First really working version for ad-hoc

src/de/tud/kom/p2psim/impl/util/guirunner/impl/RunnerController.java

@@ -55,7 +55,28 @@ public class RunnerController implements ActionListener {
 	 * @param configFile
 	 */
 	private void runSimulator() {
-		new SimulationThread(selectedFile, det.getChosenSeed()).start();
+		SimulationThread simulationThread = new SimulationThread(selectedFile, det.getChosenSeed());
+		simulationThread.start();
+		
+		Thread thread = new Thread(new Runnable() {
+			
+			@Override
+			public void run() {
+				while (true) {
+					StackTraceElement[] stackTrace = simulationThread.getStackTrace();
+					System.out.println();
+					System.out.println();
+					for (int i = 0; i < stackTrace.length; i++) {
+						System.out.println(stackTrace[i]);
+					}
+					try {
+						Thread.sleep(1000);
+					} catch (InterruptedException e) {
+					}
+				}
+			}
+		});
+//		thread.start();
 	}
 	
 	public void setLastOpened(LastOpened lastOpened) {

src/de/tud/kom/p2psim/impl/vehicular/caching/DefaultCachingComponent.java

@@ -29,6 +29,7 @@ import de.tudarmstadt.maki.simonstrator.api.Host;
 import de.tudarmstadt.maki.simonstrator.api.component.ComponentNotAvailableException;
 import de.tudarmstadt.maki.simonstrator.api.component.network.NetInterface;
 import de.tudarmstadt.maki.simonstrator.api.component.network.NetworkComponent.NetInterfaceName;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.transport.ConnectivityListener;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.VehicleInformationComponent;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.CachingComponent;
@@ -98,7 +99,6 @@ implements CachingComponent, ConnectivityListener {
 				.values()) {
 			PointInformation correctInformation = _decisionStrategy
 					.decideOnCorrectInformation(similarEntries);
-
 			decidedInformation.add((T) correctInformation);
 		}
 

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/AveragingCacheDecisionStrategy.java

@@ -60,7 +60,7 @@ public class AveragingCacheDecisionStrategy implements CacheDecisionStrategy {
 		double value = sum / count;
 		
 		if (cloned instanceof VectoralJamProperty) {
-			((VectoralJamProperty) cloned).setSpeed(((int)(value / VectoralJamProperty.SCALING)) * VectoralJamProperty.SCALING, 0);
+			((VectoralJamProperty) cloned).setSpeed(((int)(value / VectoralJamProperty.SCALING)) * VectoralJamProperty.SCALING);
 		} else {
 			throw new AssertionError("Unknown data type " + cloned.getClass().getSimpleName());
 		}

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/MajorityVotingVectoralCacheDecisionStrategy.java

@@ -20,7 +20,6 @@
 
 package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
-import java.util.Arrays;
 import java.util.List;
 
 import de.tudarmstadt.maki.simonstrator.api.Time;
@@ -71,7 +70,21 @@ public class MajorityVotingVectoralCacheDecisionStrategy implements CacheDecisio
 		
 		VectoralProperty agedProperty = currentProperty.age((Time.getCurrentTime() - maxTimestamp) / SCALING, dependencyMatrix);
 		
-		return (T) new RoadInformation(agedProperty);
+		RoadInformation roadInformation = new RoadInformation(agedProperty);
+		
+		copyAttributes((RoadInformation) pSimilarPointInformation.get(0), roadInformation);
+		
+		return (T) roadInformation;
+	}
+
+	/**
+	 * @param pT
+	 * @param pRoadInformation
+	 */
+	private void copyAttributes(RoadInformation pSrc, RoadInformation pDest) {
+		for (AvailableInformationAttributes attribute : pSrc.getAvailableAttributes()) {
+			pDest.setAttribute(attribute, pSrc.getAttribute(attribute));
+		}
 	}
 
 }

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/OptimalCacheDecisionStrategy.java

@@ -22,9 +22,12 @@ package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
 import java.util.List;
 
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.JamProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
+import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetworkEdge;
 
 public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
@@ -43,17 +46,30 @@ public class OptimalCacheDecisionStrategy implements CacheDecisionStrategy {
 
