Underwater acoustic telemetry has emerged as a powerful tool for practical applications, including resource exploration, environmental monitoring, and aquatic animal tracking. However, current acoustic telemetry systems lack the capability to transmit the collected data continuously in real time, primarily because the acoustic networking bandwidth is limited. Retrieval of the recorded measurements from the deployed receivers usually must be manual, leading to long delays in data retrieval and processing, high operational costs associated with the required manpower, and safety risks for the operators. In addition, there is no efficient way to continuously assess the status of the acoustic telemetry system, including the acoustic transmitters and receivers. Here, we describe the design, implementation, and field validation of a cloud-based, real-time, underwater acoustic telemetry system with edge computing for estimating fish behavior and monitoring environmental parameters. The system incorporates microcontrollers for edge computing and connects to a cloud-based service that further post-processes the transmitted data stream to derive behavior and survival information of tagged animals. The developed system has been demonstrated to have significantly improved performance over the benchmark system because of the integration of edge computing, with a greatly reduced energy consumption of 0.014 watts resulting in the energy used by the acoustic modem being reduced by over 300 times. This work opens up new design opportunities for future real-time and multifunctional underwater acoustic systems.

Approximately 16% of the world’s electricity and over 80% of the world’s renewable electricity is generated from hydropower resources, and there is potential for developing significantly more new hydropower capacity. In practice, however, optimizing the use of potential hydropower resources is limited by various factors, including environmental effects and related mitigation requirements. That is why hydropower regulatory requirements frequently call for targets to be met regarding fish injury and mortality rates. The sensor fish (SF) is a small autonomous sensor package that can be deployed through complex hydraulic structures, such as a turbine or spillway, to collect high resolution measurements that describe the forces and motions that live fish would encounter. The Hydropower Biological Evaluation Toolset (HBET), an integrated suite of science-based tools, is designed to use the SF (implemented) and other tools (to be implemented in the future) to characterize the hydraulic conditions of hydropower structures and provide quantitative estimates of fish injury and mortality rates resulting from exposure to various physical stressors including strike, pressure, and shear. HBET enables users to design new studies, analyze data, perform statistical analyses, and evaluate biological responses. It can be used by researchers, turbine designers, hydropower operators, and regulators to design and operate hydropower systems that minimize ecological impacts in a cost-effective manner. In this paper, we discuss the technical methodologies and algorithms implemented in HBET and describe a case study that illustrates its functionalities.

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

Water quality (WQ) monitoring systems are essential for maintaining aquatic ecosystems and safeguarding human health, but traditional systems are often limited by fixed sensor locations and reliance on manual sampling, which restricts spatial coverage and adaptability in diverse environments. To address these limitations, we developed a real-time, cloud-based WQ monitoring system using a modular remotely operated vehicle (ROV) equipped with integrated WQ sensors. The ROV’s modular design allows it to carry various sensors and additional devices, such as sonar and lasers, enabling flexible data collection tailored to specific monitoring needs. This system enhances traditional methods by enabling spatial and temporal measurements within 100 m of the ROV’s home position and eliminating the need for manual operation from a boat, making it suitable for a wide range of field conditions. The ROV can be deployed from a floating platform that collects and stores solar power, which is supplied to the ROV and the other onboard equipment. A tether management system automatically controls the length of the tether deployed, determining this based on the 3-D position of the ROV’s home location. The system was validated at three dams in North Carolina, Oregon, and Washington, successfully transmitting WQ data to a cloud service in real time using edge computing to increase transmission efficiency and reduce data transmission costs. This research presents a novel approach to WQ monitoring, offering greater flexibility and accessibility for applications across a wide range of aquatic environments.

Hydropower stands as the world's leading source of renewable electricity, with hydropower plants spanning the globe. A typical hydropower plant comprises essential components including the governor, excitation system, generator, thrust bearing, hydraulic turbine, transformer, main lead, metering and control devices, tailwater depression system, and dissolved oxygen monitoring. Each of these components undergoes various measures and monitoring procedures. These measurements are collected through a diverse array of systems, encompassing standalone sensors, programmable logic controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, Internet of Things (IoT) devices, and data acquisition and integration platforms like OSI/PI. The data obtained from these measurements is frequently stored within the plant's data management platform, and numerous institutions employ cloud-based archival systems for this purpose. Notable examples include the Hydropower Research Institution (HRI), the U.S. Army Corps of Engineers (USACE), and the Columbia River Data Access in Real Time (DART) system. Object Modeling serves as a comprehensive framework for designing information systems, with a primary focus on objects, their actions, and the messages exchanged to trigger these actions. It distinguishes itself from network modeling, data modeling, and process modeling in several key aspects. In Object Modeling, the primary emphasis lies in understanding the actions taken in response to information, the constituent objects shaping the system, the actions they execute, and the communication channels through which they exchange information. Conversely, network modeling delves into the specifics of where, when, and the volume of data transfer. Data modeling, on the other hand, hones in on the nature of the information itself and its destinations, while process modeling concerns itself with the mechanics of how and when data is transported. Object Modeling originated primarily as a tool for developing object-oriented systems and supporting object-oriented programming. It primarily characterizes the static structural elements within the system. The Object Modeling Technique is favored for its simplicity in both depiction and application, making it a logical choice for connecting physical hydropower plants to Digital Twin technology. It excels in recognizing objects and their interrelationships, identifying class attributes, and delineating their functions. Dynamic Modeling, within this framework, elucidates how objects respond to events, while Functional Modeling outlines the processes executed within objects and how data undergoes transformations during inter-object transfers. Object Modeling has found wide-ranging applications, spanning sectors such as telecommunications, transportation, and more.