An IoT General-Purpose Sensor Board for Enabling Remote Aquatic Environmental Monitoring

Abstract

The ability to provide near real-time data (e.g., < every 15 minutes) on aquatic environmental conditions via remotely deployed sensors is a highly sought-after capability. However, cost and complexity are often significant factors that limit the spatial and temporal coverage of such monitoring systems. Most existing proposals are expensive, complex and/or are un-malleable for adaptation to other environmental sensing applications. This paper presents a simple and flexible open-source IoT (Internet of Things) electronics design for viable near real-time environmental measurements – specifically tailored to the rigors of aquatic settings. The system provides the minimal required functionality for reliable remote sensor readings with a focus on low energy consumption, renewal energy supply, plug and play deployments, and stability over time. The system development is driven by actual deployment logistics and constraints. We compare three revisions of the system/circuit and show how we aspired towards the aforementioned goals, whilst outlining the evolution of the design based on practical experience. A performance evaluation of the system is given in terms of functionality, stability, cost and energy consumption. The IoT platform is at the core of an affordable near real-time aquatic monitoring system that has been used in multiple water quality studies. The system has been adapted for use in other applications including water height monitoring and air dust sensing.

Introduction

Water covers approximately two thirds of the Earth's surface. However, only about 3% of this water is considered fresh and less than 1% is suitable for human use [1, 2]. The management of scarce water resources is critical for ensuring its sustainability. One of the primary means of water resource management is the ability to monitor various chemical and biological parameters that directly impacts water quality. The dynamic interplay of these parameters combined with natural and human inputs can dramatically influence water conditions [3, 4]. However, there are many impediments and logistical challenges for viably monitoring and assessing water quality [5, 6].

Vast geographical distances, difficulty of terrain and hostility on equipment make conducting any sort of recurrent water quality monitoring regime challenging. Most traditional approaches require water samples to be collected manually and analysed in a laboratory. Alternately, sensor equipment is placed in situ with logging devices, which are later retrieved for data download [7]. Either approach is time consuming, logistically expensive, and results in delayed data. As such, there is a need to be able to affordably collect water quality data in near real-time via remotely deployed sensors – especially in developing countries or for resource-constrained operations [8].

Remote aquatic environmental data collection (i.e., deployment, management and control of sensors remotely deployed in the field [9], [10], [11], [12]) reduces the amount of time personnel must spend in the field, thereby limiting the physical dangers and logistical costs [13]. Furthermore, the capacity to view the data in near real-time (i.e., every 5 - 15 minutes) allows decision makers to monitor an event as it unfolds (e.g., a harmful algal bloom, coral bleaching) and take appropriate counter measures. While such technology now exists to monitor aquatic/marine environments using remotely deployed networked sensors, expense is still the most significant factor that limits spatial and temporal coverage. Some examples of aquatic/marine monitoring initiatives include [14], [15], [16], [17], [18], [19], [20], [21], [22]. However, most of these initiatives have since concluded due to the excessive outlay or other reasons. Furthermore, these systems typically have complex designs with substantial energy requirements. They are also difficult to configure, deploy and maintain without a team of technical specialists.

The Cave Pearl Project [23] is a proposal for affordable aquatic environmental monitoring. This project uses off-the-shelf components (microcontrollers and sensors) to construct inexpensive underwater data loggers for caves. The system runs on three AA batteries and can log data for approximately one year. However, this system does not provide telemetry for remotely deployed sensor readings and therefore does not facilitate any of the aforementioned logistical benefits or timely interpretation of the data. Furthermore, the logger's power source is finite and non-renewable. Additionally, the system is designed for electronics enthusiasts and is not plug and play from the perspective of ease-of-use for someone with a non-technical background.

This paper presents a simple and flexible open-source IoT (Internet of Things) platform for affordable remote environmental sensing that extends upon and supersedes the premise of the Cave Pearl Project [23]. The design aspires towards minimal system complexity, low energy consumption, renewable power supply, plug and play operation and stability/reliability over time using commercial-grade sensors. The application is informed by practical requirements based on actual deployment logistics and constraints specific to aquatic environments. Three revisions of the platform are presented showing how the design evolved based on practical experience. A performance evaluation of the system is given in terms of functionality, stability, cost and energy consumption. The IoT platform has been developed in conjunction with a social enterprise [24], [25], [26] and used in multiple environmental studies involving numerous types of water bodies [27, 28]. The design of the circuit is flexible and adaptable allowing it to be used in other environmental monitoring applications such as flood level observations and air dust sensing.

This paper is structured as follows: Section 2 provides background and related work. Section 3 presents three designs for IoT aquatic environmental monitoring showing how each iteration builds on the previous based on evolving requirements. Section 4 provides a performance evaluation of the designs; and Section 5 provides concluding remarks and avenues for future work.