Underwater bioinspired sensing: New opportunities to improve environmental monitoring

被引:13
作者
Tuhtan, Jeffrey A. [1 ]
Nag, Saptarshi [2 ]
Kruusmaa, Maarja [2 ]
机构
[1] Tallinn Univ Technol, Ctr Biorobot, Environm Sensing & Intelligence Grp, Tallinn, Estonia
[2] Tallinn Univ Technol, Ctr Biorobot, Tallinn, Estonia
关键词
Environmental monitoring; Sensors; Bio-inspired computing; Water; Meteorology;
D O I
10.1109/MIM.2020.9062685
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Smart 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.
引用
收藏
页码:30 / 36
页数:7
相关论文
共 16 条
  • [1] [Anonymous], 2000, 12000 COOP RES CTR F
  • [2] Bleckmann Horst., 1994, Reception of hydrodynamic stimuli in aquatic and semiaquatic animals, volume 41 of Progress in Zoology
  • [3] Boutry Clementine M., 2015, 2015 IEEE Sensors. Proceedings, P1, DOI 10.1109/ICSENS.2015.7370669
  • [4] The Influence of Uncertainty Contributions on Deep Learning Architectures in Vision-Based Evaluation Systems
    Callari, Giuseppina
    Mencattini, Arianna
    Casti, Paola
    Comes, Maria Colomba
    Di Giuseppe, Davide
    Di Natale, Corrado
    Sammarco, Innocenzo
    Pietroiusti, Antonio
    Magrini, Andrea
    Lesci, Isidoro Giorgio
    Luce, Marco
    Cricenti, Antonio
    Martinelli, Eugenio
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2019, 68 (07) : 2425 - 2432
  • [5] Estimation of Flow Turbulence Metrics With a Lateral Line Probe and Regression
    Chen, Ke
    Tuhtan, Jeffrey A.
    Fuentes-Perez, Juan Fran
    Toming, Gert
    Musall, Mark
    Strokina, Nataliya
    Kamarianen, Joni-Kristian
    Kruusmaa, Maarja
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2017, 66 (04) : 651 - 660
  • [6] Fang S., IEEE T IND INFORM, V10, P1596
  • [7] Multisource classification using ICM and Dempster-Shafer theory
    Foucher, S
    Germain, M
    Boucher, JM
    Bénié, GB
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2002, 51 (02) : 277 - 281
  • [8] Green RB, 2019, 2019 INTERNATIONAL WORKSHOP ON ANTENNA TECHNOLOGY (IWAT): SMALL ANTENNAS AND NOVEL METAMATERIALS, P70, DOI [10.1109/iwat.2019.8730633, 10.1109/IWAT.2019.8730633]
  • [9] *IEEE, 1996, 1073 IEEE
  • [10] Kumar C. V., 2015, CHEM SCI, V6