Gelecek Trendleri & Yeni Teknolojiler

5G Entegrasyonu

Ağ Yetenekleri

5G teknolojisi mobile networking'de devrim niteliğinde değişiklikler getiriyor.

Ultra-Düşük Gecikme

class FiveGOptimizedNetworking {
  constructor() {
    this.networkInfo = this.detectNetworkCapabilities();
    this.adaptToNetwork();
  }

  detectNetworkCapabilities() {
    if ('connection' in navigator) {
      const connection = navigator.connection;
      return {
        effectiveType: connection.effectiveType,
        downlink: connection.downlink,
        rtt: connection.rtt,
        saveData: connection.saveData,
        type: connection.type
      };
    }
    return null;
  }

  adaptToNetwork() {
    if (!this.networkInfo) return;

    switch (this.networkInfo.effectiveType) {
      case '5g':
        this.enable5GFeatures();
        break;
      case '4g':
        this.enable4GOptimizations();
        break;
      case '3g':
        this.enableConservativeMode();
        break;
      default:
        this.enableBasicMode();
    }
  }

  enable5GFeatures() {
    // Ultra-low latency features
    this.enableRealTimeFeatures();
    this.enableHighBandwidthContent();
    this.enableEdgeComputing();
    
    console.log('5G mode: Enabling advanced features');
  }

  enableRealTimeFeatures() {
    // Real-time collaborative features
    this.webSocketConfig = {
      heartbeatInterval: 1000, // 1 second for 5G
      reconnectDelay: 100,
      maxReconnectAttempts: 10
    };

    // AR/VR support
    this.enableARVRStreaming();
    
    // Real-time gaming
    this.enableLowLatencyGaming();
  }

  enableHighBandwidthContent() {
    // 4K/8K video streaming
    this.videoQuality = '4k';
    this.enableHighResolutionImages();
    
    // Bulk data transfers
    this.enableLargeFileTransfers();
    
    // High-quality audio
    this.enableLosslessAudio();
  }

  enableEdgeComputing() {
    // Edge server selection
    this.selectNearestEdgeServer();
    
    // Local processing
    this.enableEdgeProcessing();
    
    // Reduced round trips
    this.enableLocalCache();
  }

  async selectNearestEdgeServer() {
    try {
      // 5G network slicing için optimal edge server seçimi
      const edgeServers = await this.discoverEdgeServers();
      const optimalServer = await this.selectOptimalEdge(edgeServers);
      
      this.edgeEndpoint = optimalServer;
      console.log(`Connected to edge server: ${optimalServer.location}`);
    } catch (error) {
      console.error('Edge server selection failed:', error);
      this.fallbackToCloudServer();
    }
  }

  async discoverEdgeServers() {
    const response = await fetch('/api/edge/discover', {
      headers: {
        'X-Network-Info': JSON.stringify(this.networkInfo)
      }
    });
    
    return await response.json();
  }

  async selectOptimalEdge(servers) {
    // Latency-based selection
    const latencyTests = servers.map(async server => {
      const start = performance.now();
      
      try {
        await fetch(`${server.endpoint}/ping`, { 
          method: 'HEAD',
          cache: 'no-cache'
        });
        
        const latency = performance.now() - start;
        return { server, latency };
      } catch (error) {
        return { server, latency: Infinity };
      }
    });
    
    const results = await Promise.all(latencyTests);
    results.sort((a, b) => a.latency - b.latency);
    
    return results[0].server;
  }
}

High Bandwidth Utilization

class HighBandwidthManager {
  constructor() {
    this.bandwidthEstimate = 0;
    this.adaptiveStreaming = new AdaptiveStreamingManager();
  }

  async measureBandwidth() {
    const testSizes = [100, 500, 1000, 5000]; // KB
    const results = [];
    
    for (const size of testSizes) {
      const speed = await this.downloadSpeedTest(size);
      results.push(speed);
    }
    
