Predicting Delay in IoT Using Deep Learning: A Multiparametric Approach

Abstract

The proliferation of the Internet of Things (IoT) requires to accommodate diverse applications with stringent performance requirements. Delay is one of the key metrics in the IoT, particularly, for domains, such as health care, where critical cases requiring an emergency response frequently occur. In this paper, we analyze the performance data generated using the IEEE 802.15.4 standard to derive an accurate predictive model for delay-sensitive applications. A deep neural network (DNN) is adopted to model the relationship between diverse communication parameters (e.g., queue size, application traffic rate, and transmission power) and delay. Evaluation reveals that the DNN model achieves a prediction accuracy of over 98% and outperforms other popular regression models. In addition, a fine-grained analysis of the size of training data, depth (number of layers), width (number of neurons per layer), and epochs (number of iterations) is carried out in an attempt to achieve best possible prediction results with minimally complex DNN. The statistics show that the derived model achieves a comparable accuracy even when trained with a small fraction (≥10%) of data. The proposed model recommends the values for different controllable communication parameters to the transmitter that can be fine-tuned considering the desired delay bounds.

Keywords

• Delay prediction
• deep learning
• e-health
• internet of things
• multi-layer neural networks
• wireless sensor networks