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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()
## 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$wireless])#%>%filter(simkey!="hint")
train_set=data_ml%>%sample_frac(0.8) # 80% of the data
test_set=data_ml%>%anti_join(train_set) # 20% of the data
## KNN training
knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$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)
## Prints
print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%"))
pdf("figures/tree.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=10
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)
## 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("figures/random_inputs.pdf")
write.csv(inputs,"../inputs.csv",row.names=FALSE, quote=FALSE)
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