Deploying open-architecture infrastructure for critical zone monitoring: lessons from the environmental sensing and data network

Abstract

Characterizing Critical Zone processes requires high-frequency, spatially distributed environmental data. Environmental wireless sensing networks (WSNs) can meet this need, but few researchers currently implement this infrastructure themselves, instead relying on legacy data loggers that require manual data retrieval or commercial telemetry systems that are too often hindered by financial and logistical constraints, e.g., sensor lock in, proprietary data models, recurring cellular fees, and lack of programmatic data access (e.g., APIs). Yet advances in the Internet of Things (IoT) have made the deployment of WSNs more accessible than ever before. Understanding the current state of WSN technologies and documenting deployment cases is therefore important for broadening the use of environmental WSNs to both advance Critical Zone science and expand access to monitoring through participatory sensing. We describe the Environmental Sensing and Data Network (ESDN), a multi-site infrastructure in eastern North Carolina that demonstrates a complete, off-the-shelf solution for distributed environmental monitoring. Combining LoRaWAN telemetry, a self-hosted ThingsBoard IoT backend, and cloud archiving, the ESDN integrates diverse legacy and modern sensors into a unified, interoperable data environment. Since its deployment in mid-2024, the network has expanded across contrasting urban, agricultural, and coastal wetland environments, growing to 110 deployed sensors and 14 communications gateways. These deployments successfully capture real-time processes across developed and natural landscapes, including surface-subsurface hydrologic coupling, saltwater intrusion dynamics, and urban stormwater responses. Cloud-based rule engines minimize power and computational loads on edge devices and enable real-time analytics, while the ESDN's open time-series APIs support data-driven workflows and artificial intelligence-ready applications. This paper both provides the current technology context for WSN deployment and an empirical demonstration as a blueprint for how open-source software and hardware tools can be used to deploy scalable, replicable, highly cost-effective, and analytically robust environmental sensing networks.