Instrumentation & Measurement Magazine 23-2 - 30

Underwater Bioinspired Sensing:
New Opportunities to Improve
Environmental Monitoring
Jeffrey A. Tuhtan, Saptarshi Nag, and Maarja Kruusmaa

S

mart environmental monitoring networks are a
growing part of the Internet of Things (IoT). They are
useful to detect, forecast and assess human impacts
on the environment as well as the effects of climate change on
society. As climate uncertainty grows, we will increasingly rely
on these networks to address significant societal challenges
including the reduction of available drinking water, diminished agricultural productivity and growing threats posed
by extreme weather events on human health and safety. Bioinspired designs can lead to a new generation of devices to
ensure that environmental monitoring networks remain accurate and reliable over a wide range of physical conditions.
The Instrumentation and Measurement (IM) community is responsible for measuring, detecting, monitoring and recording
a vast range of physical phenomena. As such, IM researchers
should lead the development of new types of standardized
bioinspired sensors which can be integrated into the highly
valuable and urgently needed IoT-based environmental
monitoring networks of the future. As an example, we show
how fish-like underwater bioinspired sensing can improve
both the effectiveness and efficiency of monitoring upstream
migration.
The first environmental monitoring systems were the ancient rituals and stories of the pre-mechanical age from 3000
BC to 1450 AD, used to track the movement of animal herds
and changes in the length of the day [1]. During the mechanical
age, from 1450 to 1840, conflicts between societies related to the
overuse of forests, streams and grazing areas for livestock created the need for environmental monitoring and management.
The eletromechanical age, from 1840 to 1940, experienced the
emergence of nation-states, leading to governmental agencies tasked to protect natural resources and punish polluters.
The first technical environmental monitoring systems were
rapidly adopted in the 1960s by the water supply and wastewater sectors using supervisory control and data acquisition
(SCADA) technologies.

Today, in the electronic age (1940-present) we are experiencing the fourth industrial revolution. Environmental
monitoring systems now consist of sensors interconnected
through a mesh of networks with the capability to interact
and exchange data via the IoT. In addition to sensor-based
monitoring, these networks include separate layers for data
exchange, processing and reporting. IM researchers are leading the development of a new generation of devices and
methods to provide scalable and dependable environmental
monitoring technologies [2]. These technologies are now expanding far beyond their SCADA-based roots. Current trends
include the increasing use of remote sensing, autonomous robots and animal-borne biosensors to collect environmental
data. To address the challenges imposed by the wide variety of
data sources, Geographic information system (GIS) software
has been developed. GIS-based reporting allows researchers and managers to combine and assess environmental and
anthropogenic data together. The results are used for local,
regional and global resource assessment and management.
Future environmental monitoring systems are expected to
continue to expand, including new sources based on technologies with increasing compatibility with living organisms. A
graphical overview of the past, present and future of technical
environmental monitoring is illustrated in Fig. 1.
In the past, technical environmental monitoring consisted
of single sensors which uploaded data in a one-way exchange
from CPUs to a central data warehouse. Data were processed and then viewed using GIS software. The results were
mapped and evaluated, most commonly used in reports on disaster management, planning and forecasting. Due to rapid
advances in the IM and IT sectors, present technical environmental monitoring is IoT-based and dynamic, allowing for
real-time processing of more complex tasks including climate
resilience and adaptive management. With the help of machine learning, automated and personalized reporting is now
possible. The future of technical environmental monitoring

Research presented in this paper was financed by the base financing grant B53 "Octavo,"
Estonian Research Council PUT grant 1690 and Estonian Research Council IUT grant 339.
30	

IEEE Instrumentation & Measurement Magazine	
1094-6969/20/$25.00©2020IEEE

April 2020



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