Update

4 files changed, 124 insertions, 4 deletions

diff --git a/analysis/figures/random_inputs.pdf b/analysis/figures/random_inputs.pdf
new file mode 100644
index 0000000..8270126
--- /dev/null
+++ b/analysis/figures/random_inputs.pdf
diff --git a/analysis/figures/tree.pdf b/analysis/figures/tree.pdf
index 1e7b753..3ed995b 100644
--- a/analysis/figures/tree.pdf
+++ b/analysis/figures/tree.pdf
diff --git a/analysis/inputs.csv b/analysis/inputs.csv
new file mode 100644
index 0000000..c144b6e
--- /dev/null
+++ b/analysis/inputs.csv
@@ -0,0 +1,81 @@
+"wireless","wakeupfor","energy_constraint","coverage_constraint","simkey","model"
+"lora",60,8732.48785137262,10,"hintandextended","knn"
+"lora",60,7760.02858805553,4,"baseline","knn"
+"lora",60,7647.55841422485,11,"baseline","knn"
+"lora",60,7849.4323163005,8,"extended","knn"
+"lora",60,7531.65704064597,10,"baseline","knn"
+"lora",60,8524.20092750562,9,"hintandextended","knn"
+"lora",60,8744.00524232863,1,"hintandextended","knn"
+"lora",60,8426.54290125995,1,"hintandextended","knn"
+"lora",60,7631.27743269689,1,"baseline","knn"
+"lora",60,7661.19496849046,5,"hint","knn"
+"lora",60,8732.48785137262,10,"hintandextended","tree"
+"lora",60,7760.02858805553,4,"extended","tree"
+"lora",60,7647.55841422485,11,"extended","tree"
+"lora",60,7849.4323163005,8,"extended","tree"
+"lora",60,7531.65704064597,10,"extended","tree"
+"lora",60,8524.20092750562,9,"hintandextended","tree"
+"lora",60,8744.00524232863,1,"baseline","tree"
+"lora",60,8426.54290125995,1,"baseline","tree"
+"lora",60,7631.27743269689,1,"hint","tree"
+"lora",60,7661.19496849046,5,"extended","tree"
+"lora",180,29123.7166634847,4,"hint","knn"
+"lora",180,29655.7737664565,3,"hint","knn"
+"lora",180,23645.1436280268,11,"extended","knn"
+"lora",180,24938.4341620644,0,"hintandextended","knn"
+"lora",180,29862.9781404826,5,"hint","knn"
+"lora",180,27307.1859899517,6,"hint","knn"
+"lora",180,23232.7369937696,3,"baseline","knn"
+"lora",180,25404.8205478179,7,"hintandextended","knn"
+"lora",180,26553.2369356217,8,"hintandextended","knn"
+"lora",180,24432.1396842401,8,"hintandextended","knn"
+"lora",180,29123.7166634847,4,"hint","tree"
+"lora",180,29655.7737664565,3,"baseline","tree"
+"lora",180,23645.1436280268,11,"extended","tree"
+"lora",180,24938.4341620644,0,"baseline","tree"
+"lora",180,29862.9781404826,5,"hint","tree"
+"lora",180,27307.1859899517,6,"hint","tree"
+"lora",180,23232.7369937696,3,"baseline","tree"
+"lora",180,25404.8205478179,7,"hintandextended","tree"
+"lora",180,26553.2369356217,8,"hint","tree"
+"lora",180,24432.1396842401,8,"hintandextended","tree"
+"nbiot",60,9452.16352643334,9,"hint","knn"
+"nbiot",60,9465.21074816174,3,"hint","knn"
+"nbiot",60,8456.21622429176,1,"hintandextended","knn"
+"nbiot",60,9570.38342273607,3,"hint","knn"
+"nbiot",60,8855.86319130787,12,"hintandextended","knn"
+"nbiot",60,8444.57516620697,3,"hintandextended","knn"
+"nbiot",60,8527.76385865066,4,"hintandextended","knn"
+"nbiot",60,8685.2330654384,8,"hintandextended","knn"
+"nbiot",60,8744.42417828556,8,"hintandextended","knn"
+"nbiot",60,8664.97570233729,0,"hintandextended","knn"
+"nbiot",60,9452.16352643334,9,"hintandextended","tree"
+"nbiot",60,9465.21074816174,3,"baseline","tree"
+"nbiot",60,8456.21622429176,1,"baseline","tree"
+"nbiot",60,9570.38342273607,3,"baseline","tree"
+"nbiot",60,8855.86319130787,12,"hintandextended","tree"
+"nbiot",60,8444.57516620697,3,"baseline","tree"
+"nbiot",60,8527.76385865066,4,"hintandextended","tree"
+"nbiot",60,8685.2330654384,8,"hintandextended","tree"
+"nbiot",60,8744.42417828556,8,"hintandextended","tree"
+"nbiot",60,8664.97570233729,0,"baseline","tree"
+"nbiot",180,23875.3493146438,7,"hintandextended","knn"
+"nbiot",180,26217.4338214212,12,"hint","knn"
+"nbiot",180,23369.7590769622,9,"extended","knn"
+"nbiot",180,28487.7830938115,8,"hint","knn"
+"nbiot",180,27871.6472379192,11,"hint","knn"
+"nbiot",180,22901.9086687423,7,"extended","knn"
+"nbiot",180,27745.8252319951,8,"hint","knn"
+"nbiot",180,26146.6891755201,10,"hintandextended","knn"
+"nbiot",180,23831.1879103171,8,"extended","knn"
+"nbiot",180,27030.7096498314,10,"hint","knn"
+"nbiot",180,23875.3493146438,7,"hintandextended","tree"
+"nbiot",180,26217.4338214212,12,"hint","tree"
+"nbiot",180,23369.7590769622,9,"baseline","tree"
+"nbiot",180,28487.7830938115,8,"hint","tree"
+"nbiot",180,27871.6472379192,11,"hint","tree"
+"nbiot",180,22901.9086687423,7,"extended","tree"
+"nbiot",180,27745.8252319951,8,"hint","tree"
+"nbiot",180,26146.6891755201,10,"hint","tree"
+"nbiot",180,23831.1879103171,8,"baseline","tree"
+"nbiot",180,27030.7096498314,10,"hint","tree"
diff --git a/analysis/knn.R b/analysis/knn.R
index 729f332..d8a6ce1 100644
--- a/analysis/knn.R
+++ b/analysis/knn.R
@@ -34,12 +34,12 @@ data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum
     mutate(efficiency=energy/coverage)%>%
     ungroup()
 
-
 ## Prepare data for traning
 set.seed(1) # Reproducibility
-data_seed=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=as.numeric(as.factor(data_seed$wireless)))#%>%filter(simkey!="hint")
-train_set=data_seed%>%sample_frac(0.8)     # 80% of the data
-test_set=data_seed%>%anti_join(train_set)  # 20% of the data
+wireless_map=c("lora"=1,"nbiot"=2)
+data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$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$simkey,k=10)
@@ -66,3 +66,42 @@ pdf("figures/tree.pdf")
 tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48)))
 silent_call=dev.off()
 ## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy
+
+## Generate simulation inputs
+inputs=tibble(
+wakeupfor = c(60,180,60,180),
+wireless  = c("lora", "lora", "nbiot", "nbiot"))
+constraints=apply(inputs,1,function(row){
+    wi=row["wireless"]
+    wa=as.numeric(row["wakeupfor"])
+    ## First extract energy/coverage boundaries
+    min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$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)