Energy-efficient design for green indoor OWC-IoT systems using passive reflective filters and machine learning-assisted quality prediction

Abstract

This paper presents an energy-efficient design of optical wireless communication (OWC) system for the indoor Internet of Things (IoT) with the assistance of machine learning (ML). A central coordinator (CC) has been proposed to interrogate the IoT devices and control the uplink formations with the prediction of transmission quality using ML classifiers. The passive reflective reflectors (PRF) are utilized in the IoT devices, which replaced the power-consuming active transmitters, formulate the zero-power consuming transmission links. The communication performance of the passive link establishments from the IoT devices have been investigated in terms of quality factor (Q-factor), bit error rate (BER), and signal-to-noise ratio (SNR) under different optical wireless channel conditions and link lengths. The ML classifiers have been evaluated on the prediction of transmission quality, and the results suggested the Euclidean K-nearest neighbor (KNN) with ten number of neighbors for the implementation. The IoT devices located within 1.2 m from the CC require a transmission power of 0.5 mW for links carrying 10 Gbps data, which increases the energy efficiency to 20 Gbps/mW with transmission energy consumption of 0.05 pJ/bit. This significant improvement in energy efficiency and passive communication ensures reliable, and green IoT links suitable for data-intensive indoor applications.