Edge Computing ve IoT: Gerçek Zamanlı Veri İşleme

Edge Computing Neden Kritik?

IoT cihaz sayısı 2025'te 75 milyarı aştı. Bu kadar veriyi cloud'a göndermek imkansız. Edge computing, veriyi kaynağında işleyerek latency'yi düşürüyor ve bandwidth tasarrufu sağlıyor.

Edge vs Cloud Computing

Use Cases

• Autonomous Vehicles: Real-time decision making
• Smart Manufacturing: Predictive maintenance
• Healthcare: Patient monitoring, medical imaging
• Retail: Inventory management, customer analytics
• Smart Cities: Traffic management, public safety

Edge Computing Stack

• Hardware: NVIDIA Jetson, Intel NUC, Raspberry Pi
• OS/Runtime: AWS Greengrass, Azure IoT Edge, K3s
• Frameworks: TensorFlow Lite, OpenVINO
• Protocols: MQTT, CoAP, OPC UA
• Security: TPM, secure boot, encryption

Implementation Örneği

# Edge device Python script
import edge_impulse_linux
import cv2
import numpy as np

# ML model yükleme
model = edge_impulse_linux.ImpulseRunner(modelfile)

# Real-time inference
camera = cv2.VideoCapture(0)
while True:
    ret, frame = camera.read()
    # Preprocess
    processed = preprocess_image(frame)
    # Inference at edge
    result = model.classify(processed)
    # Local action
    if result['anomaly'] > 0.8:
        trigger_alert()