 		RoadNetworkEdge edge = (RoadNetworkEdge) pSimilarPointInformation.get(0).getAttribute(AvailableInformationAttributes.EDGE);
 		
-		boolean jam = false;
-		if (edge.getLastStepMeanSpeed() < 5) {
-			jam = true;
+		double actualSpeed = edge.getCurrentSpeedState();
+		
+		if (actualSpeed == 24d) {
+			System.out.println();
+		}
+		
+		JamProperty jamProperty = edge.getJamProperty();
+
+		long maxTimestamp;
+		if (jamProperty != null) {
+			maxTimestamp = jamProperty.getDetectionDate();
+		} else {
+			maxTimestamp = -1;
 		}
 
-		long maxTimestamp = -1;
 		T maxFitting = null;
 		for (T t : pSimilarPointInformation) {
 			long timestamp = t.getDetectionDate();
 
-			if (t.getValue().equals(jam) && timestamp > maxTimestamp) {
+			Object currentValue = t.getValue();
+			if (currentValue instanceof VectoralProperty) {
+				currentValue = ((VectoralProperty) currentValue).getMostProbableValue();
+			}
+			if (currentValue.equals(actualSpeed) && timestamp > maxTimestamp) {
 				maxTimestamp = timestamp;
 				maxFitting = t;
 			}

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/TTLbasedCacheDecisionStrategy.java

@@ -20,16 +20,21 @@
 
 package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
-import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;
 
+import cern.colt.Arrays;
 import de.tudarmstadt.maki.simonstrator.api.Time;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.RoadProperty;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralJamProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
+import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
 
 public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 	private static final long SCALING = Time.SECOND;
@@ -80,6 +85,15 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 			return null;
 		}
 		
+		Collections.sort(pSimilarPointInformation, new Comparator<T>() {
+
+			@Override
+			public int compare(T pArg0, T pArg1) {
+				return Long.compare(pArg0.getDetectionDate(), pArg1.getDetectionDate());
+			}
+			
+		});
+
 		long minTimestamp = Long.MAX_VALUE;
 		long maxTimestamp = 0;
 		Object value = pSimilarPointInformation.get(0).getValue();
@@ -103,6 +117,10 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 		}
 
 		if (differentValue) {
+			if (Time.getCurrentTime() == 463500000) {
+				System.out.println();
+			}
+			
 			long difference = maxTimestamp - minTimestamp;
 
 			if (difference == 0) {
@@ -111,7 +129,8 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 
 			double rate = difference / ((double) (pSimilarPointInformation.size() - 1) * SCALING);
 
-			rate = Math.min(rate, ttl / 20);
+			long ttl = (long)pSimilarPointInformation.get(0).getAttribute(AvailableInformationAttributes.TTL) / SCALING;
+			rate = Math.min(rate, ttl / 10.0);
 
 			double b;
 			if (Boolean.FALSE.equals(_lastDecision)) {
@@ -127,12 +146,18 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 
 				double sumImpact = 0;
 
-				if (weight.containsKey(t.getValue())) {
-					sumImpact = weight.get(t.getValue());
+				Object currentValue = t.getValue();
+				
+				if (currentValue instanceof VectoralJamProperty) {
+					currentValue = ((VectoralJamProperty) currentValue).getMostProbableValue();
+				}
+
+				if (weight.containsKey(currentValue)) {
+					sumImpact = weight.get(currentValue);
 				}
 				sumImpact += impact;
 
-				weight.put(t.getValue(), sumImpact);
+				weight.put(currentValue, sumImpact);
 			}
 
 			double maxWeight = -1;
@@ -150,7 +175,12 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 			for (T t : pSimilarPointInformation) {
 				long timestamp = t.getDetectionDate();
 
