Internet of Things (IoT) is enabled by the integration of communication and sensor systems that are used to collect important information from objects around the world. In this article, we proposed the integration of laser-based optical wireless communication (OWC) with a fiber sensor system for IoT applications. OWC can be utilized in a harsh environment to send and receive communication and sensor signals in a wireless means. Moreover, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. As compared to mechanical and electrical sensors, optical fiber sensors have numerous benefits, including the tolerance to electromagnetic interference (EMI), small size, lightweight, high bandwidth, low cost, provide distributed sensing, high sensitivity, and electronic devices are not required at the sensor point. Moreover, we proposed a deep variational autoencoder (DVAE) model to estimate the strain changes and peak wavelength of multiple fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment. The result showed that our proposed DVAE model can estimate the strain changes and peak wavelength of FBGs with low root mean squared error (RMSE) and high estimation accuracy. Furthermore, the performance of the proposed integration of OWC with the fiber sensor system achieves a clear eye diagram and excellent bit error rate (BER) curve.

A fiber Bragg grating (FBG) sensor is a favorable sensor in measuring strain, pressure, vibration, and temperature in different applications, such as in smart structures, wind turbines, aerospace, industry, military, medical centers, and civil engineering. FBG sensors have the following advantages: immune to electromagnetic interference, light weight, small size, flexible, stretchable, highly accurate, longer stability, and capable in measuring ultra-high-speed events. In this paper, we propose and demonstrate an intensity and wavelength division multiplexing (IWDM) FBG sensor system using a Raman amplifier and extreme learning machine (ELM). We use an IWDM technique to increase the number of FBG sensors. As the number of FBG sensors increases and the spectra of two or more FBGs are overlapped, a conventional peak detection (CPD) method is unappropriate to detect the central Bragg wavelength of each FBG sensor. To solve this problem, we use ELM techniques. An ELM is used to accurately detect the central Bragg wavelength of each FBG sensor even when the spectra of FBGs are partially or fully overlapped. Moreover, a Raman amplifier is added to a fiber span to generate a gain medium within the transmission fiber, which amplifies the signal and compensates for the signal losses. The transmission distance and the sensing signal quality increase when the Raman pump power increases. The experimental results revealed that a Raman amplifier compensates for the signal losses and provides a stable sensing output even beyond a 45 km transmission distance. We achieve a remote sensing of strain measurement using a 45 km single-mode fiber (SMF). Furthermore, the well-trained ELM wavelength detection methods accurately detect the central Bragg wavelengths of FBG sensors when the two FBG spectra are fully overlapped.

In this work, we designed a novel hybrid of free-space optics (FSO) communication and intensity and wavelength division multiplexing (IWDM)-fiber Bragg grating (FBG) sensor system over a single shared FSO transmission channel for both optical and sensor signal transmission. In the proposed system, we use one broadband light source and one FSO link for both optical and FBG sensor signal transmission, which significantly reduces the cost of system installation and complexity. Besides, the FSO transmission channel is used to solve the problem of geographical constraints when the environment is difficult to install the fiber. In addition, IWDM is one of the best multiplexing technique to increase the sensor number by more than two times the conventional wavelength division multiplexing (WDM) method. Besides, to solve the overlap spectra problem of IWDM based FBG sensor system, we proposed the stacked gated recurrent unit (SGRU) algorithm with the neural network (NN) profile method. The results prove that the proposed SGRU with the NN profile method improves the accuracy of wavelength detection techniques with the Gaussian profile method. Moreover, the FSO transmission channel achieves a clear eye diagram even the system is a hybrid of optical communication and FBG sensors system.

This paper proposes a new fiber Bragg grating central wavelength interrogation system by combining evolutionary algorithm and machine learning techniques integrated with an unsupervised autoencoder (AE) pre-training strategy. The proposed unsupervised AE pre-training convolution neural network (CNN) allows training of the convolutional layers independently from a regression task in order to learn a new data representation and give better generalization. It is also used to improve the system accuracy by four times without extra-labeled data. Moreover, AE is combined with a differential evolutionary (DE) algorithm to automate the human labeling task. The proposed autoencoder pre-training convolution neural network and differential evolutionary (AECNNDE) interrogation system achieve good accuracy and can speed-up the computational time by a maximum of 30 times than DE algorithm.