    // Use median to avoid outliers
    results.sort((a, b) => a - b);
    this.bandwidthEstimate = results[Math.floor(results.length / 2)];
    
    this.adaptContentDelivery();
  }

  async downloadSpeedTest(sizeKB) {
    const start = performance.now();
    
    try {
      const response = await fetch(`/api/speedtest/${sizeKB}kb`);
      await response.blob();
      
      const end = performance.now();
      const duration = (end - start) / 1000; // seconds
      const speed = (sizeKB * 8) / duration; // kbps
      
      return speed;
    } catch (error) {
      console.error('Speed test failed:', error);
      return 0;
    }
  }

  adaptContentDelivery() {
    if (this.bandwidthEstimate > 50000) { // >50 Mbps (5G typical)
      this.enableUltraHighQuality();
    } else if (this.bandwidthEstimate > 10000) { // >10 Mbps (4G good)
      this.enableHighQuality();
    } else {
      this.enableStandardQuality();
    }
  }

  enableUltraHighQuality() {
    this.adaptiveStreaming.setQuality({
      video: '4k',
      audio: 'lossless',
      images: 'uncompressed',
      prefetch: 'aggressive'
    });
  }

  enableHighQuality() {
    this.adaptiveStreaming.setQuality({
      video: '1080p',
      audio: 'high',
      images: 'high',
      prefetch: 'moderate'
    });
  }

  enableStandardQuality() {
    this.adaptiveStreaming.setQuality({
      video: '720p',
      audio: 'standard',
      images: 'compressed',
      prefetch: 'conservative'
    });
  }
}

Network Slicing

class NetworkSlicingManager {
  constructor() {
    this.slices = {
      realtime: { priority: 'high', latency: 'ultra-low' },
      streaming: { priority: 'medium', bandwidth: 'high' },
      background: { priority: 'low', efficiency: 'high' }
    };
  }

  requestNetworkSlice(sliceType, requirements) {
    const slice = this.slices[sliceType];
    
    if (!slice) {
      throw new Error(`Unknown slice type: ${sliceType}`);
    }

    return this.negotiateSlice(slice, requirements);
  }

  async negotiateSlice(slice, requirements) {
    try {
      const response = await fetch('/api/network/slice', {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({
          slice,
          requirements,
          deviceId: this.getDeviceId(),
          location: await this.getLocation()
        })
      });

      if (response.ok) {
        const sliceInfo = await response.json();
        return this.configureSlice(sliceInfo);
      } else {
        throw new Error('Slice negotiation failed');
      }
    } catch (error) {
      console.error('Network slicing error:', error);
      return this.fallbackConfiguration();
    }
  }

  configureSlice(sliceInfo) {
    return {
      sliceId: sliceInfo.id,
      endpoint: sliceInfo.endpoint,
      qosParams: sliceInfo.qos,
      expiresAt: sliceInfo.expiresAt
    };
  }

  fallbackConfiguration() {
    return {
      sliceId: null,
      endpoint: '/api',
      qosParams: { priority: 'best-effort' },
      expiresAt: null
    };
  }
}

Edge Computing

Edge Processing

Edge computing, veriyi kullanıcıya daha yakın noktlarda işleyerek latency'yi azaltır.

Local Computation

class EdgeComputingManager {
  constructor() {
    this.edgeNodes = [];
    this.localProcessor = new LocalProcessor();
    this.cloudFallback = new CloudFallback();
  }

  async processData(data, processingType) {
    // Önce local processing'i dene
    if (this.canProcessLocally(processingType)) {
      try {
        return await this.localProcessor.process(data, processingType);
      } catch (error) {
        console.warn('Local processing failed, trying edge:', error);
      }
    }

    // Edge node'da processing
    if (this.hasAvailableEdgeNode()) {
      try {
        return await this.processAtEdge(data, processingType);
      } catch (error) {
        console.warn('Edge processing failed, falling back to cloud:', error);
      }
    }

    // Cloud fallback
    return await this.cloudFallback.process(data, processingType);
  }

  canProcessLocally(processingType) {
    const localCapabilities = {
      imageResize: true,
      textAnalysis: true,
      audioTranscode: false,
      videoProcessing: false,
      aiInference: this.hasLocalAI()
    };

    return localCapabilities[processingType] || false;
  }

  hasLocalAI() {
    // WebAssembly veya WebGL desteği kontrolü
    return typeof WebAssembly !== 'undefined' && 
           this.localProcessor.hasAICapabilities();
  }

  async processAtEdge(data, processingType) {
    const optimalEdge = await this.selectOptimalEdgeNode(processingType);
    
    if (!optimalEdge) {
      throw new Error('No suitable edge node available');
    }

    const response = await fetch(`${optimalEdge.endpoint}/process`, {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        data,
        type: processingType,
        priority: this.calculatePriority(processingType)
      })
    });

    if (!response.ok) {
      throw new Error(`Edge processing failed: ${response.statusText}`);
    }

    return await response.json();
  }

  async selectOptimalEdgeNode(processingType) {
    const suitableNodes = this.edgeNodes.filter(node => 
      node.capabilities.includes(processingType) && 
      node.status === 'available'
    );

    if (suitableNodes.length === 0) {
      return null;
    }

    // Load balancing ve latency'ye göre seçim
    const nodeScores = await Promise.all(
      suitableNodes.map(async node => {
        const latency = await this.measureLatency(node);
        const load = await this.getNodeLoad(node);
        
