  This paper aims to design robust Edge Intelligence using semantic
communication for time-critical IoT applications. We systematically analyze the
effect of image DCT coefficients on inference accuracy and propose the
channel-agnostic effectiveness encoding for offloading by transmitting the most
meaningful task data first. This scheme can well utilize all available
communication resource and strike a balance between transmission latency and
inference accuracy. Then, we design an effectiveness decoding by implementing a
novel image augmentation process for convolutional neural network (CNN)
training, through which an original CNN model is transformed into a Robust CNN
model. We use the proposed training method to generate Robust MobileNet-v2 and
Robust ResNet-50. The proposed Edge Intelligence framework consists of the
proposed effectiveness encoding and effectiveness decoding. The experimental
results show that the effectiveness decoding using the Robust CNN models
perform consistently better under various image distortions caused by channel
errors or limited communication resource. The proposed Edge Intelligence
framework using semantic communication significantly outperforms the
conventional approach under latency and data rate constraints, in particular,
under ultra stringent deadlines and low data rate.
