Bayesian Optimization-Based Time-Sensitive and Power-Efficient DNN Task Partitioning in a Dynamic IoT Computing System

Deploying deep neural networks (DNNs) on resource-constrained Internet of Things (IoT) devices is challenging due to limited processing power, energy constraints, and stringent latency requirements. This article introduces a new method to split DNN tasks in IoT systems, focusing on saving power and reducing delays. We use Gaussian process regression (GPR) to predict how long tasks will take under different conditions. We also use a simple linear regression model to estimate how much power IoT devices use based on their CPU usage. These predictive models are integrated into a Bayesian optimization framework to determine the optimal DNN task partitioning point, balancing latency and energy efficiency. The system adjusts to changes in the network and device conditions, ensuring it works well in different situations. Experiments on a heterogeneous IoT testbed demonstrate that GPR accurately predicts execution latencies, and the linear regression model provides reliable power consumption estimates. The Bayesian optimization algorithm efficiently explores the tradeoff space, offering low power consumption and high latency satisfaction rates. Our code is shared for public use<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nucleusbiao/Time-Sensitive-and-Power-Efficient-DNN-Task-Partitioning</uri>.