A protocol generation model for protocol-unknown IoT devices

Abstract

The rapid growth of Internet of Things (IoT) applications depends on the deployment of numerous heterogeneous devices, and the deployed devices require various communication protocols to be accessed. Matching the correct protocol for accessed devices, particularly those with unknown protocols, is a complex and challenging task due to the diversity of device types, the growing number of protocols, and the reliance on domain-specific knowledge. To address these challenges, we propose a Device Clustering and Deep Reinforcement Learning-based Protocol Generation Model (DCDPM). The DCDPM generates the best-matched protocol for protocol-unknown IoT devices using only device basic information (DBI). The DCDPM employs a two-stage device clustering mechanism based on DBI similarity density to generate device clusters, and extracts protocol features from these clusters. Furthermore, a Weight Twin Delay-DDPG (WTD-DDPG), an enhanced deep reinforcement learning (DRL) method, is developed to determine the optimal weights for identifying the optimal device cluster. The WTD-DDPG addresses issues related to continuous action space and Q-value overestimation. Lastly, a feature-original fusion mechanism is designed to further enhance protocol matching by fusing the extracted protocol features with the original protocols within the optimal device cluster. Experimental validation of the DCDPM is conducted within two distinct scenarios: a communication base station and a copper smelting production line. A device library containing 1296 devices is created and 130 devices are tested. Experimental results demonstrate that DCDPM outperforms existing methods in terms of protocol matching rate, hit rate, and network traffic consumption.