab:gray')
ax10.tick_params(axis='y', labelcolor='black')
ax10.set_ylabel(' Rate', color='black')
fig10.tight_layout()
ModeLines = [Line2D([0],[0],color='black', lw=2), Line2D([0],[0],color='gray', lw=2, linestyle="--")]
ax10.set_ylim(0, 70)
ax10.legend(ModeLines, ['Sending', 'Scheduling'], loc='lower right')
plt.savefig(dirstr+'User_demand1.png', bbox_inches='tight')
fig10, ax10 = plt.subplots(figsize=(8,4))
ax10.grid(linestyle='--')
ax10.set_xlabel('Time (sec)')
l1= ax10.plot(np.arange(window, SIM_TIME, STEP), np.sum(avguserTX[int(window/STEP):,:], axis=1), color = 'black')
#ax10.set_ylim((0,1.1*NU))
l2= ax10.plot(np.arange(window, SIM_TIME, STEP), [50 for i in range(int((SIM_TIME-window)/STEP))], color='tab:gray', linestyle="--")
ax10.tick_params(axis='y', labelcolor='black')
ax10.set_ylabel(' Rate (sec)', color='black')
fig10.tight_layout()
ModeLines = [Line2D([0],[0],color='black', lw=2), Line2D([0],[0],color='gray', lw=2, linestyle="--")]
ax10.set_ylim(0, 70)
ax10.legend(ModeLines, ['Sending', 'Scheduling'], loc='lower right')
plt.savefig(dirstr+'User_demand2.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('EstTXPoolDelay')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgEstTXPoolDelay[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgEstTXPoolDelay[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgEstTXPoolDelay[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
#ax5.set_ylim(0, 120)
plt.savefig(dirstr+'/EstTXPoolDelay.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('AvgUserDelay')
N=100
for NodeID in range(USERNUMBER):
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgUserDelay[:,NodeID], 'valid'))
#ax5.set_ylim(0, 120)
plt.savefig(dirstr+'/AvgUserDelay.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('AvgActualTXdelay')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgActualTXdelay[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgActualTXdelay[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgActualTXdelay[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
#ax5.set_ylim(0, 120)
plt.savefig(dirstr+'/AvgActualTXdelay.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('TXdelayError')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgTXdelayError[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgTXdelayError[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgTXdelayError[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
#ax5.set_ylim(0, 120)
plt.savefig(dirstr+'/TXdelayError.png', bbox_inches='tight')
fig5, ax5 = plt.subplots(figsize=(8,4))
ax5.grid(linestyle='--')
ax5.set_xlabel('Time (sec)')
ax5.set_ylabel('AvgFilteredRateRecord')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgFilteredRateRecord[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgFilteredRateRecord[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax5.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, AvgFilteredRateRecord[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
#ax5.set_ylim(0, 120)
plt.savefig(dirstr+'/AvgFilteredRateRecord.png', bbox_inches='tight')
fig6, ax6 = plt.subplots(figsize=(8,4))
ax6.grid(linestyle='--')
ax6.set_xlabel('Time (sec)')
N=100
for NodeID in range(NUM_NODES):
if MODE[NodeID]==1:
ax6.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:blue', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]==2:
ax6.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:red', linewidth=5*REP[NodeID]/REP[0])
