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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()
generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl="lora",wuf=180) {
attempts=seq(1,attempts_max)
results=sapply(attempts,function(attempt){
## Prepare data for traning
set.seed(1+attempt) # Reproducibility
wireless_map=c("lora"=1,"nbiot"=2)
data_seed=data_seed%>%filter(wakeupfor==wuf,wireless==wrl)
data_ml=data_seed%>%select(-efficiency)%>%mutate(wireless=wireless_map[data_seed$wireless])
if(ignore_hint){
data_ml=data_ml%>%filter(simkey!="hint")
}
train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed) # train data on seed_max*3 days
test_set=data_ml%>%anti_join(train_set)%>%select(-seed) # build test_sed excluding training set
## KNN training
knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$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)
list(tibble(seed_max=seed_max,knn_accuracy=knn_accuracy,tree_accuracy=tree_accuracy))
})
## Prints
results=do.call("rbind",results)
results%>%mutate(seed_max=seed_max,attempts_max=attempts_max,wireless=wrl,wakeupfor=wuf)
}
generate_accuracy = function(wireless,wakeupfor,steps=20, accuracy=10){
## Generate inputs
result=tibble()
for(i in seq(1,200,by=steps)){
acc=generate_accuracy_for(ignore_hint=TRUE,seed=i,attempts_max=accuracy,wrl=wireless,wuf=wakeupfor)
result=rbind(result,acc)
}
result%>%mutate(days=seed_max*3) # Since 3 policies (since ignore_hint=TRUE)
}
## Generate accuracy for each wireless and uptime
accuracy=rbind(generate_accuracy("lora",60),
generate_accuracy("lora",180),
generate_accuracy("nbiot",60),
generate_accuracy("nbiot",180))
## Summarize
result_summary=accuracy%>%group_by(wireless,wakeupfor,seed_max)%>%summarize(attempts_max=mean(attempts_max),days=mean(days),mean_knn_accuracy=mean(knn_accuracy),sd_knn_accuracy=sd(knn_accuracy),min_knn_accuracy=min(knn_accuracy),max_knn_accuracy=max(knn_accuracy),mean_tree_accuracy=mean(tree_accuracy),sd_tree_accuracy=sd(tree_accuracy),min_tree_accuracy=min(tree_accuracy),max_tree_accuracy=max(tree_accuracy))
## Result max
result_max=result_summary%>%group_by(wireless,wakeupfor)%>%summarize(max_knn_mean=max(mean_knn_accuracy),max_tree_mean=max(mean_tree_accuracy))
## Plot
ggplot(result_summary,aes(days,mean_knn_accuracy))+
geom_errorbar(aes(ymin=min_knn_accuracy,ymax=max_knn_accuracy),width=5)+
geom_boxplot(aes(ymin=min_knn_accuracy,ymax=max_knn_accuracy,
middle=mean_knn_accuracy,
upper=mean_knn_accuracy+sd_knn_accuracy,
lower=mean_knn_accuracy-sd_knn_accuracy,group=days),stat="identity",fill="grey")+
geom_line(size=1.1)+geom_point(size=3,pch=15)+xlab("Number of training days")+ylab("Mean KNN accuracy")+ggtitle("KNN Accuracy")+
ylim(c(0,100))+
facet_wrap(~wireless+wakeupfor)+
geom_hline(data=result_max,aes(yintercept=max_knn_mean),color="red",size=1)+
geom_text(data=result_max, geom="text",x=0,aes(y=max_knn_mean,label = max_knn_mean,vjust=-1),color="red")
ggsave("figures/days_knn.pdf")
ggplot(result_summary,aes(days,mean_tree_accuracy))+
geom_errorbar(aes(ymin=min_tree_accuracy,ymax=max_tree_accuracy),width=5)+
geom_boxplot(aes(ymin=min_tree_accuracy,ymax=max_tree_accuracy,
middle=mean_tree_accuracy,
upper=mean_tree_accuracy+sd_tree_accuracy,
lower=mean_tree_accuracy-sd_tree_accuracy,group=days),stat="identity",fill="grey")+
geom_line(size=1.1)+geom_point(size=3,pch=15)+xlab("Number of training days")+ylab("Mean tree accuracy")+ggtitle("TREE Accuracy")+
ylim(c(0,100))+
facet_wrap(~wireless+wakeupfor)+
geom_hline(data=result_max,aes(yintercept=max_tree_mean),color="red",size=1)+
geom_text(data=result_max, geom="text",x=0,aes(y=max_tree_mean,label = max_tree_mean,vjust=-1),color="red")
ggsave("figures/days_tree.pdf")
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