        // Lower score is better
        const score = latency + (load * 100);
        
        return { node, score };
      })
    );

    nodeScores.sort((a, b) => a.score - b.score);
    return nodeScores[0].node;
  }

  async measureLatency(node) {
    const start = performance.now();
    
    try {
      await fetch(`${node.endpoint}/ping`, { method: 'HEAD' });
      return performance.now() - start;
    } catch (error) {
      return Infinity;
    }
  }

  async getNodeLoad(node) {
    try {
      const response = await fetch(`${node.endpoint}/status`);
      const status = await response.json();
      return status.load || 1.0;
    } catch (error) {
      return 1.0; // Assume full load on error
    }
  }
}

class LocalProcessor {
  constructor() {
    this.workers = new Map();
    this.aiModel = null;
  }

  hasAICapabilities() {
    return this.aiModel !== null || this.canLoadAIModel();
  }

  canLoadAIModel() {
    // WebAssembly ve WebGL desteği kontrolü
    return typeof WebAssembly !== 'undefined' && 
           typeof WebGLRenderingContext !== 'undefined';
  }

  async process(data, type) {
    switch (type) {
      case 'imageResize':
        return this.resizeImage(data);
      case 'textAnalysis':
        return this.analyzeText(data);
      case 'aiInference':
        return this.runAIInference(data);
      default:
        throw new Error(`Unsupported processing type: ${type}`);
    }
  }

  async resizeImage(imageData) {
    return new Promise((resolve, reject) => {
      const worker = new Worker('/workers/imageProcessor.js');
      
      worker.postMessage({
        type: 'resize',
        data: imageData,
        targetWidth: 800,
        targetHeight: 600
      });

      worker.onmessage = (event) => {
        resolve(event.data);
        worker.terminate();
      };

      worker.onerror = (error) => {
        reject(error);
        worker.terminate();
      };
    });
  }

  async analyzeText(text) {
    // Simple local text analysis
    return {
      wordCount: text.split(' ').length,
      characterCount: text.length,
      sentiment: this.basicSentimentAnalysis(text),
      keywords: this.extractKeywords(text)
    };
  }

  basicSentimentAnalysis(text) {
    const positiveWords = ['good', 'great', 'excellent', 'amazing', 'wonderful'];
    const negativeWords = ['bad', 'terrible', 'awful', 'horrible', 'disappointing'];
    
    const words = text.toLowerCase().split(' ');
    let score = 0;
    
    words.forEach(word => {
      if (positiveWords.includes(word)) score++;
      if (negativeWords.includes(word)) score--;
    });
    
    if (score > 0) return 'positive';
    if (score < 0) return 'negative';
    return 'neutral';
  }

  extractKeywords(text) {
    const stopWords = ['the', 'is', 'at', 'which', 'on', 'and', 'a', 'to', 'as'];
    const words = text.toLowerCase()
      .replace(/[^\w\s]/g, '')
      .split(' ')
      .filter(word => word.length > 3 && !stopWords.includes(word));
    
    const frequency = {};
    words.forEach(word => {
      frequency[word] = (frequency[word] || 0) + 1;
    });
    
    return Object.entries(frequency)
      .sort((a, b) => b[1] - a[1])
      .slice(0, 5)
      .map(([word]) => word);
  }
}

Data Synchronization

class EdgeDataSync {
  constructor() {
    this.localStore = new LocalDataStore();
    this.edgeStore = new EdgeDataStore();
    this.cloudStore = new CloudDataStore();
    this.syncQueue = [];
  }

  async syncData(data, syncLevel = 'edge') {
    // Local'e hemen kaydet
    await this.localStore.save(data);
    
    // Sync level'e göre stratejik sync
    switch (syncLevel) {
      case 'local':
        // Sadece local
        break;
      case 'edge':
        await this.syncToEdge(data);
        break;
      case 'cloud':
        await this.syncToCloud(data);
        break;
      case 'all':
        await Promise.all([
          this.syncToEdge(data),
          this.syncToCloud(data)
        ]);
        break;
    }
  }

  async syncToEdge(data) {
    try {
      await this.edgeStore.save(data);
      