-				if (t.getValue().equals(maxValue) && timestamp > maxTimestamp) {
+				Object currentValue = t.getValue();
+				if (currentValue instanceof VectoralJamProperty) {
+					currentValue = ((VectoralJamProperty)currentValue).getMostProbableValue();
+				}
+				
+				if (currentValue.equals(maxValue) && timestamp > maxTimestamp) {
 					maxTimestamp = timestamp;
 					maxFitting = t;
 				}
@@ -178,8 +208,7 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 	}
 
 	public double calculateImpact(double errorProbability, long ttl, long time, double b, long maxTimestamp) {
-		long currentTime = Time.getCurrentTime() / SCALING;
-		long age = currentTime - time;
+		long age = maxTimestamp - time;
 		if (errorProbability == 0) {
 			if (time == maxTimestamp) {
 				return 1;
@@ -190,6 +219,12 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 			return 1;
 		} else if (errorProbability == 0.5) {
 			return (errorProbability - 1) / ttl * age + errorProbability;
+		} else if (b == Double.NEGATIVE_INFINITY) {
+			if (time == maxTimestamp) {
+				return 1;
+			} else {
+				return 0;
+			}
 		}
 
 		return (1 - errorProbability) * (Math.exp(b * age) - Math.exp(b * ttl)) / (1 - Math.exp(b * ttl));
@@ -217,6 +252,10 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 
 		int optimalAmount = getOptimalMessageAmountForSwitch(p_c, errorProbability, costSlow, costFast);
 
+		if (optimalAmount == 1) {
+			return Double.NEGATIVE_INFINITY;
+		}
+		
 		boolean first = true;
 
 		double leftSide;
@@ -290,16 +329,16 @@ public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
 
 	public double calculateWeightingForOldState(int optimalMessageAmount, double rate, double errorProbability, long ttl, double b) {
 		double impact = 0;
-		for (int a = optimalMessageAmount + 1; a < Math.max(Math.floor(ttl / rate), optimalMessageAmount + 2); a++) {
-			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, 0);
+		for (int a = optimalMessageAmount; a < Math.max(Math.floor(ttl / rate), optimalMessageAmount + 2); a++) {
+			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, Time.getCurrentTime() / SCALING);
 		}
 		return impact;
 	}
 
 	public double calculateWeightingForNewState(int optimalMessageAmount, double rate, double errorProbability, long ttl, double b) {
 		double impact = 0;
-		for (int a = 0; a <= optimalMessageAmount; a++) {
-			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, 0);
+		for (int a = 0; a < optimalMessageAmount; a++) {
+			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, Time.getCurrentTime() / SCALING);
 		}
 		return impact;
 	}

src/de/tud/kom/p2psim/impl/vehicular/caching/decision/TTLbasedVectoralCacheDecisionStrategy.java

@@ -20,18 +20,21 @@
 
 package de.tud.kom.p2psim.impl.vehicular.caching.decision;
 
+import java.util.ArrayList;
 import java.util.Comparator;
 import java.util.List;
 import java.util.Map;
 import java.util.Map.Entry;
 
 import de.tudarmstadt.maki.simonstrator.api.Time;
+import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralJamProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.VectoralProperty;
 import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.vector.TemporalDependencyMatrix;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.caching.decision.CacheDecisionStrategy;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.AvailableInformationAttributes;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.PointInformation;
 import de.tudarmstadt.maki.simonstrator.api.component.vehicular.information.RoadInformation;
+import de.tudarmstadt.maki.simonstrator.api.component.vehicular.roadnetwork.RoadNetworkEdge;
 import edu.emory.mathcs.backport.java.util.Collections;
 
 public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrategy {
@@ -39,12 +42,14 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 
 	private static final double ACCURACY_FACTOR = 100000;
 
+	private static final double MIN_STEP = 0.00001;
+
 	private double accuracy = 1;
 
 	private double costWrongKeep = 1;
 	private double costWrongChange = 1;
 
