We present a machine and deep learning method to offload trained deep learning model and transmit packets efficiently on resource-constrained internet of things (IoT) edge devices and networks. Recently, the types of IoT devices have become diverse and the volume of data has been increasing, such as images, voice, and time-series sensory signals generated by various devices. However, transmitting large amounts of data to a server or cloud becomes expensive owing to limited bandwidth, and leads to latency for time-sensitive operations. Therefore, we propose a novel offloading and transmission policy considering energy-efficiency, execution time, and the number of generated packets for resource-constrained IoT edge devices that run a deep learning model and a reinforcement learning method to find an optimal contention window size for effective channel access using a contention-based medium access control (MAC) protocol. A Reinforcement learning is used to improve the performance of the applied MAC protocol. Our proposed method determines the offload and transmission strategies that are better to directly send fragmented packets of raw data or to send the extracted feature vector or the final output of deep learning networks, considering the operation performance and power consumption of the resource-constrained microprocessor, as well as the power consumption of the radio transceiver and latency for transmitting the all the generated packets. In the performance evaluation, we measured the performance parameters of ARM Cortex-M4 and Cortex-M7 processors for the network simulation. The evaluation results show that our proposed adaptive channel access and learning-based offload and transmission methods outperform conventional role-based channel access schemes. They transmit packets of raw data and are effective for IoT edge devices and network protocols.
High-performance and energy-efficient deep learning for resource-constrained
devices
