Design and Implementation of 2D MIMO-Based Optical Camera Communication Using a Light-Emitting Diode Array for Long-Range Monitoring System

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate.

Radio frequency (RF) waveforms are widely used in wireless technologies, such as mobile system, smart factory, and smart home systems. Due to its simplicity of installation, wireless communication is better than wire communication. However, the use of high frequencies for data transmission can be harmful to human health. To replace RF waveform for communication system, Optical Wireless Communication (OWC), which deploys visible light waveform for communication, was researched widely by researchers around the world. Visible Light Communication (VLC), Light Fidelity (LiFi), and Optical Camera Communication (OCC) are the three candidates for replacing RF system. In this research, we present an multiple-input multiple-output (MIMO) modulation system based on on-off keying (OOK) modulation in the time domain and a light-emitting diode (LED) array. By adjusting the exposure time, focal length, forward error correction, and Deep learning for object detection, it is still possible to implement this technique with 10 links in various locations at a distance of 20 m with a velocity of 2 m/s.

Currently, wireless communication systems that use radio frequency are commonly deployed, for example, mobile communication systems, satellite systems, and the Internet of Things (IoT) systems. Based on their easy installation, wireless communication systems have benefits over other wired communication systems. However, using high frequencies to transfer data via wireless communication can hold significant risks for human health. Several researchers have studied this topic using visible light instead of Radio Frequency (RF) waveforms in communication systems. Many potential approaches are relevant in this regard, i.e., visible light communication, light fidelity, free-space optical, and optical camera communication. Artificial intelligence is also influencing the future of industry and people and is used to solve complex problems, create intelligent solutions, and replace human intelligence as the driving force behind emerging technologies such as big data, smart factories, and the IoT. In this paper, we proposed the architecture of the MIMO C-OOK (Multiple-Input Multiple-Output Camera On–Off Keying) scheme, which uses a convolutional neural network for light-emitting diode detection and a deep learning neural network for threshold predictions considering long-distance communication and mobility support. Our suggested method aimed to improve the performance of the traditional camera on–off keying scheme by increasing data rate, communication distance, and low bit error rate. Our suggested technique may achieve a communication distance of up to 22 m with a low error rate when considering the mobility impact (2 m/s, i.e., walking velocity) by controlling the exposure time, focal length, and employing Forward Error Correction code.

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.

This paper experimentally investigates the performance of visible light based optical camera communications (OCC) link with motion detection (MD) for the optical Internet of things applications. This efficient MD can be considered another functionality of OCC in addition to traditional features of vision, illumination data communications and sensing. The experiments were conducted in an indoor static downlink OCC system employing a mobile phone front camera is employed as the receiver and an $8\times 8$ red, green, and blue (RGB) light-emitting diode array as the transmitter. The motion is detected by observing the user's finger movement in the form of centroid through the OCC link via a camera. The experiment results demonstrate that, the proposed scheme can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 80 cm. We show a BER of $1.7\times 10^{-3}$ below the forward error correction limit of $3.8\times 10^{-3}$ over a transmission distance of up to 1 m. The proposed neuron based MD combined together with OCC can be considered an efficient system, which provides illumination, communications, and motion detection in a convenient smart home environment.

Radio-frequency technologies are widely applied in many fields such as mobile systems, healthcare systems, television and radio broadcasting, and satellite communications. However, one major problem in wireless communication based on radio frequencies is its impact on human health. High frequencies adversely impact human health more than low frequencies if the signal power transgresses the permissible threshold. Therefore, researchers are investigating the use of visible light waves (instead of the radio-frequency band) for data transmission in three major areas: visible light communication, light fidelity, and optical camera communication. In this paper, we propose a scheme that upgrades the camera on–off keying (COOK) scheme by using it with the multiple-input multiple-output (MIMO) scheme; COOK has been recommended by the IEEE 802.15.7-2018 standard. By applying technologies, such as matched filter, region of interest, and MIMO, our proposed scheme promises to improve the performance of the conventional scheme by improving the data rate, communication distance, and bit error rate. By controlling the exposure time, the focal length in a single camera and using channel coding, our proposed scheme can achieve the communication distance of up to 20 m, with a low error rate.

In this paper, a novel method is introduced to enhance the performance of vehicle-to-vehicle (V2V) visible light communication (VLC) by employing different transmitter (Tx) light-emitting diode (LED) array arrangements with different LED orientations. Improving the signal-to-noise ratio (SNR) is crucial for V2V VLC systems to provide long communication ranges. For this purpose, six transmitter configurations are proposed: single-LED transmitters, as well as 3 × 3 square-, single hexagonal-, octagonal-, 5 × 5 square-, and honeycomb hexagonal-shaped LED arrays. Indoor VLC studies using LED arrays offer a uniform SNR, while outdoor studies focus on optimizing the receiver side to enhance system performance. This paper optimizes system performance by increasing the SNR and communication range of V2V VLC systems by changing the geometry of the Tx LED array and LED orientations. A V2V VLC system using on–off keying (OOK) is modeled in MATLAB, and the SNR and bit error rate (BER) are simulated for different Tx configurations. Our results show that the honeycomb hexagonal transmitter design provides a 19% improvement in system performance with a spacing of 1 cm, and maintains a 16% improvement when the array size is reduced by a factor of 100, making it smaller than one of the smallest industrial headlight modules.

