This article presents an Internet-of-Drones (IoD)-enabled system for real-time, high-resolution, in-situ water quality sensing, sampling, 2-D mapping, and 3-D depth profiling from discrete sensing. Unlike isolated mooring platforms and stationary sensor buoys, our Uncrewed Aerial Vehicle (UAV) platform features a modular IoD architecture with wireless communication for adaptive sampling, parameter mapping, and real-time monitoring. The system integrates pH, temperature, turbidity, total dissolved solids (TDS), and depth sensors within a single-actuator multi-cartridge vessel that collects samples via TDS, depth, and ML-based triggers into four 50mL tubes. The proposed embedded system incorporates LoRa/LoRaWAN for low-power telemetry and LTE for wireless data transmission, with SD card logging, GPS geo-tagging, and Real-time Clock (RTC) synchronization. A high-resolution 3-D interpolation framework is implemented that reconstructs water quality fields from UAV-based missions over a 0.8m× 0.8m× 1mvolume and a comprehensive 2-D mapping over a 180m× 180marea. The system incorporates a smart sampling logic based on predefined thresholds (e.g., TDS and depth triggers), along with a machine–learning–assisted mode for adaptive chlorophyll-adetection and real-time decision-making. Comprehensive field tests at the Lake Erie digital test bed validate system performance.

Modern Function-as-a-Service (FaaS) cloud platforms offer great potential for supporting event-driven scientific workflows. Nonetheless, there remain barriers to adoption by the scientific community in domains such as environmental sciences, where R is the focal language used for the development of applications and where users are typically not well-versed with FaaS APIs. This paper describes the design and implementation of FaaSr, a novel middleware system that supports event-driven scientific workflows in R. A key novelty in FaaSr is the ability to deploy workflows across FaaS providers without the need for any managed servers for coordination. With FaaSr: 1) functions are written in R; 2) the runtime environments for their execution are customizable containers; 3) functions access data in cloud storage (S3) with a familiar file-based abstraction supporting both full file put/get primitives and subsetting using the Parquet format; and 4) function invocation and workflow coordination only requires S3 cloud object storage, without relying on any dedicated, active workflow engine server or cloud-specific queues/databases. The paper reports on the functionality and performance of FaaSr for micro-benchmarks and two case studies: event-driven forecast and batch job workflows. These demonstrate the ability to deploy workflows across multiple platforms (GitHub Actions, Amazon Web Services Lambda, and the open-source OpenWhisk), without the need for dedicated coordination servers, across both cloud and edge resources. FaaSr is open-source and available as a CRAN package.

The management of drinking water quality is critical to public health and can benefit from techniques and technologies that support near real-time forecasting of lake and reservoir conditions. The cyberinfrastructure (CI) needed to support forecasting has to overcome multiple challenges, which include: 1) deploying sensors at the reservoir requires the CI to extend to the network’s edge and accommodate devices with constrained network and power; 2) different lakes need different sensor modalities, deployments, and calibrations; hence, the CI needs to be flexible and customizable to accommodate various deployments; and 3) the CI requires to be accessible and usable to various stakeholders (water managers, reservoir operators, and researchers) without barriers to entry. This paper describes the CI underlying FLARE (Forecasting Lake And Reservoir Ecosystems), a novel system co-designed in an interdisciplinary manner between CI and domain scientists to address the above challenges. FLARE integrates R packages that implement the core numerical forecasting (including lake process modeling and data assimilation) with containers, overlay virtual networks, object storage, versioned storage, and event-driven Function-as-a-Service (FaaS) serverless execution. It is a flexible forecasting system that can be deployed in different modalities, including the Manual Mode suitable for end-users’ personal computers and the Workflow Mode ideal for cloud deployment. The paper reports on experimental data and lessons learned from the operational deployment of FLARE in a drinking water supply (Falling Creek Reservoir in Vinton, Virginia, USA). Experiments with a FLARE deployment quantify its edge-to-cloud virtual network performance and serverless execution in OpenWhisk deployments on both XSEDE-Jetstream and the IBM Cloud Functions FaaS system.