Artificial intelligence models will continue to be pervasively deployed to support diverse intelligent Internet of Things (IoT) applications in the 5G/6G era. Many such applications rely on deep neural networks (DNN) for object classification. In this presentation, DNN inference uses a pre-trained DNN model to process an input data sample such as raw sensing data, and generates a classification result. We will discuss when to offload DNN inference computation from resource constrained IoT devices to the edge and how to incorporate different contributions from multiple random DNN inference results to improve task classification accuracy, while achieving high transmission, computation, and energy resource utilization.