Optical camera communication (OCC) provides license-free communication channels without radio waves. The rolling shutter mechanism is expected to enhance the capacity of wireless links in OCC. However, signal erasure occurs due to waiting times between frames in consecutive signal transmission. To address this issue, we propose a symbol timing synchronization for OCC using a rolling shutter camera. The receiver camera synchronizes to the transmitter with additional waiting time which is computed based on the defined preamble signals. We analyzed the performance to clarify the theoretical limit. The feasibility of the proposed idea was confirmed with experimental results.

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution.

Modern automotive vehicles are often equipped with a budget commercial rolling shutter (RS) camera. These devices usually produce distorted images due to the camera's inter-row delay while capturing the image. They will defeat the following computer vision tasks, such as ego-motion estimation. In this paper, we present a closed-form solver for a vehicle-mounted RS camera ego-motion estimation problem with only one point-match between two consecutive distorted RS images, called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RS-1 pt</b>. To the best of our knowledge, this is the first work to address the ego-motion estimation problem for vehicle-mounted RS cameras specifically. Thanks to the 1 pt minimal solver, extensive experiments demonstrate the proposed method surpasses classical global shutter based (GS-based) solvers and existing RS-based solvers by a significant margin in terms of both efficiency and effectiveness. Besides, we synthesize and self-collect three vehicle-mounted RS camera datasets with ground-truth GS images and trajectory, which are absent from the existing works and will be publicly available as a benchmark to the research community.

This paper presents a method for performing traffic light detection using computer vision. Reliable traffic light detection and classification is crucial for automated driving in urban environments. By using big data and artificial intelligence, a complex dataset belonging to an urban area in China is preprocessed to determine the level of vehicular congestion, and then different machine learning algorithms are applied to a dataset of traffic light images in order to validate them in the urban environment to be studied, this process is explained step by step. The models obtained in this work can be applied in optical camera communication (OCC) systems, and also in intelligent transportation systems (ITS), using tracking channels for visible light communication (VLC). The two optical channels, VLC and OCC, are simulated in terms of the quality of information received in order to apply the previously generated datasets. In this work, a traffic light feature dataset has been generated from images and two traffic light classification models present in images and video frames have been generated from their features, obtaining a maximum accuracy of 94.52 %.

LEDs are quickly becoming the standard lighting solution. Their ability to be turned on and off fast determined scientists to create novel communication methods, such as Visible Light Communication (VLC), in which the transmitter is commonly represented by LEDs and the receiver is represented by photodetectors. The receiver in Optical Camera Communications (OCC) is an image sensor. Designing a communication system that can use low frame rate cameras (30 frames per second) while preventing light flickering at the transmitter is a specific problem in OCC. This work proposes and experimentally confirms an improvement of an OCC solution that does not cause flickering at the transmitter and does not require synchronization between the transmitter and the receiver, while improving the bit rate.

Data rate and communication distance are two important criteria for measuring the performance of optical camera communication (OCC) systems. Intensive researches have been made to find possible solutions to improve these two performance indicators. However, most existing literature either provides solutions which closely related to the experimental parameters of their own designed OCC systems or provides models which are suitable for visible light communication (VLC) and cannot be directly used in OCC. Motivated by this, we propose a simplified system model for OCC in this paper. By jointly applying Lambertian model for the transmitter light-emitting diode (LED), channel direct current (DC) gain for free space channel, and mathematical descriptions for the imaging process of the camera receiver, the proposed model provides the relationship between the optical transmission power and the final pixel values on the image sensor. We specifically explore the parameters of the transmission distance and the exposure time based on the proposed model. Simulation results qualitatively and quantitatively explore the influences of these two parameters on the final pixel values. Our proposed model can provide guidance for the design of a generalized OCC system as well as the selection of demodulating threshold, thus it can help to realize a highperformance OCC system.

Communicate to illuminate: State-of-the-art and research challenges for visible light communications

As mobile payments become increasingly popular, the security of the transaction process should be taken more seriously. Many mobile phones are equipped with complementary metal-oxide-semiconductor (CMOS) cameras, which can be used as visible light communication (VLC) receivers. An optical camera communication (OCC) system is a special VLC system that uses an optical camera as a receiver. OCC offers acceptable security, but challenges remain. In this paper, we propose an OCC system that uses an optical camera with a rolling shutter as a receiver and a light-emitting diode (LED) panel light as a transmitter. The feature of an LED panel light serving as a surface light source reduces the possibility of overexposure and avoids the blooming effect of the camera. The rolling shutter effect of the CMOS camera is used to increase the data rate, which is numerically much higher than the frame rate. Finally, the results of a symbol error rate (SER) test show that the VLC system exhibits error-free performance. The data rate is approximately 4.2kb/s, while the net data rate is approximately 2.46kb/s due to the redundancy of the 3-9 code bar code and the redundant cycle. This data rate is sufficient for mobile payments, which require only 60bits of data at a time.