      // Background sync to cloud
      this.queueCloudSync(data);
    } catch (error) {
      console.error('Edge sync failed:', error);
      
      // Fallback to cloud sync
      await this.syncToCloud(data);
    }
  }

  async syncToCloud(data) {
    try {
      await this.cloudStore.save(data);
    } catch (error) {
      console.error('Cloud sync failed:', error);
      
      // Queue for retry
      this.queueCloudSync(data);
    }
  }

  queueCloudSync(data) {
    this.syncQueue.push({
      data,
      timestamp: Date.now(),
      retries: 0
    });
    
    this.processSyncQueue();
  }

  async processSyncQueue() {
    if (this.syncQueue.length === 0) return;
    
    const item = this.syncQueue.shift();
    
    try {
      await this.cloudStore.save(item.data);
    } catch (error) {
      if (item.retries < 3) {
        item.retries++;
        this.syncQueue.push(item);
      } else {
        console.error('Max retries reached for sync item:', item);
      }
    }
    
    // Process next item
    if (this.syncQueue.length > 0) {
      setTimeout(() => this.processSyncQueue(), 1000);
    }
  }

  async resolveConflicts(localData, edgeData, cloudData) {
    // Timestamp-based conflict resolution
    const candidates = [
      { data: localData, timestamp: localData.updatedAt, source: 'local' },
      { data: edgeData, timestamp: edgeData.updatedAt, source: 'edge' },
      { data: cloudData, timestamp: cloudData.updatedAt, source: 'cloud' }
    ].filter(item => item.data && item.timestamp);
    
    if (candidates.length === 0) return null;
    
    // Sort by timestamp (newest first)
    candidates.sort((a, b) => b.timestamp - a.timestamp);
    
    const winner = candidates[0];
    
    // Propagate winning version to other stores
    await this.propagateWinner(winner);
    
    return winner.data;
  }

  async propagateWinner(winner) {
    const promises = [];
    
    if (winner.source !== 'local') {
      promises.push(this.localStore.save(winner.data));
    }
    
    if (winner.source !== 'edge') {
      promises.push(this.edgeStore.save(winner.data));
    }
    
    if (winner.source !== 'cloud') {
      promises.push(this.cloudStore.save(winner.data));
    }
    
    await Promise.allSettled(promises);
  }
}

AI/ML Integration

Network Optimization

AI ve ML, network behavior'ını öğrenerek optimize eder.

Predictive Loading

class PredictiveLoader {
  constructor() {
    this.userBehaviorModel = new UserBehaviorModel();
    this.prefetchCache = new Map();
    this.learningEnabled = true;
  }

  async predictAndPrefetch(currentContext) {
    if (!this.learningEnabled) return;

    const predictions = await this.userBehaviorModel.predict(currentContext);
    
    // High confidence predictions için prefetch
    const highConfidencePredictions = predictions.filter(p => p.confidence > 0.7);
    
    await Promise.all(
      highConfidencePredictions.map(prediction => 
        this.prefetchResource(prediction)
      )
    );
  }

  async prefetchResource(prediction) {
    if (this.prefetchCache.has(prediction.resource)) {
      return; // Already prefetched
    }

    try {
      const response = await fetch(prediction.resource, {
        method: 'GET',
        headers: { 'X-Prefetch': 'true' },
        priority: 'low' // Background prefetch
      });

      if (response.ok) {
        const data = await response.blob();
        this.prefetchCache.set(prediction.resource, {
          data,
          timestamp: Date.now(),
          prediction
        });

        console.log(`Prefetched: ${prediction.resource} (confidence: ${prediction.confidence})`);
      }
    } catch (error) {
      console.warn('Prefetch failed:', prediction.resource, error);
    }
  }

  learnFromUserAction(action, context) {
    if (!this.learningEnabled) return;

    this.userBehaviorModel.addTrainingData({
      action,
      context,
      timestamp: Date.now()
    });

