Autonomous real-time water quality sensing as an alternative to conventional monitoring to improve the detection of food, energy, and water indicators

Advances in sensors and wireless sensor networks (WSNs) are enabling real-time environmental monitoring, which has the potential to provide a plethora of fine-grained data to assist in understanding the symbiosis between food, energy, and water (FEW) systems. This paper presents the advantages of autonomous real-time water quality monitoring systems over conventional systems and proposes cost-effective and feasible approaches to designing a system that autonomously collects environmental data by integrating digital and mechanical devices connected through various communication networks, both wired and wireless. More specifically, the autonomous sensing devices proposed include low-cost water quality sensors implemented on commercial hardware and cell-based biosensors using electric cell-substrate impedance sensing (ECIS), which are capable of detecting water and/or air toxicants in real time. The paper discusses the key design considerations of the underlying WSN communication system supporting autonomous data transmission, including the spatial distribution of sensors, costs, and operation autonomy. Communication among connected devices (e.g., sensors) requires both precision timing and network security against attacks, as well as means to ensure the privacy and integrity of the data being collected and transmitted through the network. Preliminary results demonstrate the importance of precision timing and synchronization by using measured timing information and signal strength to identify man-in-the-middle attacks in fixed wireless networks and to locate the attack source using machine-learning approaches. Data modeling and recovery methods are presented to efficiently analyze and process sensing data to address the missing measurement issue caused by noise and device failure. The system proposed herein can serve as a valuable tool for real-time monitoring of FEW resources and can be broadly applied to efficient management of their sustainability.