-	private Object _lastDecision = false;
+	private Object _lastDecision = null;
 
 	public TTLbasedVectoralCacheDecisionStrategy(Map<String, String> pParams) {
 		for (Entry<String, String> param : pParams.entrySet()) {
@@ -94,7 +99,13 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		long minTimestamp = Long.MAX_VALUE;
 		long maxTimestamp = 0;
 		Object value = pSimilarPointInformation.get(0).getValue();
+		if (value instanceof VectoralProperty) {
+			value = ((VectoralProperty) value).getMostProbableValue();
+		}
 		boolean differentValue = false;
+		
+		List<Double> possibleValues = new ArrayList<>();
+		
 		for (T t : pSimilarPointInformation) {
 			if (!t.hasAttribute(AvailableInformationAttributes.TTL)) {
 				throw new AssertionError("Unable to perform TTL-based majority voting witout TTL");
@@ -108,9 +119,18 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 				maxTimestamp = timestamp;
 			}
 
-			if (!value.equals(t.getValue())) {
+			Object currentValue = t.getValue();
+			if (currentValue instanceof VectoralProperty) {
+				VectoralProperty currentProperty = (VectoralProperty) currentValue;
+				currentValue = currentProperty.getMostProbableValue();
+				if (!value.equals(currentValue)) {
 					differentValue = true;
 				}
+				
+				if (!possibleValues.contains(currentValue)) {
+					possibleValues.add((Double) currentValue);
+				}
+			}
 		}
 		
 		if (differentValue) {
@@ -122,22 +142,35 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 
 			double rate = difference / ((double) (pSimilarPointInformation.size() - 1) * SCALING);
 
-			long ttl = (long)pSimilarPointInformation.get(0).getAttribute(AvailableInformationAttributes.TTL) / SCALING;
-			rate = Math.min(rate, ttl / 10);
-
-			double b = determineB(rate, 1 - accuracy, ttl, costWrongKeep, costWrongChange);
+			long ttl = getTTL(pSimilarPointInformation.get(0));
+			double numberOfMessages = ttl / rate;
 
 			VectoralProperty currentProperty = null;
 
+			double b = 0;
+			int count = 0;
+			for (Double possibleValue : possibleValues) {
+				double temp = determineB(((RoadInformation)pSimilarPointInformation.get(0)).getEdge(), possibleValue, getChangeRate(pSimilarPointInformation.get(0), rate), rate, 1 - accuracy, numberOfMessages, costWrongKeep, costWrongChange);
+				if (!Double.isNaN(temp)) {
+					b += temp;
+					count++;
+				}
+			}
+			
+			if (count > 0) {
+				b /= count;
+			} else {
+				b = Double.NEGATIVE_INFINITY;
+			}
+
 			for (T t : pSimilarPointInformation) {
 				RoadInformation roadInformation = ((RoadInformation) t);
 				VectoralProperty property = (VectoralProperty) roadInformation.getValue();
 				
-				double impact = calculateImpact(1 - accuracy, ttl, t.getDetectionDate() / SCALING, b, maxTimestamp / SCALING) / (accuracy);
+				double impact = calculateImpact(1 - accuracy, numberOfMessages, (((t.getDetectionDate() - maxTimestamp) / SCALING  + ttl) / (double)ttl) * numberOfMessages, b) / (accuracy);
 
 				TemporalDependencyMatrix dependencyMatrix = property.getDependencyMatrix();
-				dependencyMatrix = dependencyMatrix.age((maxTimestamp - property.getDetectionDate()) / SCALING);
-				dependencyMatrix = modifyDependencyMatrix(dependencyMatrix, impact);
+				dependencyMatrix = modifyDependencyMatrix(dependencyMatrix.age((maxTimestamp - property.getDetectionDate()) / SCALING), impact);
 				
  				VectoralProperty agedProperty = property.age(1, dependencyMatrix);
 				if (currentProperty != null) {
@@ -147,7 +180,17 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 				}
 			}
 			
-			return (T) new RoadInformation(currentProperty);
+			if (Double.isNaN(currentProperty.getValueProbabilities()[0])) {
+				return pSimilarPointInformation.get(pSimilarPointInformation.size() - 1);
+			}
+			
+			RoadInformation roadInformation = new RoadInformation(currentProperty);
+			
+			copyAttributes((RoadInformation) pSimilarPointInformation.get(0), roadInformation);
+			
+			_lastDecision = roadInformation.getValue();
+			
+			return (T) roadInformation;
 		} else {
 			maxTimestamp = -1;
 			T maxFitting = null;
@@ -166,6 +209,46 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		}
 	}
 