    // Validate prediction accuracy
    this.validatePredictions(action);
  }

  validatePredictions(actualAction) {
    // Check if we correctly predicted this action
    const recentPredictions = Array.from(this.prefetchCache.values())
      .filter(item => Date.now() - item.timestamp < 60000); // Last minute
    
    const correctPrediction = recentPredictions.find(item => 
      item.prediction.resource === actualAction.resource
    );

    if (correctPrediction) {
      this.userBehaviorModel.recordSuccess(correctPrediction.prediction);
    } else {
      this.userBehaviorModel.recordMiss(actualAction);
    }
  }

  getPrefetchedResource(resource) {
    const cached = this.prefetchCache.get(resource);
    
    if (cached && this.isCacheValid(cached)) {
      this.prefetchCache.delete(resource); // Use once
      return cached.data;
    }
    
    return null;
  }

  isCacheValid(cached) {
    const TTL = 300000; // 5 minutes
    return Date.now() - cached.timestamp < TTL;
  }
}

class UserBehaviorModel {
  constructor() {
    this.trainingData = [];
    this.model = null;
    this.patterns = new Map();
  }

  async predict(context) {
    // Simple pattern-based prediction
    const contextKey = this.getContextKey(context);
    const pattern = this.patterns.get(contextKey);
    
    if (!pattern) {
      return [];
    }

    // Return predictions based on learned patterns
    return pattern.nextActions.map(action => ({
      resource: action.resource,
      confidence: action.frequency / pattern.totalCount,
      action
    }));
  }

  addTrainingData(dataPoint) {
    this.trainingData.push(dataPoint);
    
    // Update patterns
    this.updatePatterns(dataPoint);
    
    // Retrain model periodically
    if (this.trainingData.length % 100 === 0) {
      this.retrainModel();
    }
  }

  updatePatterns(dataPoint) {
    const contextKey = this.getContextKey(dataPoint.context);
    
    if (!this.patterns.has(contextKey)) {
      this.patterns.set(contextKey, {
        nextActions: [],
        totalCount: 0
      });
    }

    const pattern = this.patterns.get(contextKey);
    
    // Update action frequency
    const existingAction = pattern.nextActions.find(a => 
      a.resource === dataPoint.action.resource
    );

    if (existingAction) {
      existingAction.frequency++;
    } else {
      pattern.nextActions.push({
        resource: dataPoint.action.resource,
        frequency: 1,
        ...dataPoint.action
      });
    }

    pattern.totalCount++;
  }

  getContextKey(context) {
    // Create a simple context key
    return `${context.page}_${context.timeOfDay}_${context.userType}`;
  }

  async retrainModel() {
    // Simple retraining - in production, use TensorFlow.js or similar
    console.log('Retraining model with', this.trainingData.length, 'data points');
    
    // Cleanup old patterns
    this.cleanupOldPatterns();
  }

  cleanupOldPatterns() {
    // Remove patterns with low confidence
    for (const [key, pattern] of this.patterns.entries()) {
      if (pattern.totalCount < 5) {
        this.patterns.delete(key);
      }
    }
  }

  recordSuccess(prediction) {
    // Increase confidence for successful predictions
    console.log('Prediction success:', prediction.resource);
  }

  recordMiss(action) {
    // Learn from missed predictions
    console.log('Prediction miss:', action.resource);
  }
}

Adaptive Compression

class AdaptiveCompressionManager {
  constructor() {
    this.compressionStrategies = new Map();
    this.performanceMetrics = new Map();
    this.aiOptimizer = new CompressionAI();
  }

  async compressData(data, contentType, context) {
    const strategy = await this.selectOptimalStrategy(contentType, context);
    
    const startTime = performance.now();
    const compressedData = await this.applyCompression(data, strategy);
    const endTime = performance.now();
    
    // Record performance metrics
    this.recordMetrics(strategy, {
      originalSize: data.length,
      compressedSize: compressedData.length,
      compressionTime: endTime - startTime,
      compressionRatio: compressedData.length / data.length
    });

    return compressedData;
  }

  async selectOptimalStrategy(contentType, context) {
    // AI-based strategy selection
    const recommendation = await this.aiOptimizer.recommendStrategy({
      contentType,
      context,
      historicalData: this.getHistoricalMetrics(contentType)
    });

    if (recommendation.confidence > 0.8) {
      return recommendation.strategy;
    }

