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.

Optical intelligent reflecting surfaces (OIRS) have the potential to enhance the capacity of the next generation optical wireless communication systems by re-configuring the propagation environment of optical waves. In this work, a submerged OIRS and a planar mirror surface (PMS) are used to build up a non-line of sight (NLOS) underwater wireless optical communication (UWOC) system. An NLOS UWOC setup, which makes use of the total internal reflection (TIR) phenomenon, serves as a benchmark against which the OIRS/PMS-assisted UWOC systems are compared. With the help of the Mellin inverse transform, Meijer’s G function, and Fox’s H-function, the closed-form expressions for the probability density function (PDF), cumulative distribution function (CDF), average spectral efficiency (SE), average energy efficiency (EE), outage probability, and average bit error rate (BER) of the proposed systems are derived. Further, an asymptotic analysis of average SE is performed to illustrate a better understanding of these proposed system’s performance at high SNR. The simulation results indicate that the OIRS-assisted UWOC system shows improved performance in comparison to benchmark scenario. Additionally, the efficient zones of OIRS deployment are analyzed.

In this paper, we present the design, implementation and evaluation of non-line-of-sight (NLOS) perception to achieve a virtual see-through functionality for road vehicles. In this system, a safety event, such as pedestrian crossing or traffic list status or vehicle merge, that are occluded to a driver due to another vehicle on the driving lane, are perceived and communicated by the occluding vehicle. Each vehicle is equipped with a camera, placed on the dashboard, that perceives the scene in a drivers view. This scene is analyzed and mapped into specific warning codes that are specific to a safety event, and communicated as short packets using visible light communication. The camera in the following vehicle captures this information and generates a recommendation for safety action to the driver by comparing the warning from the packet and from its own scene perception. Through experimental evaluations of a proof-of-concept implementation, we show that our system is able to achieve up to 90% accuracy in identifying nine occluded safety events, which correspond to traffic light statuses (red, green, yellow), other vehicles' lane change behaviors (merge/leave lane left/right), and pedestrian detection.

Underwater image enhancement and turbidity removal (dehazing) is a very challenging problem, not only due to the sheer variety of environments where it is applicable, but also due to the lack of high-resolution, labelled image data. In this paper, we present a novel, two-step deep learning approach for underwater image dehazing and colour correction. In iDehaze, we leverage computer graphics to physically model light propagation in underwater conditions. Specifically, we construct a three-dimensional, photorealistic simulation of underwater environments, and use them to gather a large supervised training dataset. We then train a deep convolutional neural network to remove the haze in these images, then train a second network to transform the colour space of the dehazed images onto a target domain. Experiments demonstrate that our two-step iDehaze method is substantially more effective at producing high-quality underwater images, achieving state-of-the-art performance on multiple datasets. Code, data and benchmarks will be open sourced.

The proliferation of Internet-of-Things (IoT) devices has opened up plethora of opportunities for smart networking and connected applications. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking presents an opportunity for such devices to collaborate. This paper proposes the idea of opportunistic collaboration among mobile IoT devices to share their services and excess computing resources. Opportunistic collaboration among devices over wireless networking requires proper coordination and agreements among the devices in a purely distributed manner. To facilitate the distributive collaboration, we propose a decentralized architecture design using blockchain technology. Through experimental evaluation of a prototype collaborative mobile-IoT system involving RaspberryPis and a Dell IoT edge gateway, we show that our proposed distributed collaborative approach is feasible and comparable to a non-collaborative edge- computing approach from a latency perspective.

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.