1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
|
library("tidyverse")
library("class")
library("rpart")
library("rpart.plot")
library("viridis")
## Simulation Parameters:
## simkey {baseline,extended,hint,hintandextended}
## wireless {lora,nbiot}
## wakeupfor {60s,180s}
## seed [1,200]
## node on[0,12]
## isSender {0,1}
## dataSize {1MB}
## Metrics:
## energy [0,+inf)
## nDataRcv [0,+inf)
nseed=200
nwakeupfor=2
nwireless=2
nsimkey=4
nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200
## Load data
data=read_csv("../CCGRID2022.csv")%>%distinct() # Note that in the data experiment wireless=="lora",seed==1,wakeupfor==60,simkey=="baseline" is present 2 times in the CSV file
tmp_data_coverage=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%mutate(coverage=sum(nDataRcv))%>%ungroup()%>%filter(isSender==1)%>%select(simkey,wireless,wakeupfor,seed,coverage)
data_seed_isSender=data%>%group_by(simkey,wireless,wakeupfor,seed,isSender)%>%summarize(energy_mean=mean(energy))%>%
left_join(tmp_data_coverage,by=c("simkey","wireless","wakeupfor","seed"))%>%
mutate(efficiency=energy_mean/coverage)%>%
ungroup()
data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>%
mutate(efficiency=energy/coverage)%>%
ungroup()
generate_inputs=function(ignore_hint=FALSE) {
## Prepare data for traning
set.seed(1) # Reproducibility
wireless_map=c("lora"=1,"nbiot"=2)
data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])
if(ignore_hint){
data_ml=data_ml%>%filter(simkey!="hint")
}
train_set=data_ml%>%sample_frac(0.8) # 80% of the data
test_set=data_ml%>%anti_join(train_set) # 20% of the data
## KNN training
knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10)
## KNN analysis
knn_cont_table=table(knn_predictions,test_set$simkey)
knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100)
knn_prop_table=round(prop.table(knn_cont_table),digits=2)
## Decision tree
tree=rpart(
simkey ~ wireless + wakeupfor + energy + coverage,
data=train_set,
method="class",
minsplit=60,
minbucket=1)
tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class")
tree_cont_table=table(tree_predictions,test_set$simkey)
tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100)
tree_prop_table=round(prop.table(tree_cont_table),digits=2)
## Elbow plot
elbow_data=lapply(seq(1,10),function(kvalue){
knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=kvalue)
## KNN analysis
knn_cont_table=table(knn_predictions,test_set$simkey)
knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100)
knn_prop_table=round(prop.table(knn_cont_table),digits=2)
tibble(k=kvalue,accuracy=knn_accuracy)
})
elbow_data=do.call("rbind",elbow_data)
ggplot(data=elbow_data,aes(k,accuracy))+geom_point()+geom_line()+ggtitle(paste("K-elbow for with NoHint to",as.character(ignore_hint)))
ggsave(paste0("figures/knn_elbow_NoHintIs",as.character(ignore_hint),".pdf"))
## Prints
print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%"))
pdf(paste0("figures/tree_",as.character(ignore_hint),".pdf"))
tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48)))
silent_call=dev.off()
## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy
## Generate simulation inputs
inputs=tibble(
wakeupfor = c(60,180,60,180),
wireless = c("lora", "lora", "nbiot", "nbiot"))
constraints=apply(inputs,1,function(row){
wi=row["wireless"]
wa=as.numeric(row["wakeupfor"])
## First extract energy/coverage boundaries
min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
max_energy=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
min_coverage=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
max_coverage=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
## Generate random points (10 per scenarios)
n=100
current_inputs=tibble(
wireless=rep(wi,n),
wakeupfor=rep(wa,n),
energy_constraint=runif(n,min_energy,max_energy),
coverage_constraint=round(runif(n,min_coverage,max_coverage)))
predictions_knn=knn(train=train_set%>%select(-simkey),test=current_inputs%>%
rename(energy=energy_constraint,coverage=coverage_constraint)%>%
mutate(wireless=wireless_map[wireless]),cl=train_set$simkey,k=10)
predictions_tree=predict(tree,newdata=current_inputs%>%
rename(energy=energy_constraint,coverage=coverage_constraint)%>%
mutate(wireless=wireless_map[wireless]),type="class")
knn_final=tibble(cbind(current_inputs,tibble(simkey=predictions_knn,model="knn")))
tree_final=tibble(cbind(current_inputs,tibble(simkey=predictions_tree,model="tree")))
rbind(knn_final,tree_final)
})
inputs=do.call("rbind",constraints)%>%distinct()
## Dimension Energy/Coverage
ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
aes(coverage,energy,color=simkey))+geom_point()+
geom_point(data=inputs%>%mutate(wakeupfor=as.character(wakeupfor)),aes(coverage_constraint,energy_constraint),size=3,pch=5)+
ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Sum of nodes energy consumption (J)")+
facet_wrap(~wakeupfor+wireless,scale="free")
ggsave(paste0("figures/random_inputs_NoHintIs",as.character(ignore_hint),".pdf"),width=15)
write.csv(inputs,paste0("../inputs_NoHintIs",as.character(ignore_hint),".csv"),row.names=FALSE, quote=FALSE)
}
## Generate inputs
generate_inputs(FALSE)
generate_inputs(TRUE)
|
