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+library("tidyverse")
+options(dplyr.summarise.inform = FALSE)
+library("class")
+library("rpart")
+library("rpart.plot")
+library("viridis")
+library("MLmetrics")
+
+## 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=suppressMessages(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
+data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>%
+ mutate(efficiency=energy/coverage)%>%
+ ungroup()
+
+
+F1_Score2=function(truth, pred){
+ result=sapply(c("baseline","extended","hint","hintandextended"),function(c){
+ cur_truth=truth[truth==c]
+ cur_pred=pred[truth==c]
+ col=paste0("f1_",c)
+ score=F1_Score(cur_truth,cur_pred)
+ if(is.nan(score)){score=0}
+ list(tibble(!!col:=score))
+ })
+ do.call("cbind",result)
+}
+
+build_models=function(ignore_hint=TRUE){
+ ## 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$wireless])
+ if(ignore_hint){
+ data_ml=data_ml%>%filter(simkey!="hint")
+ }
+ train_set=data_ml%>%sample_frac(0.8) # 80% of the data
+ test_set=data_ml%>%suppressMessages(anti_join(train_set)) # 20% of the data
+
+ ## KNN predict function
+ knn_fn=function(inputs){
+ as.vector(knn(train=train_set%>%select(-simkey),test=inputs%>%select(-simkey),cl=train_set$simkey,k=10))
+ }
+
+ ## Decision tree
+ tree=rpart(
+ simkey ~ wireless + wakeupfor + energy + coverage,
+ data=train_set,
+ method="class",
+ minsplit=60,
+ minbucket=1)
+ ## Tree predict function
+ tree_fn=function(inputs){
+ as.vector(predict(tree,newdata=inputs%>%select(-simkey),type="class"))
+ }
+
+ ## Build models
+ models=list(predict_knn=knn_fn,predict_tree=tree_fn)
+
+ ## Computer performances
+ perfs=sapply(seq(1,20),function(i){
+ ## 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$wireless])
+ if(ignore_hint){
+ data_ml=data_ml%>%filter(simkey!="hint")
+ }
+ train_set=data_ml%>%sample_frac(0.8) # 80% of the data
+ test_set=data_ml%>%suppressMessages(anti_join(train_set)) # 20% of the data
+
+ ## KNN
+ knn_predictions=as.vector(knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10))
+ ## Decision tree
+ tree=rpart(
+ simkey ~ wireless + wakeupfor + energy + coverage,
+ data=train_set,
+ method="class",
+ minsplit=60,
+ minbucket=1)
+ tree_predictions=as.vector(predict(tree,newdata=test_set%>%select(-simkey),type="class"))
+
+ ## Prefs
+ f1_knn=F1_Score2(test_set$simkey,knn_predictions)
+ f1_tree=F1_Score2(test_set$simkey,tree_predictions)
+ accuracy_knn=sum(test_set$simkey==knn_predictions)/length(test_set$simkey)
+ accuracy_tree=sum(test_set$simkey==tree_predictions)/length(test_set$simkey)
+ list(cbind(tibble(model=c("knn","tree")),rbind(f1_knn,f1_tree),tibble(accuracy=c(accuracy_knn,accuracy_tree))))
+ })
+ perfs=do.call("rbind",perfs)%>%mutate_if(is.numeric, ~round(.,digits=2))
+ perfs=perfs%>%group_by(model)%>%summarize(
+ f1_baseline=mean(f1_baseline),
+ f1_hint=mean(f1_hint),
+ f1_extended=mean(f1_extended),
+ f1_hintandextended=mean(f1_hintandextended),
+ accuracy=mean(accuracy))
+ write.csv(perfs,paste0("figures/f1_scores_offline_ignoreHINT",ignore_hint,".csv"),quote=FALSE,row.names=FALSE)
+
+ ## Return models
+ models
+}
+
+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=(sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))
+ 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=(sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))
+ 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)))+ylim(c(0,1))
+ 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)),tweak=1.111)
+ 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)
generated by cgit v1.2.3 (git 2.46.0) at 2026-04-16 03:20:48 +0000