Instrumentation & Measurement Magazine 23-2 - 35

Fig. 5. A simple, two-step procedure for ensuring that bioinspired sensor performance is adequate for inclusion in
existing environmental monitoring systems, based on detailed analysis found in [16].

biofouling and limited communications. This has the unfortunate result that the costs of existing stationary underwater
sensors and animal-borne biologgers are already considerably
higher than their land-based counterparts. A new generation
of underwater bioinspired devices will need to make use of
new materials for robust sensing and communications. Additionally, a major challenge to the success of bioinspired sensing
is the significant and undervalued threat posed by the lack of
standardization. Above all, the standardization challenge hinders technological scalability. This is because integrating large
numbers of new and largely novel data streams can place a
significant burden on available communications bandwidth
and requires considerable signal processing. Recent advances
include biodegradable and biocompatible pressure sensors
[10] and implantable antennas [11] as promising bioinspired
alternatives to conventional devices but will require comprehensive testing before they will be accepted by industry as part
of IoT-enabled environmental monitoring systems. To address
the standardization challenge, the IEEE Standards Association
is undoubtedly the best-suited candidate to address this challenge due to their longstanding experience in developing and
implementing global technology standards. In addition, we
suggest that the IM community is well-posed to develop environmental monitoring communications standards, akin to
those for medical device communications and computerized
healthcare systems [12].
To partially address the standards challenge at the research
and development stage, we have adopted a simple two-step
procedure for the calibration and validation of new bioinspired sensors based on our own experiences developing the
artificial lateral line probe [8], presented in Fig. 5. The first step
involves the familiar procedures for laboratory calibration: experimentation under controlled conditions followed by data
pre-processing (e.g., outlier removal, band pass filtering) and
finally curve fitting and classification. Because bioinspired devices are often multisource, they are especially well-suited
for classification. Therefore, laboratory calibration activities
may also require traditional data fusion using Dempster-Shafer theory [13] as well as newer, machine learning approaches
April 2020	

such as deep learning [14].
The second step requires
the additional effort required to collect real-world
data for testing and validation of the fit curves
and classification results.
These results are then used
to conduct an uncertainty
analysis of the new bioinspired technology. This is
critical because implementing cost-effective solutions
for environmental monitoring in the underwater
environment requires especially reliable designs [15].

Conclusion
The IM community is responsible for measuring, detecting,
monitoring and recording a vast range of physical phenomena. In addition to these observational activities come the
responsibilities of calibration, uncertainty analysis and the
development of data processing tools and applications for environmental monitoring.
The main objectives of smart environmental monitoring
are to detect, forecast and assess the impacts of environmental
change on human society. As the uncertainty in our environment grows, we will increasingly rely on these networks to
address environmental challenges to societal well-being. We
propose that bioinspired underwater sensors can provide
new and effective means to explore, detect and monitor the
Earth's rivers, lakes, seas and oceans. To achieve this, the IM
community should lead the way in developing new types
of standardized sensors which can be integrated into rapidly developing IoT-based environmental monitoring sensor
networks.

References
[1]	 R. A. Rappaport, Pigs for the Ancestors: Ritual in the Ecology of a New
Guinea People, Second Edition. Long Grove, IL, USA: Waveland
Press, 2000.
[2]	 L. Lombardo, S. Corbellini, M. Parvis, A. Elsayed, E. Angelini,
and S. Grassini, "Wireless sensor network for distributed
environmental monitoring," IEEE Trans. Instrum. Meas., vol. 67,
no. 5, pp. 1214-1222, May 2018.
[3]	 S. Fang et al., "An integrated system for regional environmental
monitoring and management based on internet of things," IEEE
Trans. Industrial Informatics, vol. 10, no. 2, pp. 1596-1605, May
2014.
[4]	 D. Macii, A. Boni, M. D. Cecco, and D. Petri, "Tutorial 14:
multisensor data fusion," IEEE Instrum. Meas. Mag., vol. 11, no. 3,
pp. 24-33, Jun. 2008.
[5]	 Y. Yang, M. Zhong, H. Yao, F. Yu, X. Fu, and O. Postolache,
"Internet of things for smart ports: technologies and challenges,"
IEEE Instrum. Meas. Mag., vol. 21, no. 1, pp. 34-43, Feb. 2018.

IEEE Instrumentation & Measurement Magazine	35



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