TTLbasedCacheDecisionStrategy.java

/*
 * Copyright (c) 2005-2010 KOM – Multimedia Communications Lab
 *
 * This file is part of PeerfactSim.KOM.
 *
 * PeerfactSim.KOM is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * any later version.
 *
 * PeerfactSim.KOM is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with PeerfactSim.KOM.  If not, see <http://www.gnu.org/licenses/>.
 *
 */

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 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.VectoralProperty;
import de.tudarmstadt.maki.simonstrator.api.component.sensor.environment.data.jam.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;
import de.tudarmstadt.maki.simonstrator.api.component.vehicular.relevance.impl.SimpleQoIBasedImpactFunction;

public class TTLbasedCacheDecisionStrategy implements CacheDecisionStrategy {
	private static final long SCALING = Time.SECOND;

	private static final double ACCURACY_FACTOR = 100000;

	private long ttl = 300 * Time.SECOND / SCALING;
	private double accuracy = 1;

	private double costWrongKeep = 1;
	private double costWrongChange = 1;

	private Object _lastDecision = false;

	public TTLbasedCacheDecisionStrategy(Map<String, String> pParams) {
		for (Entry<String, String> param : pParams.entrySet()) {
			switch (param.getKey()) {
			case "ACCURACY":
				accuracy = Double.valueOf(param.getValue());
				break;
			case "COST_RATIO":
				double ratio = Double.valueOf(param.getValue());
				costWrongChange = 2 / (ratio + 1);
				costWrongKeep = 2 - costWrongChange;
				break;
			default:
				break;
			}
		}
	}

	public double getCostWrongChange() {
		return costWrongChange;
	}

	public double getCostWrongKeep() {
		return costWrongKeep;
	}

	@Override
	public <T extends PointInformation> T decideOnCorrectInformation(
			List<T> pSimilarPointInformation) {
		if (pSimilarPointInformation.size() == 1) {
			T decision = pSimilarPointInformation.get(0);
			_lastDecision = decision.getValue();
			return decision;
		} else if (pSimilarPointInformation.size() == 0) {
			return null;
		}

		Collections.sort(pSimilarPointInformation, new Comparator<T>() {

			@Override
			public int compare(T pArg0, T pArg1) {
				return Long.compare(pArg0.getDetectionDate(), pArg1.getDetectionDate());
			}

		});

		Object value = pSimilarPointInformation.get(0).getValue();
		boolean differentValue = false;
		for (T t : pSimilarPointInformation) {
			if (!value.equals(t.getValue())) {
				differentValue = true;
			}
		}

		if (differentValue) {
			SimpleQoIBasedImpactFunction<T> impactFunction = new SimpleQoIBasedImpactFunction<>(pSimilarPointInformation, accuracy);

			Map<Object, Double> weight = new HashMap<>();

			for (T t : pSimilarPointInformation) {
				double impact = impactFunction.calculateImpact(t);

				double sumImpact = 0;

				Object currentValue = t.getValue();

				if (currentValue instanceof VectoralJamProperty) {
					currentValue = ((VectoralJamProperty) currentValue).getMostProbableValue();
				}

				if (weight.containsKey(currentValue)) {
					sumImpact = weight.get(currentValue);
				}
				sumImpact += impact;

				weight.put(currentValue, sumImpact);
			}

			double maxWeight = -1;
			Object maxValue = null;

			for (Object key : weight.keySet()) {
				if (weight.get(key) > maxWeight) {
					maxWeight = weight.get(key);
					maxValue = key;
				}
			}

			long maxTimestamp = -1;
			T maxFitting = null;
			for (T t : pSimilarPointInformation) {
				long timestamp = t.getDetectionDate();

				Object currentValue = t.getValue();
				if (currentValue instanceof VectoralProperty) {
					currentValue = ((VectoralProperty)currentValue).getMostProbableValue();
				}

				if (currentValue.equals(maxValue) && timestamp > maxTimestamp) {
					maxTimestamp = timestamp;
					maxFitting = t;
				}
			}

			if (maxFitting.getValue() instanceof VectoralProperty) {
				VectoralProperty vectoralProperty = ((VectoralProperty)maxFitting.getValue()).clone();
				double[] valueProbabilities = vectoralProperty.getValueProbabilities();
				Arrays.fill(valueProbabilities, 0);

				double sum = 0;
				for (Object key : weight.keySet()) {
					valueProbabilities[vectoralProperty.getIndexForValue(key)] = weight.get(key);
					sum += weight.get(key);
				}

				for (int i = 0; i < valueProbabilities.length; i++) {
					valueProbabilities[i] /= sum;
				}

				RoadInformation roadInformation = new RoadInformation(vectoralProperty);
				roadInformation.copyAttributes((RoadInformation)maxFitting);
				maxFitting = (T) roadInformation;
			}

			_lastDecision = maxFitting.getValue();

			return maxFitting;
		} else {
			long maxTimestamp = -1;
			T maxFitting = null;
			for (T t : pSimilarPointInformation) {
				long timestamp = (long) t.getAttribute(AvailableInformationAttributes.TTL);

				if (timestamp > maxTimestamp) {
					maxTimestamp = timestamp;
					maxFitting = t;
				}
			}

			_lastDecision = maxFitting.getValue();

			return maxFitting;
		}
	}
}