    // Fallback to rule-based selection
    return this.getRuleBasedStrategy(contentType, context);
  }

  async applyCompression(data, strategy) {
    switch (strategy.algorithm) {
      case 'gzip':
        return await this.gzipCompress(data, strategy.level);
      case 'brotli':
        return await this.brotliCompress(data, strategy.level);
      case 'lz4':
        return await this.lz4Compress(data);
      case 'webp':
        return await this.webpCompress(data, strategy.quality);
      case 'avif':
        return await this.avifCompress(data, strategy.quality);
      default:
        throw new Error(`Unknown compression algorithm: ${strategy.algorithm}`);
    }
  }

  async gzipCompress(data, level = 6) {
    // Use CompressionStream API if available
    if ('CompressionStream' in window) {
      const stream = new CompressionStream('gzip');
      const writer = stream.writable.getWriter();
      const reader = stream.readable.getReader();
      
      writer.write(new TextEncoder().encode(data));
      writer.close();
      
      const chunks = [];
      let done = false;
      
      while (!done) {
        const { value, done: streamDone } = await reader.read();
        done = streamDone;
        if (value) chunks.push(value);
      }
      
      return new Uint8Array(chunks.reduce((acc, chunk) => [...acc, ...chunk], []));
    }
    
    // Fallback to Web Worker
    return this.workerCompress(data, 'gzip', { level });
  }

  async webpCompress(imageData, quality = 80) {
    return new Promise((resolve, reject) => {
      const canvas = document.createElement('canvas');
      const ctx = canvas.getContext('2d');
      const img = new Image();
      
      img.onload = () => {
        canvas.width = img.width;
        canvas.height = img.height;
        ctx.drawImage(img, 0, 0);
        
        canvas.toBlob(resolve, 'image/webp', quality / 100);
      };
      
      img.onerror = reject;
      img.src = URL.createObjectURL(new Blob([imageData]));
    });
  }

  recordMetrics(strategy, metrics) {
    const key = strategy.algorithm;
    
    if (!this.performanceMetrics.has(key)) {
      this.performanceMetrics.set(key, []);
    }
    
    this.performanceMetrics.get(key).push({
      ...metrics,
      timestamp: Date.now(),
      strategy
    });
    
    // Feed data to AI optimizer
    this.aiOptimizer.addTrainingData(strategy, metrics);
  }

  getHistoricalMetrics(contentType) {
    const allMetrics = Array.from(this.performanceMetrics.values()).flat();
    
    return allMetrics
      .filter(metric => metric.strategy.contentType === contentType)
      .slice(-100); // Last 100 operations
  }

  getRuleBasedStrategy(contentType, context) {
    // Fallback rule-based strategy selection
    switch (contentType) {
      case 'text/html':
      case 'text/css':
      case 'application/javascript':
        return { algorithm: 'gzip', level: 6 };
      
      case 'image/jpeg':
      case 'image/png':
        return { algorithm: 'webp', quality: this.getImageQuality(context) };
      
      case 'application/json':
        return { algorithm: 'brotli', level: 4 };
      
      default:
        return { algorithm: 'gzip', level: 6 };
    }
  }

  getImageQuality(context) {
    if (context.networkSpeed === 'slow') return 60;
    if (context.networkSpeed === 'fast') return 90;
    return 75; // default
  }
}

class CompressionAI {
  constructor() {
    this.trainingData = [];
    this.model = null;
  }

  async recommendStrategy(input) {
    // Simple heuristic-based recommendation
    // In production, use TensorFlow.js or similar ML library
    
    const { contentType, context, historicalData } = input;
    
    if (historicalData.length === 0) {
      return { strategy: null, confidence: 0 };
    }
    
    // Find best performing strategy for similar content
    const relevantMetrics = historicalData
      .filter(metric => metric.strategy.contentType === contentType)
      .sort((a, b) => a.compressionRatio - b.compressionRatio); // Better compression first
    
    if (relevantMetrics.length === 0) {
      return { strategy: null, confidence: 0 };
    }
    
    const bestStrategy = relevantMetrics[0].strategy;
    const confidence = Math.min(relevantMetrics.length / 10, 1.0); // More data = higher confidence
    
    return { strategy: bestStrategy, confidence };
  }

  addTrainingData(strategy, metrics) {
    this.trainingData.push({
      input: strategy,
      output: metrics,
      timestamp: Date.now()
    });
    
    // Retrain periodically
    if (this.trainingData.length % 50 === 0) {
      this.retrain();
    }
  }

  async retrain() {
    console.log('Retraining compression AI with', this.trainingData.length, 'samples');
    
    // In production, implement actual ML training here
    // For now, just cleanup old data
    this.trainingData = this.trainingData.slice(-500); // Keep last 500 samples
  }
}

Bu future trends ve emerging technologies, mobile networking'in geleceğini şekillendiriyor. 5G, edge computing ve AI/ML integration ile daha hızlı, daha akıllı ve daha efficient network solutions mümkün oluyor.