+	/**
+	 * @param pT
+	 * @return
+	 */
+	private <T extends PointInformation> double getAccuracy(T pT) {
+		if (pT instanceof RoadInformation) {
+			RoadInformation roadInformation = ((RoadInformation) pT);
+			VectoralProperty property = (VectoralProperty) roadInformation.getValue();
+			double accuracy = property.getProbabilityForValue(property.getMostProbableValue());
+			return accuracy;
+		}
+		return this.accuracy;
+	}
+
+	private <T extends PointInformation> double getChangeRate(T pT, double pRate) {
+//		if (pT instanceof RoadInformation) {
+//			RoadInformation roadInformation = ((RoadInformation) pT);
+//			VectoralProperty property = (VectoralProperty) roadInformation.getValue();
+//			
+//			TemporalDependencyMatrix dependencyMatrix = property.getDependencyMatrix();
+//			return 1 - Math.pow(1 - dependencyMatrix.getChangeProbability(0), pRate * (SCALING / Time.SECOND));
+//		}
+		return getChangeProbability((long) (getTTL(pT) / pRate));
+	}
+
+	public <T extends PointInformation> long getTTL(
+			T pT) {
+		return (long)pT.getAttribute(AvailableInformationAttributes.TTL) / SCALING;
+	}
+
+	/**
+	 * @param pT
+	 * @param pRoadInformation
+	 */
+	private void copyAttributes(RoadInformation pSrc, RoadInformation pDest) {
+		for (AvailableInformationAttributes attribute : pSrc.getAvailableAttributes()) {
+			pDest.setAttribute(attribute, pSrc.getAttribute(attribute));
+		}
+	}
+
 	private TemporalDependencyMatrix modifyDependencyMatrix(
 			TemporalDependencyMatrix pDependencyMatrix, double pImpact) {
 		TemporalDependencyMatrix result = pDependencyMatrix.clone();
@@ -175,34 +258,34 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 			double finalPercentages = 1.0 / dependencies[i].length;
 			
 			for (int j = 0; j < dependencies[i].length; j++) {
-				dependencies[i][j] = finalPercentages + (dependencies[i][j] - finalPercentages) * pImpact;
+				dependencies[i][j] = finalPercentages + (pDependencyMatrix.getDependencies()[i][j] - finalPercentages) * pImpact;
 			}
 		}
 		
 		return result;
 	}
 
-	public double calculateImpact(double errorProbability, long ttl, long time, double b, long maxTimestamp) {
-		long age = maxTimestamp - time;
+	public double calculateImpact(double errorProbability, double pNumberOfMessages, double pMessageNumber, double b) {
+		double age = pNumberOfMessages - pMessageNumber;
 		if (errorProbability == 0) {
-			if (time == maxTimestamp) {
+			if (pMessageNumber == pNumberOfMessages) {
 				return 1;
 			} else {
 				return 0;
 			}
 		} else if (errorProbability == 1) {
 			return 1;
-		} else if (errorProbability == 0.5) {
-			return (errorProbability - 1) / ttl * age + errorProbability;
+		} else if (errorProbability == 0.5 || b == 0) {
+			return (1 - errorProbability) / pNumberOfMessages * age + errorProbability;
 		} else if (b == Double.NEGATIVE_INFINITY) {
-			if (time == maxTimestamp) {
+			if (pMessageNumber == pNumberOfMessages) {
 				return 1;
 			} else {
 				return 0;
 			}
 		}
 
-		return (1 - errorProbability) * (Math.exp(b * age) - Math.exp(b * ttl)) / (1 - Math.exp(b * ttl));
+		return (1 - errorProbability) * (Math.exp(b * age) - Math.exp(b * pNumberOfMessages)) / (1 - Math.exp(b * pNumberOfMessages));
 	}
 
 	public double getChangeProbability(long ttl) {
@@ -213,17 +296,23 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		return (int) Math.round(Math.log(-changeProbability / Math.log(errorProbability) * costSlow / costFast) / Math.log(errorProbability));
 	}
 
