A machine learning-based approach for detecting communication failures in internet of things networks

Abstrak

In industrial systems, the exchange of massive content, such as high-quality video and large sensing data, among industrial internet of things devices (IIoTDs) is essential, often under strict deadlines. Utilizing millimeter-wave (mmWave) frequencies at 28 and 60 GHz can meet the requirements of industrial internet of things (IIoT) by offering high data rates. However, in the mmWave band, the use of directional antennas is imperative due to the short wavelength, rendering directional links susceptible to adverse effects like deafness problems, where a communicating node fails to receive signals from other transmitting nodes. To mitigate the deafness problem, this paper proposes a machine learning-based communication failure identification scheme for reliable device-to-device (D2D) communication in the mmWave band. The proposed scheme determines the type of network failure (deafness/interference) based on the IIoTD's state parameters. Furthermore, we introduce machine learning based directional medium access control (ML-DMAC) to enhance throughput and minimize the duration of deafness in D2D communication. Performance evaluations demonstrate that the proposed ML-DMAC outperforms existing schemes, achieving approximately 31% higher aggregate throughput and an 88% reduction in deafness duration.