Sensing and communication technology has been used successfully in various event monitoring applications over the last two decades, especially in places where long-term manual monitoring is infeasible. However, the major applicability of this technology was mostly limited to terrestrial environments. On the other hand, underwater wireless sensor networks (UWSNs) opens a new space for the remote monitoring of underwater species and faunas, along with communicating with underwater vehicles, submarines, and so on. However, as opposed to terrestrial radio communication, underwater environment brings new challenges for reliable communication due to the high conductivity of the aqueous medium which leads to major signal absorption. In this paper, we provide a detailed technical overview of different underwater communication technologies, namely acoustic, magnetic, and visual light, along with their potentials and challenges in submarine environments. Detailed comparison among these technologies have also been laid out along with their pros and cons using real experimental results.

Visual identification of objects using cameras requires precise detection, localization, and recognition of the objects in the field-of-view. The visual identification problem is very challenging when the objects look identical and features between distinct objects are indistinguishable, even with state-of-the-art computer vision techniques. The problem becomes significantly more challenging when the objects themselves do not carry rich geometric and photometric features, for example, in visual identification and tracking of light emitting diodes (LED) for visible light communication (VLC) applications. In this paper, we present a camera based visual identification solution where objects or regions of interest are tagged with an actively transmitting LED. Motivated by the concept of pilot symbols, typically used for synchronization and channel estimation in radio communication systems, the LED actively transmits unique pilot symbols which are detected by the camera across a series of image frames using our proposed spatio-temporal correlation based algorithm. We setup the visual identification as a problem of localization of the LED on the camera image, which involves identifying the ( pixels ) and the unique ID corresponding to the LED. In this paper, we present the algorithm and trace-based evaluation of the identification accuracy under real-world conditions including indoor, outdoor, static and mobile scenarios. In addition to micro-benchmarking the localization accuracy of our technique across different parameter configurations, we show that our technique outperforms comparative techniques, including, color based detection, support-vector machine based (SVM) machine learning, and you only look once (YOLO), which is a state-of-the-art convolutional neural network (CNN) deep learning based object identification tool.

The directionality of optical signals provides an opportunity for efficient space reuse of optical links in visible light communication (VLC). Space reuse in VLC can enable multiple-access communication from multiple light emitting transmitters. Traditional VLC system design using photo-receptors requires at least one receiving photodetector element for each light emitter, thus constraining VLC to always require a light-emitter to light-receptor element pair. In this paper, we propose, design and evaluate a novel architecture for VLC that can enable multiple-access reception using a photoreceptor receiver that uses only a single photodiode. The novel design includes a liquid-crystal-display (LCD) based shutter system that can be automated to control and enable selective reception of light beams from multiple transmitters. We evaluate the feasibility of multiple access on a single photodiode from two light emitting diode (LED) transmitters and the performance of the communication link using bit-error-rate (BER) and packet-error-rate (PER) metrics. Our experiment and trace based evaluation through proof-of-concept implementation reveals the feasibility of multiple LED reception on a single photodiode. We further evaluate the system in controlled mobile settings to verify the adaptability of the receiver when the LED transmitter changes position.

Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication.

Theoretical models estimate visible light communication (VLC) data capacity to be of the order of Tera-bits-per-second (Tbps). However, practical limitations in receiver designs have limited state-of-the-art VLC prototypes to (multiple) orders of magnitude lower data rates. This paper explores a new architecture to realize ultra-high data rates in visible light communication systems by dramatically improving the Signal-to-Interference-Noise-Ratio (SINR) at the receiver. The key idea is to leverage the fast sampling rates of photodiode receivers and integrate a shutter mechanism that filters noise and interference thus creating a high-speed imaging receiver effect. Through adaptive selection of the exact receiver area over which the transmitted light is detected, the SINR can be dramatically increased yet not compromising the high sampling rate achievable using state-of-the-art photoreceptors. In addition to introducing the new hybrid architecture for high SINR reception, in this paper, we study the feasibility of noise and interference reduction through a proof-of-concept experimentation.

The directionality of optical signals provides an opportunity for efficient space reuse of optical links in visible light communication (VLC). Space reuse in VLC can enable multiple access communication from multiple light emitting transmitters. However, traditional VLC system design using photo-receptors requires at least one receiving photodetector element for each light emitter, thus constraining VLC to always require a light emitter-to-light receiving element pair. In this paper, we propose, design and evaluate a novel architecture for VLC that can enable multiple-access reception using a photoreceptor receiver that uses only a single photodiode. The novel design includes a liquid-crystal-display (LCD) based shutter system that can be automated to control and enable selective reception of light beams from multiple transmitters. We evaluate the feasibility of multiple access on a single photodiode from two light emitting diode (LED) transmitters and the performance of the communication link using bit-error-rate (BER) and packet-error-rate (PER) metrics. Our experiment and trace based evaluation reveals the feasibility of multiple LED reception on a single photodiode and estimated throughput of the order of Mbps.