-	public double determineB(double rate, double errorProbability, long ttl, double costSlow, double costFast) {
-		return determineB(rate, errorProbability, ttl, costSlow, costFast, 1);
+	public double determineB(RoadNetworkEdge pRoadNetworkEdge, double pPossibleValue, double change, double rate, double errorProbability, double pNumberOfMessages, double costSlow, double costFast) {
+		if (errorProbability == 0 || errorProbability == 1 || errorProbability == 0.5) {
+			return Double.NaN;
 		}
 		
-	public double determineB(double rate, double errorProbability, long ttl, double costSlow, double costFast, int reversed) {
-		if (errorProbability == 0 || errorProbability == 1 || errorProbability == 0.5) {
+		if (_lastDecision != null) {
+			if (pPossibleValue == ((VectoralJamProperty)_lastDecision).getMostProbableValue()) {
+				return Double.NaN;
+			}
+		} else {
+			if (pPossibleValue == pRoadNetworkEdge.getCurrentSpeedState()) {
 				return Double.NaN;
 			}
+		}
 		
 		double b;
-		double p_c = getChangeProbability((long) (ttl / rate));
+		double p_c = change;
 		
 		int optimalAmount = getOptimalMessageAmountForSwitch(p_c, errorProbability, costSlow, costFast);
 		
@@ -233,47 +322,32 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 		
 		boolean first = true;
 		
-		double leftSide;
-		double rightSide;
-
-		double step = 5;
+		double step = 10;
 		
 		if (errorProbability < 0.5) {
-			b = -2 * step * reversed;
+			b = -1 * step;
 		} else {
-			b = 2 * step * reversed;
+			b = 1 * step;
 		}
 		
-		int similar = 0;
-		double lastDifference = -1;
 		do {
-			leftSide = calculateWeightingForOldState(optimalAmount, rate, errorProbability, ttl, b);
-			rightSide = calculateWeightingForNewState(optimalAmount, rate, errorProbability, ttl, b);
-			
-			if (Math.abs(Math.round((rightSide - leftSide) * ACCURACY_FACTOR)) == lastDifference) {
-				similar++;
-			} else {
-				lastDifference = Math.abs(Math.round((rightSide - leftSide) * ACCURACY_FACTOR));
-				similar = 0;
+			VectoralJamProperty valueAfterN = calculateMostProbableValue(pRoadNetworkEdge, pPossibleValue, optimalAmount, rate, errorProbability, pNumberOfMessages, b);
+			double probableValueAfterN = Double.NaN;
+			if (valueAfterN != null) {
+				probableValueAfterN = valueAfterN.getMostProbableValue();
+			}
+			VectoralJamProperty valueBeforeN = calculateMostProbableValue(pRoadNetworkEdge, pPossibleValue, optimalAmount - 1, rate, errorProbability, pNumberOfMessages, b);
+			double probableValueBeforeN = Double.NaN;
+			if (valueBeforeN != null) {
+				probableValueBeforeN = valueBeforeN.getMostProbableValue();
 			}
 			
-			if (Double.isNaN(leftSide) || Double.isNaN(rightSide) || similar > 100) {
-				if (reversed != -1) {
-					double determineB = determineB(rate, errorProbability, ttl, costSlow, costFast, -1);
-					if (!Double.isNaN(determineB)) {
-						return determineB;
-					} else {
+			if (probableValueAfterN == pPossibleValue && probableValueAfterN != probableValueBeforeN) {
 				return b;
 			}
-				} else {
-					return Double.NaN;
-				}
-			}
-
-			leftSide = Math.round(leftSide * ACCURACY_FACTOR);
-			rightSide = Math.round(rightSide * ACCURACY_FACTOR);
 			