if MODE[NodeID]>2:
ax6.plot(np.arange((N-1)*STEP, SIM_TIME, STEP), np.convolve(np.ones(N)/N, avgInboxLen[:,NodeID], 'valid'), color='tab:green', linewidth=5*REP[NodeID]/REP[0])
ax6.set_xlim(0, SIM_TIME)
ax62 = ax6.twinx()
ax62.plot(np.arange(0, SIM_TIME, STEP), PerBlacklisted, color='tab:gray')
ax6.tick_params(axis='y', labelcolor='black')
ax62.tick_params(axis='y', labelcolor='tab:gray')
ax6.set_ylabel('Reputation-scaled inbox length (neighbour)')
ax62.set_ylabel('Percent of blacklisting nodes', color='tab:gray')
fig6.tight_layout()
plt.savefig(dirstr+'/Reputation-scaled inbox length (neighbour)-Percent of blacklisted nodes.png', bbox_inches='tight')
def plot_cdf(data, ax, xlim=0):
step = STEP/10
maxval = 0
for NodeID in range(NUM_NODES):
val = np.max(data[NodeID][0])
if val>maxval:
maxval = val
maxval = max(maxval, xlim)
Lines = [[] for NodeID in range(NUM_NODES)]
mal = False
iot = False
for NodeID in range(NUM_NODES):
if MODE[NodeID]>0:
bins = np.arange(0, round(maxval*1/step), 1)*step
pdf = np.zeros(len(bins))
i = 0
if isinstance(data[NodeID][0], np.ndarray):
if data[NodeID][0].size>1:
lats = sorted(data[NodeID][0])
for lat in lats:
while i<len(bins):
if lat>=bins[i]:
i += 1
else:
break
pdf[i-1] += 1
pdf = pdf/sum(pdf) # normalise
cdf = np.cumsum(pdf)
if IOT[NodeID]:
iot = True
marker = 'x'
else:
marker = None
if MODE[NodeID]==1:
Lines[NodeID] = ax.plot(bins, cdf, color='tab:blue', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.3)
if MODE[NodeID]==2:
Lines[NodeID] = ax.plot(bins, cdf, color='tab:red', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.3)
if MODE[NodeID]>2:
mal = True
Lines[NodeID] = ax.plot(bins, cdf, color='tab:green', linewidth=4*REP[NodeID]/REP[0], marker=marker, markevery=0.3)
if mal:
ModeLines = [Line2D([0],[0],color='tab:red', lw=4), Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:green', lw=4)]
ax.legend(ModeLines, ['Best-effort', 'Content','Malicious'], loc='lower right')
elif iot:
ModeLines = [Line2D([0],[0],color='tab:blue'), Line2D([0],[0],color='tab:red'), Line2D([0],[0],color='tab:blue', marker='x'), Line2D([0],[0],color='tab:red', marker='x')]
ax.legend(ModeLines, ['Content value node','Best-effort value node', 'Content IoT node', 'Best-effort IoT node'], loc='lower right')
else:
ModeLines = [Line2D([0],[0],color='tab:blue', lw=4), Line2D([0],[0],color='tab:red', lw=4)]
ax.legend(ModeLines, ['Content','Best-effort'], loc='lower right')
return maxval
def plot_cdf_exp(data, ax):
step = STEP/10
maxval = 0
for NodeID in range(NUM_NODES):
if len(data[NodeID][0])>0:
val = np.max(data[NodeID][0])
else:
val = 0
if val>maxval:
maxval = val
for NodeID in range(len(data)):
if MODE[NodeID]>0:
bins = np.arange(0, round(maxval*1/step), 1)*step
pdf = np.zeros(len(bins))
i = 0
lats = sorted(data[NodeID][0])
for lat in lats:
while i<len(bins):
if lat>=bins[i]:
i += 1
else:
break
pdf[i-1] += 1
pdf = pdf/sum(pdf) # normalise
cdf = np.cumsum(pdf)
ax.plot(bins, cdf, color='tab:red')
lmd = np.mean(data[1][0])
ax.axvline(lmd, linestyle='--', color='tab:red')
ax.set_title('rho = ' + str(1/(lmd*NU)))
ax.plot(bins, np.ones(len(bins))-np.exp(-(1/lmd)*bins), color='black')
#ax.plot(bins, np.ones(len(bins))-np.exp(-0.95*NU*bins), linestyle='--', color='tab:red')
ModeLines = [Line2D([0],[0],color='tab:red', lw=2), Line2D([0],[0],linestyle='--',color='black', lw=2)]
ax.legend(ModeLines, ['Measured',r'$1-e^{-\lambda t}$'], loc='lower right')