-			if (leftSide > rightSide) {
+			if (!Double.isNaN(probableValueBeforeN) && !Double.isNaN(probableValueBeforeN) && step >= MIN_STEP) {
+				if (probableValueAfterN != pPossibleValue) {
 					if (b < 0) {
 						b -= step;
 						if (!first) {
@@ -284,7 +358,7 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 						step *= 0.5;
 						first = false;
 					}
-			} else if (leftSide < rightSide) {
+				} else if (probableValueAfterN == pPossibleValue && probableValueAfterN == probableValueBeforeN) {
 					if (b > 0) {
 						b += step;
 						if (!first) {
@@ -296,26 +370,65 @@ public class TTLbasedVectoralCacheDecisionStrategy implements CacheDecisionStrat
 						first = false;
 					}
 				} else {
-				break;
+					return Double.NaN;
+				}
+			} else {
+				return Double.NaN;
 			}
 		} while (true);
+	}
 
-		return b;
+	public VectoralJamProperty calculateMostProbableValue(RoadNetworkEdge pRoadNetworkEdge, double pNewValue, int optimalMessageAmount, double rate, double errorProbability, double pNumberOfMessages, double b) {
+		VectoralJamProperty currentProperty = null;
+		
+		for (int a = optimalMessageAmount + 1; a <= pNumberOfMessages; a++) {
+			VectoralJamProperty jamProperty = new VectoralJamProperty();
+			
+			if (_lastDecision != null) {
+				jamProperty.setGaussianSpeedWithAccuracy(((VectoralJamProperty)_lastDecision).getMostProbableValue(), accuracy);
+			} else {
+				jamProperty.setGaussianSpeedWithAccuracy(pRoadNetworkEdge.getCurrentSpeedState(), accuracy);
+			}
+			TemporalDependencyMatrix temporalDependencyMatrix = jamProperty.getDependencyMatrix();
+			temporalDependencyMatrix = temporalDependencyMatrix.age((long) (a * rate));
+			double impact = calculateImpact(errorProbability, pNumberOfMessages, pNumberOfMessages - a, b);
+			
+			if (Double.isNaN(impact)) {
+				return null;
 			}
 			
-	public double calculateWeightingForOldState(int optimalMessageAmount, double rate, double errorProbability, long ttl, double b) {
-		double impact = 0;
-		for (int a = optimalMessageAmount; a < Math.max(Math.floor(ttl / rate), optimalMessageAmount + 2); a++) {
-			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, Time.getCurrentTime() / SCALING);
+			temporalDependencyMatrix = modifyDependencyMatrix(temporalDependencyMatrix, impact);
+			jamProperty = (VectoralJamProperty) jamProperty.age(1, temporalDependencyMatrix);
+
+			if (currentProperty != null) {
+				currentProperty = (VectoralJamProperty) currentProperty.combine(jamProperty);
+			} else {
+				currentProperty = jamProperty;
 			}
-		return impact;
 		}
 		
-	public double calculateWeightingForNewState(int optimalMessageAmount, double rate, double errorProbability, long ttl, double b) {
-		double impact = 0;
-		for (int a = 0; a < optimalMessageAmount; a++) {
-			impact += calculateImpact(errorProbability, ttl, Time.getCurrentTime() / SCALING - (long)Math.floor(a * rate), b, Time.getCurrentTime() / SCALING);
+		for (int a = 0; a <= optimalMessageAmount; a++) {
+			VectoralJamProperty jamProperty = new VectoralJamProperty();
+			jamProperty.setGaussianSpeedWithAccuracy(pNewValue, accuracy);
+
+			TemporalDependencyMatrix temporalDependencyMatrix = jamProperty.getDependencyMatrix();
+			temporalDependencyMatrix = temporalDependencyMatrix.age((long) (a * rate));
+			double impact = calculateImpact(errorProbability, pNumberOfMessages, pNumberOfMessages - a, b);
+
+			if (Double.isNaN(impact)) {
+				return null;
 			}
-		return impact;
+
+			temporalDependencyMatrix = modifyDependencyMatrix(temporalDependencyMatrix, impact);
+			jamProperty = (VectoralJamProperty) jamProperty.age(1, temporalDependencyMatrix);
+
+			if (currentProperty != null) {
+				currentProperty = (VectoralJamProperty) currentProperty.combine(jamProperty);
+			} else {
+				currentProperty = jamProperty;
+			}
+		}
+
+		return currentProperty;
 	}
 }